International Journal of Obesity最新文献

Background: X (formerly Twitter) is a unique social medium where many famous people and health institutions post and interact with casual users. We aimed to explore reactions to tweets about obesity and weight loss from accounts representing celebrities, politicians, sportsmen, and health authorities.

Methods: We collected tweets from n = 2444 X profiles representing seven categories: celebrities, politicians, sportsmen, medical specialists, medical journals, medical universities, and health institutions. We retrieved tweets from the accounts and selected tweets about, e.g., obesity, overweight, body mass index, and weight loss. We conducted sentiment analysis, descriptive statistics, and multivariable quantile regression modeling. In quantile regression models, each tau represents a decile from 0.1 to 0.9 of the dependent variable (number of likes or retweets). Therefore, a tau value of 0.5 represents the 5th decile, the 50th percentile, and the median of the dependent variable.

Results: The final dataset consisted of n = 8989 tweets. Achieving a large number of likes (taus 0.7, 0.8, and 0.9) was positively associated with posts written by celebrities, politicians, medical journals, and universities, while it was negatively associated with tweets authored by health institutions or medical specialists. In the case of a significant number of retweets, a positive association was observed for all account types, except for health institutions, for which the relationship was negative. These relationships were independent of verification status, the number of followers, tweet length, and sentiment.

Conclusions: Tweets concerning obesity and weight loss originating from accounts representing health institutions garnered fewer likes and retweets compared to other types of accounts, including non-medical ones. A limitation of the study is the relatively small number of tweets emanating from non-medical accounts. A X informational campaign about obesity should engage non-medical accounts with many followers to reach as many users as possible.

Backgrounds: The association between weight change in patients with diabetes, and the development of infective endocarditis (IE) has never been studied. Therefore, we evaluated the associations of weight changes in patients with diabetes with the development of IE.

Methods: In this Korean population-based cohort study, we included patients with diabetes aged ≥20 years who underwent health screenings twice in a 2-year interval between 2009 and 2012. Patients were categorized into five groups according to the degree of weight change between the two health screenings and were followed up until December 2018. A patient with a weight change of ≤-10% was designated to the severe weight loss group, -10 to ≤-5% to the moderate weight loss group, -5 to ≤5% to the stable weight group, 5 to ≤10% to the moderate weight gain group, and ≥10% to the severe weight gain group. The primary outcome was the incidence of IE.

Results: A total of 1,762,108 patients with diabetes were included. There were 67,580 (3.9%) individuals with severe weight loss, 247,969 (14.1%) with moderate weight loss, 1,267,849 (72.0%) with stable weight, 135,774 (7.7%) with moderate weight gain, 42,936 (2.4%) with severe weight gain. During the follow-up (median, 5.21 years), 828 cases of IE occurred. After adjusting for covariates, both weight loss (HR: 2.41, 95% CI: 1.87-3.12 for the severe weight loss group; HR: 1.28, 95% CI: 1.05-1.55 for the moderate weight loss group) and weight gain (HR: 1.17, 95% CI: 0.91-1.50 for the moderate weight gain group; HR: 1.59, 95% CI: 1.11-2.28 for the severe weight gain group) were associated with an increased risk of IE compared to those for the stable weight group.

Conclusion: Both weight gain and weight loss are associated with an increased incidence of IE, and the greater the degree of weight change, the greater the risk.

Objectives: While the association between obesity and kidney diseases has been found in previous studies, the relationship between preschool-age obesity and later-life kidney health remains unclear, posing challenges for effective interventions in this critical life period.

Methods: Utilizing the hitherto largest genome-wide association studies, we conducted two-sample mendelian randomization (MR) to estimate the association of preschool age-specific obesity on kidney health and diseases, including blood urea nitrogen (BUN), eGFRcrea, eGFRcys, chronic kidney disease (CKD), IgA nephropathy, and diabetic nephropathy. Then, we applied multivariable Mendelian randomization (MVMR) and stepwise MR to elucidate the role of adult obesity and 12 other potential factors in the pathway between preschool age-specific obesity and kidney health. Finally, we employed colocalization analysis to understand the mechanism of preschool age-specific obesity and kidney damage further by detecting shared causal variants.

Results: Our two-sample MR results indicated that preschool obesity could be associated with kidney health and disease. In addition, we observed a switch in the direction of associations between age-specific body mass index (BMI) and CKD, manifesting as negative associations before 3 years old and positive associations after 3 years old. Furthermore, MVMR and stepwise MR results suggested potential pathways linking preschool obesity to kidney health, involving factors such as adult BMI, circulating high-density lipoprotein cholesterol levels, and circulating C-reactive protein levels. Finally, we detected that preschool-age BMI and kidney function could share causal variants such as rs76111507, rs62107261, rs77165542 in the region of chromosome 2, and rs571312 in the region of chromosome 18.

Conclusion: Our study supports the association between preschool obesity and kidney health, emphasizing the role of adult BMI in this relationship. These findings underscore the importance of interventions starting in early childhood and continuing through adulthood to reduce the long-term risk of obesity-related kidney damage.

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.