Rates Of Obesity In Population Research Articles

Associations between wastewater gut microbiome and community obesity rates: Potential microbial biomarkers for surveillance

Gut microbes are crucial for human health, which are usually accumulated in urban wastewater systems. Seven wastewater treatment plants in Australia with distinct population obesity rates between 18% and 33% were selected for wastewater sampling and analysis. Human gut microbiome were detected using metagenomic sequencing to investigate their associations with the community obesity rate. To unravel this complex relationship, a range of algorithm models, including linear discriminant analysis effect size (LEfSe), similarity percentage analysis (SIMPER), statistical analysis of metagenomic profiles (STAMP), linear models for microarray and RNA-Seq data analysis (LIMMA), Relief, ratio approach for identifying differential abundance (RAIDA), least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), Boruta, DESeq2 and analysis of compositions of microbiomes with bias correction (ANCOM-BC), were used to identify potential bacterial biomarkers for obesity in the wastewater microbiome. Among these algorithm models, LEfSe, LIMMA, SIMPER and SVM are effective in identifying multiple microbial biomarkers. Specific human gut microbes, including Ruminococcus_E, Agathobacter, Fusicatenibacter, Anaerobutyricum, Blautia_A and Neisseria, were identified as potential consensus microbial biomarkers for obesity in the population. A high obesity rate is mainly characterized by a high abundance of pathogenic bacteria and microorganisms associated with xenobiotic biodegradation and metabolism, endocrine and metabolic diseases, and transcription pathways. This study underscores the innovative potential of leveraging human gut microbes in wastewater as biomarkers for monitoring obesity levels across communities, offering a novel, cost-effective, and indirect approach to public health surveillance.

Differential Impact of Body Mass Index in Hip Arthroscopy: Obesity Does Not Impact Outcomes

Background: Hip arthroscopy is commonly used for the treatment of hip pathologies. As population obesity rates continue to increase, elucidating the impact of body mass index (BMI) on hip arthroscopy outcomes is essential. This investigation was conducted to quantify the effects of BMI on hip arthroscopy outcomes. Methods: We conducted a retrospective medical records review of 459 patients undergoing hip arthroscopy at a single center from 2008 to 2016. The Harris Hip Score (HHS) and 2 component scores of the 12-Item Short Form Survey-the physical component score (PCS-12) and the mental component score (MCS-12)-were used to measure outcomes. Patients were stratified into 4 cohorts based on their BMI: underweight (BMI <18.5 kg/m2), normal weight (BMI 18.5 to 24.9 kg/m2), overweight (BMI 25.0 to 29.9 kg/m2), and obese (BMI ≥30.0 kg/m2). Results: At 1 and 2 years postoperatively, all cohorts experienced statistically significant improvements in the HHS and PCS-12. At 3 years postoperatively, statistically significant improvements were seen in the HHS for all cohorts; in the PCS-12 for the normal weight, overweight, and obese cohorts; and in the MCS-12 for the normal weight cohort. Intercohort differences were not statistically significant at 1, 2, or 3 years postoperatively. Conclusion: In our population, BMI did not have statistically significant effects on patient outcome scores following hip arthroscopy. All patient cohorts showed postoperative improvements, and differences between BMI cohorts were not statistically significant at any postoperative time point.

SHARED GENETIC AETIOLOGY OF TYPE 2 DIABETES AND OSTEOARTHRITIS.

Purpose: Osteoarthritis (OA) and type 2 diabetes (T2D) are two of the most prevalent chronic health disorders worldwide. Both diseases are affected by genetic and environmental factors. Beyond their common risk factors, such as obesity and increasing age, it has been shown that both diseases are epidemiologically positively correlated, often co-existing in older adults. For instance, individuals with OA have a 40% higher risk of becoming diabetic compared to non-osteoarthritic patients. Taking into consideration that the world’s population obesity rates and average age are rising, this comorbidity pair can be considered an increasing global health challenge.

Evaluating the Effectiveness of a Clinical Practice Intervention in Increasing Obesity Data Recording at a Western Australian Country Health Service Hospital: A Quasi-Experimental Controlled Trial.

PurposeIdentification and mitigation of obesity-related risks to staff and healthcare organisations can occur using patient obesity data; however, a 2017/18 audit of obesity data accuracy was assessed to be poor. This study investigates the results of an intervention to improve obesity data recording and coding accuracy at an Australian hospital.BackgroundIncreasing population obesity rates result in increased organisational and financial risks to hospitals. Australian obesity prevalence has steadily increased since 1995, and 42% of the Australian population is predicted to be obese in 2035. To reduce risks to healthcare staff who care for obese patients, complete and accurate obesity recording is required.MethodsFollowing a previous audit of obesity recording and coding accuracy of patients admitted to hospital with Type II diabetes, a 12-month intervention was undertaken, comprising staff education, introduction of tape measures and obesity decision-making tools, recording of patient volunteered height, regular reinforcement of obesity recording requirements and enhanced clinical coding of obesity. A re-audit was subsequently conducted to determine if the intervention impacted obesity recording and coding at the previously audited site.ResultsImproved recording of obesity-related measures and obesity data accuracy were observed, including increased patient BMI, impacted by increased patient height measurements and increased patient weight measurements. Obesity recording accuracy increased due to the intervention, including increased sensitivity, increased negative predictive values and reduced false negatives.ConclusionThe obesity recording intervention was successful; however, as hospitals increasingly use electronic health records, improvement opportunities should be considered such as compulsory recording of patient weight and height, embedded BMI calculators and “check boxes” for recording impacts of obesity conditions on treatment. Immediate improvement of obesity recording in manual patient files can be achieved in the meantime by implementing targets of 100% weight, height and BMI recording, introducing education programs and auditing compliance.

An audit of obesity data and concordance with diagnostic coding for patients admitted to Western Australian Country Health Service hospitals

Objective: Accurate patient obesity data can be used to identify and mitigate patient manual handling risks to healthcare staff. This study investigates the accuracy of patient obesity data within the Western Australian Country Health Service (WACHS) and examines factors potentially affecting obesity data accuracy. Background: Risk of injuries to healthcare staff are increasing due to rising patient obesity. Consistent increases in the prevalence of obesity in Australia have been recorded since 1995 and Australian obesity projections predict that 42% of the population will be obese in 2035. To manage the increased risks of injuries to healthcare workers due to obese patient management, accurate healthcare data relating to patient obesity is required. Design: Researchers examined records of patients admitted to WACHS hospitals with Type II Diabetes, which has confirmed links with obesity. Manual data extraction and comparison of obesity related data within patient medical records and electronic patient admission data was conducted to determine accuracy. Results: Analysis of the patient data examination demonstrated poor recording of weight (67%), height (24%) and Body Mass Index (BMI) when weight and height measurements were recorded (10%). Poor obesity data accuracy was also determined by low sensitivity results (40%), high false negative results (60%) and a Cohen’s kappa value of 0.44. Discussion: The sensitivity result demonstrates that only 40% of obese patients were coded as obese when obesity is recorded in their medical files, and the false negative result demonstrates that where obesity notations were present in medical files, 60% of these cases were incorrectly coded as ‘normal weighted’. There was only moderate agreement between the occurrences of coded obesity and the recorded obese patient notations in the medical files. Conclusion: Further research is required to inform enhancements to improve obesity recording and coding accuracy, which will increase the collection of reliable obesity data that could be used to reduce obese patient handling risks to nurses and other healthcare staff. What is already known about the topic? Increasing Australian population obesity rates have been previously demonstrated, this increase corresponds with increasing numbers of obese patients being admitted into hospitals. Healthcare staff who care for obese patients are at increased risk of injuries when conducting patient handling tasks. What this paper adds: A model to measure obesity accuracy utilising 14 data accuracy indicators was used, revealing poor obesity data accuracy and poor completeness of obesity data. Completeness of obesity data is influenced by time demands and workload of clinicians, breadth of clinical recording requirements, lack of organisational direction for the need of obesity data, and challenges in obtaining height measurements of patients who are mobility impaired, bed-ridden or unable to stand. Complete and accurate obesity data collections will result in increased ability to mitigate safety risks to healthcare staff who manage obese patients and may improve healthcare funding accuracy.

Diet Quality and Food Prices Modify Associations between Genetic Susceptibility to Obesity and Adiposity Outcomes.

The role of the retail food environment in obesity risk is unclear, which may be due in part to the lack of consideration of individual differences in the responsivity to food cues. This cross-sectional investigation geo-temporally linked the CARTaGENE biobank (including genetic, dietary, lifestyle, and anthropometric data) with in-store retail food environment data to examine interactions between a polygenic risk score (PRS) for obesity and (1) diet quality (n = 6807) and (2) in-store retail food measures (n = 3718). The outcomes included adiposity-related measures and diet quality assessed using the 2010 Canadian-adapted Healthy Eating Index. A vegetable:soft drink ratio was constructed for each retail measure to assess the relative healthfulness of exposures. Generalized linear models adjusted for individual and neighborhood socio-demographic factors were used to evaluate main and interactive effects. Diet quality significantly modified the association between polygenic risk of obesity and body mass index, waist circumference, and body fat percent. A significant interaction was also observed between PRS and regular price of vegetables in relation to soft drinks on waist circumference. These results replicate previous reports of diet moderating polygenic risk of obesity and suggest that prices of low vs. high-energy density foods are an intervention target to address population obesity rates.

ARE OBESITY AND ADENOMA DEVELOPMENT ASSOCIATED AS COLORECTAL CANCER PRECURSORS?

ABSTRACT Background:One of the most important concerns on health is the increased rates of obesity in population and the speed in which this number is increasing. This number translates a serious public health problem, since it also increases the risk of several other diseases associated with obesity resulting in significant morbidity and mortality. Among them, it seems to be connected to several neoplasms, such as colorectal carcinoma. Aim:To evaluate the impact of obesity as a risk factor for colorectal carcinoma through the detection of adenoma, and to discuss the mechanisms that could establish a link between obesity and neoplasm. Methods:Patients who underwent colonoscopy were included. Personal and anthropometric data, clinical history, and results of the tests were analyzed in order to verify the correlation of BMI and the presence of adenomatous polyps. Results:A total of 142 patients were studied, which a mean age of 62 years. Of the patients, 74 (52.1%) were men and 68 (47.9%) were. Obesity was identified in 16.2% of the patients. Polyps were found in 61 (42.9%), mostly smaller than 1 cm. Obese individuals were 1.56 times more likely to present colorectal adenoma than patients with normal weight. Conclusion:This study, although showing the greater presence of colorectal adenomas in obese individuals, did not show a significant difference in the occurrence of pre-malignant lesions.

Demographics of Women with Type 1 Diabetes Attending a Diabetes and Pregnancy Clinic: 2000–2002 vs. 2014–2016

Background: The numbers of women attending diabetes and pregnancy clinics (DPCs) are increasing. Some of the increase may reflect population obesity rates on gestational diabetes prevalence, but the number and characteristics of women with type 1 diabetes (T1DM) are unknown. The DPC in London, Ontario, sees all pregnant women with diabetes within the catchment area. Longitudinal characteristics of women with T1DM attending the DPC were documented for demographic insights.

Demographics of Women with Type 2 Diabetes Attending a Diabetes and Pregnancy Clinic: 2000–2002 vs. 2014–2016

Background: The numbers of women attending diabetes and pregnancy clinics (DPCs) are increasing. Some of the increase may reflect population obesity rates on GDM prevalence, but the number and characteristics of women with type 2 diabetes (T2DM) are unknown. The DPC in London, Ontario, sees all pregnant women with diabetes within the catchment area. Longitudinal characteristics of women with T2DM attending the DPC were documented for demographic insights.

Temporal trends, center-level variation, and the impact of prevalent state obesity rates on acceptance of obese living kidney donors.

The impact of pre-donation obesity on long-term outcomes of living kidney donors remains controversial. Published guidelines offer varying recommendations regarding BMI (kg/m2 ) thresholds for donor acceptance. We examined temporal and center-level variation in BMI of accepted donors across US transplant centers. Using national transplant registry data, we performed multivariate hierarchical logistic regression modeling using pairwise comparisons (overweight, BMI: 25-29.9; mildly obese, BMI: 30-34.9; very obese, BMI: ≥35; versus normal BMI: 18.5-24.9). Metrics of heterogeneity, including median odds ratio (MOR), were calculated. Among 90013 living kidney donors, 2001-2016, proportions who were very obese decreased and proportions who were mildly obese or overweight increased. Significant center-level heterogeneity was noted in BMI of accepted donors; the MOR varied from 1.10 for overweight to 1.93 for very obese donors. At centers located in the 10 states with the highest general population obesity rates, adjusted odds of very obese donor status were 185% higher (reference: normal BMI) than in states with the lowest obesity rates. Although there is a declining trend in acceptance of very obese living kidney donors, variation across centers is significant. Furthermore, local population obesity rates may affect the decision to accept obese individuals as donors.

Public and Opinion Leader Willingness to Fund Obesity-Focused Policies in Kansas.

Obesity increases the risk for leading causes of death, including cardiovascular disease and some cancers. Midwestern and southern states have the highest obesity rates-in Kansas, one in every three adults is obese. We compared the willingness of Kansas adults and opinion leaders to pay more in taxes to fund obesity prevention policies. In 2014, we asked a representative sample of 2,203 Kansas adults (response rate 15.7%) and 912 opinion leaders (response rate 55%) drawn from elected office and other sectors, including business and health, whether they would pay an additional $50 in annual taxes to support five policies that improve access to healthy foods and opportunities for physical activity. We used adjusted Wald tests to compare public and opinion leaders' responses, and regression analysis to assess whether differences in respondents' gender, age, location (urban/rural), race/ethnicity, and political stance affected results. Adjusting for demographic differences, Kansas adults were more willing than opinion leaders to pay $50 in taxes for each of the five policy interventions. This study demonstrates a willingness among residents of a fiscally conservative state to pay increased taxes for policies that could reduce population obesity rates. Health professionals, including nurses, can use these findings to educate policy makers in Kansas and geopolitically similar states about widespread public support for obesity prevention policies. Public health and other nurses could also apply our methods to assess support for obesity prevention policies in their jurisdictions.