International Journal of Obesity最新文献

Introduction: High body mass index (BMI) in adolescence is a strong predictor of adult obesity. However, the nature of this association is unclear. We investigated how adolescent BMI is associated with adult weight change using longitudinal data from ages 11.5 to 37 years and examined the genetic factors behind these associations.

Data and methods: The study cohort consisted of 1400 Finnish twin individuals (40% males) with 494 complete twin pairs who reported their body mass index (BMI) at five ages: 11.5, 14, 17.5, 24, and 37 years. BMI trajectories (defined as BMI changes (i.e., slope) and BMI at baseline age (i.e., intercept)) were calculated in adulthood (from 17.5 to 37 years of age) using linear mixed-effects models. Polygenic Risk Scores of BMI (PRS_BMI) and genetic twin models were utilised to analyse the role of genetic factors underlying BMI trajectories and their associations with BMI at 11.5 and 14 years of age.

Results: Mean BMI increased in adulthood (4.06 kg/m² in men and 3.39 kg/m² in women). The BMI changes correlated with BMI at the baseline age of 17.5 years (i.e. intercept) (r = 0.24 in men and r = 0.35 in women) as well as with BMI in adolescence (11.5 and 14 years of age). Genetic factors contributed to the BMI changes during adulthood (correlation with PRS_BMI r = 0.25 in men and r = 0.27 in women; heritability estimates 0.63 and 0.64 respectively) as well as to their correlations with BMI at the baseline age (r_A = 0.5 in men and 0.54 in women) and BMI during adolescence (at 11.5 and 14 years of age) (r_A = 0.63-0.64).

Conclusion: We found that genetic factors play a role in BMI change in adulthood, and part of this genetic component overlaps with the genetics of BMI in adolescence. Genetic predisposition to high BMI in adolescence is also related to adult weight gain.

Background: This study aims to investigate the differential miRNA expression profile between the visceral white adipose tissue and the skeletal muscle of people with obesity undergoing bariatric surgery.

Methods: Skeletal muscle and visceral adipose tissue samples of 10 controls and 38 people with obesity (50% also with type 2 diabetes) undergoing bariatric surgery were collected. miRNA expression profiles were analyzed using Next-Generation Sequencing and subsequently validated using RT-PCR.

Results: Approximately 69% of miRNAs showed similar expression in both tissues, however, 55 miRNAs were preferentially expressed in visceral adipose tissue and 53 in skeletal muscle. miR-122b-5p was uniquely identified in skeletal muscle, while miR-1-3p and miR-206 were upregulated in skeletal muscle. Conversely, miR-224-5p and miR-335-3p exhibited upregulation in visceral adipose tissue. Notably, distinctions related to the presence of type 2 diabetes were observed solely in the expression of miR-1-3p and miR-206 in visceral adipose tissue.

Conclusions: This is the first study unveiling distinct miRNA expression profiles in paired samples of visceral adipose tissue and skeletal muscle in humans. The identification of obesity-specific miRNAs in these tissues opens up promising avenues for research into potential biomarkers for obesity diagnosis and treatment.

Background: Short sleep duration and poor sleep quality have been associated with obesity. Asian populations report shorter sleep duration compared to other groups. We therefore aimed to explore the relationships between sleep duration, sleep quality, dozing, daytime napping, snoring, insomnia and adiposity in a multi-ethnic Asian population, and investigate the potential contribution of disturbed sleep to the risk of obesity amongst Asian populations.

Methods: We studied 8876 participants of the HELIOS study, a multi-ethnic population-based cohort comprising Chinese, Malay, and Indian Asian men and women living in Singapore. Sleep traits and psychological symptoms were assessed using validated tools which included the Pittsburg Sleep Quality Index, Generalised Anxiety Disorder-7, and Patient Health Questionnaire-9. We employed multivariable regression models to examine the associations between sleep and adiposity, while also conducting sub-group and sensitivity analyses to strengthen the reliability of our results.

Results: The 8876 participants were 69.3% Chinese, 12.5% Malays, and 18.2% Indians, with mean age: 51.7 ± 11.8 years (standard deviation). Malays had the shortest sleep duration, while Chinese had the best sleep quality. Short sleep duration, poor sleep quality, and snoring were associated with higher BMI and waist circumference, independent of age, sex, ethnicity, and various confounding factors (education, household income, current smoking, regular alcohol drinking status, presence of diabetes and hypertension, and markers for anxiety and depression; P < 0.005). The estimated population attributable fraction for short sleep and snoring as contributors to obesity were 6.6% (95% CI: 2.5-10.6%) and 18.6% (95% CI: 17.0-20.2%), respectively.

Conclusion: Sleep duration, sleep quality, and snoring are associated with adiposity in a multi-ethnic Asian population of Chinese, Malays, and Indians. Our findings suggest that a substantial portion of obesity in Asian populations could be averted through public health interventions aimed at improving sleep duration and quality.

Objective: A systematic review and meta-analysis were performed following PRISMA 2020 guidelines to identify the difference in gait pattern between adults with obesity and adults with a normal bodyweight assessed with 3D-4D gait analysis (3D-4DGA) devices.

Methods: Articles about the spatiotemporal parameters of adults with obesity compared with adults with a normal bodyweight using a 3DGA were sought on the 4th of October 2023 in three different databases (PubMed, Web of Science and IEEE). A total of 3371 articles were found: 2065 with PubMed, 1185 with Web of Science, and 121 with IEEE. The data was screened double-blind. Fourteen case control studies were included in the systematic review and meta-analysis, and for all of them, the risk of bias was determined. Obesity was defined using the BMI, with a range of 30 kg/m² until 54.06 kg/m². Participants from both sexes (35% men and 65% women) were included, and they had an age range of 18-65 years.

Results and discussion: The risk of bias was assessed with the Newcastle Ottowa Scale (NOS), and the certainty of evidence was assessed with the Evidence-Based Richtlijn Ontwikkeling (EBRO). The meta-analysis showed a decrease in gait speed and cadence and an increase in stance phase, double stance, and step width. No significant difference was found regarding stride length. In the systematic review, step length, step rate, and swing phase were found to have decreased. Regarding the single stance, step time, CoM, and CoP, no conclusions could be drawn.

Conclusion: There is a difference in gait parameters between adults with obesity and adults with a normal bodyweight. Namely, the gait speed, step rate, step length, swing phase is decreased in adults with obesity. However, there is an increase in step width, stance phase, and double stance phase.

Background: General obesity commonly represented by body mass index (BMI) is an established risk factor for colorectal cancer (CRC). However, it is unclear to what extent this association is accounted for by central obesity. We aimed to evaluate the associations between BMI, waist-to-hip ratio (WHR), and waist circumference (WC) with CRC risk and to investigate if and to what extent these associations are independent from each other.

Methods: Data from more than 500,000 male and female participants aged 40-69, recruited in the UK Biobank study between 2006 and 2010, were analyzed. Multivariable Cox proportional hazards models were fitted and hazard ratios (HR) and their 95% confidence intervals (CI) were calculated.

Results: During a median follow-up of 12.5 years, of 460,784 participants, 5,977 developed CRC. Multivariable adjusted HRs (95% CIs) per standard deviation increase of BMI, WHR, and WC were 1.10 (1.07-1.13), 1.18 (1.14-1.22), and 1.14 (1.11-1.18), respectively. After mutual adjustment, the association with CRC was substantially attenuated for BMI (1.04 (1.01-1.07)), and remained substantially stronger for WHR (1.15 (1.11-1.20)). Furthermore, WHR showed strong, statistically significant associations with CRC risk within all BMI categories, whereas associations of BMI with CRC risk were weak and not statistically significant within WHR categories. BMI was also not associated with CRC risk in women and with rectal cancer after mutual adjustment. Conversely, WHR was strongly associated with CRC risk in both sexes and with both colon and rectal cancer risk before and after adjustment for BMI. BMI and WC could not be mutually adjusted for due to their high correlation.

Conclusion: Central obesity is a much stronger predictor of CRC and may account for most of the CRC risk linked to obesity. Our findings also emphasize the need for incorporating measures such as WHR alongside BMI in clinical practice to improve obesity prevention and management.

Long wait times, limited resources, and a lack of local options mean that many people with severe obesity cannot access treatment. Face-to-face group-based interventions have been found effective and can treat multiple people simultaneously, but are limited by service capacity. Digital group interventions could reduce wait times, but research on their effectiveness is limited. This systematic review aimed to examine the literature about online group-based interventions for adults with severe obesity (BMI ≥ 35 kg/m²). The review followed the PRISMA and PICOS frameworks. MEDLINE, Embase, CINAHL, Web of Science, and Cochrane Central Register of Controlled Trials were searched. Two authors independently screened articles. Data extraction, analysis, and quality assessment (using RoB2 and MMAT) was shared between two authors. A meta-analysis was conducted on eligible studies; other results were descriptively analysed. 20 papers reporting on 15 studies were included. Most studies reported some evidence of weight loss, but evidence of weight-related behaviour change was mixed. A meta-analysis on four studies indicated that online, group-based interventions had a statistically significant impact on weight loss (p = 0.001; 95% CI -0.69 to -0.17) with a small-to-moderate effect size, compared to waitlist or standard care conditions. Online interventions were considered more convenient but lack of familiarity with the group or counsellor, accessibility issues, and time constraints hindered engagement. Technical support, incentives, and interactive forums to improve group cohesion could mitigate these barriers. The findings suggested that online, group-based interventions are feasible and potentially beneficial, but barriers such as internet accessibility, digital literacy, and unfamiliarity with group members need to be mitigated. Key recommendations to improve experience and impact include providing instructions and run-throughs, building group cohesion, and providing session and additional content throughout the intervention. Future studies should focus on the influence of specific intervention characteristics and investigate the effect of these interventions compared to face-to-face interventions. Registration: National Institute for Health Research, PROSPERO CRD42021227101; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021227101 .

Background/objectives: A high level of visceral fat area (VFA) is associated with obesity and cardiometabolic risk factors. VFA measured by computer tomography (CT) scan is accurate but has limitations for everyday use. Meanwhile, near-infrared (NIR) light penetrates the superficial layers of the human body so that fat content can be measured just as CT imaging measures fat accumulation.

Subjects/methods: This study evaluated whether discrete multi-wavelength NIR spectroscopy (DMW-NIRS) can be used to measure abdominal fat as a satisfactory alternative to a CT scan. 290 subjects were enrolled in this study, and each subject underwent DMW-NIRS NIR measurement and CT scan. A sex-specific DMW-NIRS-based VFA estimation formula was developed by multiple linear regression, including lipid density, age, and body mass index (BMI).

Results: The model of DMW-NIRS estimated VFA gave the least Akaike Information Criterions (AIC), Root Mean Squared Errors (RMSE), and the greatest Coefficient of determination (R²) to predict VFA (1199, 29.5, 0.544 in female, and 1714, 41.3, 0.504 in male, respectively). Also, the DMW-NIRS estimated that VFA was highly performed to determine visceral obesity, which is comparable with other obesity surrogates.

Conclusions: This study suggested that lipid density can be used as a valid, noninvasive method to determine visceral obesity.

Background: Body mass index (BMI) trajectories have been used to assess the growth of children with respect to their peers, and to anticipate future obesity and disease risk. While retrospective BMI trajectories have been actively studied, models to prospectively predict continuous BMI trajectories have not been investigated.

Materials and methods: Using longitudinal BMI measurements between birth and age 10 y from a mother-offspring cohort, we leveraged a multi-task Gaussian process approach to develop and evaluate a unified framework for modeling, clustering, and prospective prediction of BMI trajectories. We compared its sensitivity to missing values in the longitudinal follow-up of children, compared its prediction performance to cubic B-spline and multilevel Jenss-Bayley models, and used prospectively predicted BMI trajectories to assess the probability of future BMIs crossing the clinical cutoffs for obesity.

Results: MagmaClust identified 5 distinct patterns of BMI trajectories between 0 to 10 y. The method outperformed both cubic B-spline and multilevel Jenss-Bayley models in the accuracy of retrospective BMI trajectories while being more robust to missing data (up to 90%). It was also better at prospectively forecasting BMI trajectories of children for periods ranging from 2 to 8 years into the future, using historic BMI data. Given BMI data between birth and age 2 years, prediction of overweight/obesity status at age 10 years, as computed from MagmaClust's predictions exhibited high specificity (0.94), negative predictive value (0.89), and accuracy (0.86). The accuracy, sensitivity, and positive predictive value of predictions increased as BMI data from additional time points were utilized for prediction.

Conclusion: MagmaClust provides a unified, probabilistic, non-parametric framework to model, cluster, and prospectively predict childhood BMI trajectories and overweight/obesity risk. The proposed method offers a convenient tool for clinicians to monitor BMI growth in children, allowing them to prospectively identify children with high predicted overweight/obesity risk and implement timely interventions.

Background/objectives: This study investigated the hitherto unclear association of body mass index (BMI) with age at overall, disability, and disability-free survival in older adults with and without frailty.

Methods: This prospective cohort study enroled 10232 Japanese adults aged ≥65 years, who underwent follow-up for adverse events, from the Kyoto-Kameoka Study conducted from 2011-2016. BMI, calculated based on self-reported height and body weight, was classified into five categories: <18.5, 18.5-21.4, 21.5-24.9, 25.0-27.4, and ≥27.5 kg/m². Frailty was assessed using the validated Kihon Checklist. The relationships between BMI and disability and mortality were analysed using multivariate Cox proportional hazards models and Laplace regression.

Results: During the 5.3-year median follow-up period (45472 person-years), 2348 (22.9%) incidences of disabilities occurred. After adjusting for confounders, including medical history and lifestyle, individuals in the lowest and highest BMI categories had a higher hazard ratio (HR) of disability [<18.5 kg/m²: HR: 1.31, confidence interval (CI): 1.16-1.49; ≥27.5 kg/m²: HR: 1.27, 95% CI: 1.08-1.49, p for non-linearity <0.001] compared with that of those with BMI = 21.5-24.9 kg/m². In the 50th percentile differences in age at overall and disability-free survival, participants with BMI < 18.5 kg/m² were more likely to die before disability incidence [survival with disability (overall survival - disability-free survival): -10.2 months]; those with BMI ≥ 27.5 kg/m² had longer survival with disability (12.5 months). These relationships were more marked in the frailty-stratified model, where in the BMI ≥ 27.5 kg/m² group, individuals with frailty survived longer with disability (27.2 months) than did individuals without frailty (6.2 months).

Conclusion: Higher BMI is associated with a longer duration of survival with disability among older adults, especially in those with frailty. Therefore, reversing frailty should be prioritised because individuals with frailty have a shorter probability of disability-free survival than do individuals without frailty, regardless of BMI.

Background: Sixty minutes is currently the shortest testing duration for 24-h resting energy expenditure (24-h REE) utilizing whole-room indirect calorimetry.

Objective: Show that recalculated 30-min extrapolated 24-h REE from previously published 60-min metabolic data are valid.

Methods: Propane consumption linearity was determined through an 8-h combustion test. Thereafter, metabolic data for 24-h extrapolated ventilation rates of oxygen (VO₂; l/d), carbon dioxide (VCO₂; l/d), respiratory quotient (RQ; VCO₂/VO₂), and REE (MJ/d) from ten 60-min propane combustion tests were recalculated to reflect a 30-min testing duration. A similar analysis was performed utilizing data from 60-min subject metabolic measurements within a whole-room indirect calorimeter (4597 liters) specific for measuring resting energy expenditure (REE). Statistical (p < 0.05) comparisons between recalculated and original 60-min metabolic data were determined by SPSS (version 29).

Results: Propane consumption during a combustion test was found to be linear for up to 8-h. Furthermore, no differences existed between propane stoichiometry and combustion for any of the extrapolated 24-h metabolic parameters when recalculated from 60-min propane combustion data to reflect a 30-min duration. Finally, similar results were obtained for all recalculated subject metabolic data.

Conclusions: Recalculated extrapolated 24-h metabolic data derived from a 30-min testing duration appear to be valid. This suggests that whole-room indirect calorimetry could be an adjunct for various weight loss or other programs where accurate metabolic measurements are required.