Obesity Class Research Articles

Abstract 4124307: Outcome of Obesity in Atrial Fibrillation - Insights from The National Inpatient Sample Database

Background: Obesity is considered a significant risk factor for numerous cardiovascular conditions due to its effects on cardiac structure and function. The prevalence of atrial fibrillation (AF) is elevated among patients with obesity due to the dysregulation of several mechanisms. Weight loss has been shown to reverse cardiac remodeling, leading to a lower recurrence of AF despite the better prognosis in obese patients described as the obesity paradox. Methods: We utilized the National Inpatient Sample 2016-2019 to extract patients ≥18 years of age admitted with AF as the primary diagnosis based on ICD 10 codes. We performed univariate and multivariate regression analysis for known coronary risk factors. We divided patients based on their body mass index (BMI), and our primary outcomes were determining the odds of electrical cardioversion (ECV) and cardiac ablation (CA) due to AF. Results: The analysis included 1,625,809 weighted patients. Patients include underweight (6.66%), normal BMI (4.03), overweight (6.51%), obesity class I (20.65%), obesity class II (21.45%), and obesity class III (40.7). After multivariate regression analysis, patients with obesity class I, II, or III had higher odds of ECV, irrespectively of coronary risk factors (OR 1.3, 95% CI 1.25-1.37, OR 1.3, 95% CI 1.32-1.43, OR 1.3, 95% CI1.29-1.38, respectively, with statistically significant P values). However, underweight or normal BMI patients had fewer odds of ECV (OR 0.5 95%CI 0.49-0.61 and OR 0.6 95%CI 0.58-0.74, respectively, with P values <0.001). Meanwhile, there was no statistical significance between a BMI and the odds of CA. Conclusion: Our study highlights the significant impact of BMI on managing atrial fibrillation, particularly regarding electrical cardioversion (ECV). Patients in higher BMI categories (obesity class I, II, and III) demonstrated increased odds of undergoing ECV, suggesting that obesity may influence treatment approaches and outcomes in AF management. Interestingly, BMI did not affect the likelihood of cardiac ablation (CA), indicating a more complex relationship between body weight and AF treatment modalities that warrants further investigation. These findings underscore the importance of personalized treatment strategies to optimize outcomes, with a focus on weight management.

Higher BMI Is Associated With Wound Breakdown Following Resection of Haglund Deformity.

Haglund deformity is characterized by an enlarged posterosuperior calcaneus, often with inflammation of the retrocalcaneal bursa and Achilles tendon. This study aims to determine if obesity is associated with an increased rate of complications after Haglund resection. A retrospective review was conducted on patients who underwent surgical resection for Haglund deformity from January 2015 to December 2023 at a single academic center. The patient cohort was initially stratified by body mass index (BMI) into normal (BMI < 25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30). For those classified as obese, further subclassification was performed using the standard system: obesity class I (mild obesity) = BMI 30.0-34.9; obesity class II (moderate obesity) = BMI 35.0-39.9; and obesity class III (severe obesity) = BMI ≥40.0. Data on demographics, surgical techniques, and weightbearing were collected and analyzed. Postoperative complications were compared between groups. Of the 370 patients included in this study, 20 (5.4%) were classified as normal, 77 (20.8%) were overweight, and 273 (73.8%) were obese. Within the cohort of patients with obesity, 96 (35.2%) were classified as obesity class I, 96 (35.2%) as obesity class II, and 81 (29.7%) as obesity class III.The obese group had a higher proportion of females (70.0%) and Black/African American race (24.5%), and a higher prevalence of diabetes mellitus (22.0%) and American Society of Anesthesiologists scores compared with other groups. Additionally, analysis within the obesity subclassifications revealed significant differences in smoking status, with a higher proportion of nonsmokers as obesity class increased (58.3% in class I, 76.0% in class II, and 79.0% in class III; P = .01). Follow-up duration averaged 10.5 months, with wound breakdown rates significantly higher in the obese group vs the overweight or normal groups (11.0% vs 2.6% vs 0.0%, P = .02). No significant differences in wound complications or outcomes were observed between patients based on different obesity subclassifications. Our findings demonstrate that after Haglund resection, obese patients have a higher risk of complications, particularly wound breakdown. This underscores the necessity of careful patient selection and perioperative optimization.

Comparisons of 30-day outcomes after ventral hernia repair by body mass index and surgical approach: a retrospective cohort study.

Obesity is a known risk factor for postoperative complication after ventral or incisional hernia repair (VIHR). Whether minimally invasive techniques can mitigate this risk for certain patients remains unknown. This study investigates whether MIS approaches offer advantages in reducing any medical or surgical complication after VIHR across clinically meaningful BMI categories. This study analyzes data from the Michigan Surgical Quality Collaborative Core Optimization Hernia Registry (MSQC COHR). The registry is a representative, random sample of adult patients from 70 Hospitals across the state of Michigan. This study includes adult patients receiving VIHR between Jan 2020 and September 2023. All elective VIHRs captured in the MSQC database were included in analysis. Univariate statistics were used for cohort summary and multivariate logistic regression was used to estimate the probability of any 30-day postoperative complication while controlling for age, sex, the interaction of BMI and surgical approach, medical comorbidities, hernia size, mesh use, use of component separation, and previous hernia repair. A total of 11,886 people met the study inclusion criteria. The median (IQR) age was 55.0 (44-65) and 5,111(43.0%) were female. The median (IQR) BMI was 31.7 (27.6-36.3). 5,260(44.3%) cases were performed with an MIS approach and 6,626(55.7%) were performed with an open approach. The adjusted relative risk of experiencing a complication after open repair when compared to MIS repair was 1.34 (95% CI [1.04-1.70], p = .02) in Obesity Class I, 1.62 (95% CI [1.23-2.14], p < .001) in Obesity Class II, and 2.31 (95% CI[1.49-3.56]) for Obesity Class III. MIS repair was associated with improved 30-day outcomes for patients with class I, II, and III obesity, but not for healthy to overweight patients. In clinical scenarios where delay for severely obese patients may be difficult, electing to use an MIS repair may be the optimal strategy to reduce risk of complications.

H-NMR metabolomics identifies three distinct metabolic profiles differentially associated with cardiometabolic risk in patients with obesity in the Di@bet.es cohort

BackgroundObesity is a complex, diverse and multifactorial disease that has become a major public health concern in the last decades. The current classification systems relies on anthropometric measurements, such as BMI, that are unable to capture the physiopathological diversity of this disease. The aim of this study was to redefine the classification of obesity based on the different H-NMR metabolomics profiles found in individuals with obesity to better assess the risk of future development of cardiometabolic disease.Materials and methodsSerum samples of a subset of the Di@bet.es cohort consisting of 1387 individuals with obesity were analyzed by H-NMR. A K-means algorithm was deployed to define different H-NMR metabolomics-based clusters. Then, the association of these clusters with future development of cardiometabolic disease was evaluated using different univariate and multivariate statistical approaches. Moreover, machine learning-based models were built to predict the development of future cardiometabolic disease using BMI and waist-to-hip circumference ratio measures in combination with H-NMR metabolomics.ResultsThree clusters with no differences in BMI nor in waist-to-hip circumference ratio but with very different metabolomics profiles were obtained. The first cluster showed a metabolically healthy profile, whereas atherogenic dyslipidemia and hypercholesterolemia were predominant in the second and third clusters, respectively. Individuals within the cluster of atherogenic dyslipidemia were found to be at a higher risk of developing type 2 DM in a 8 years follow-up. On the other hand, individuals within the cluster of hypercholesterolemia showed a higher risk of suffering a cardiovascular event in the follow-up. The individuals with a metabolically healthy profile displayed a lower association with future cardiometabolic disease, even though some association with future development of type 2 DM was still observed. In addition, H-NMR metabolomics improved the prediction of future cardiometabolic disease in comparison with models relying on just anthropometric measures.ConclusionsThis study demonstrated the benefits of using precision techniques like H-NMR to better assess the risk of obesity-derived cardiometabolic disease.

Logistic Regression Model on Nutritional Status Based on Body Mass Index (BMI) in Women of Reproductive Age (WRA) in Indonesia

The aggregate analysis of Riskesdas data in Indonesia revealed that nearly all provinces face a serious double burden of malnutrition. Both undernutrition and overnutrition negatively affect the quality of human resources, especially when they occur in adolescent girls, Women of Reproductive Age (WRA), and pregnant and breastfeeding women, as this can lead to intergenerational nutritional problems. Body Mass Index (BMI) is a nutritional status measurement technique recommended by WHO. Nutritional problems occur when BMI is abnormal or not ideal, categorized as underweight (&lt;18.5 kg/m²), pre-obesity (25-29.9 kg/m²), obesity class I (30-34.9 kg/m²), obesity class II (35-39.9 kg/m²), and obesity class III (&gt;40 kg/m²). Data from laboratory results completed up to 2017, based on selected samples from the 2013 Basic Health Research (Riskesdas), were used to assess the nutritional status of the Indonesian population. This study used 9,418 respondent data samples from 33 provinces in Indonesia. Logistic regression analysis was applied because the response variable in this study is binary, representing ideal BMI (1) and non-ideal BMI (0). The independent variables used in this study include Hemoglobin level, Ferritin level, CRP level, age group, physical activity, living location, marital status, education, and risky food consumption patterns. All independent variables are categorical. The study found that the factors influencing nutritional status based on BMI in WRA are Hemoglobin level, Ferritin level, CRP level, age, and location. The resulting logit model is logit (π)=0.27695-0.1491 (Non-anemic Hemoglobin level)-0.1721 (Normal Ferritin level)+0.741 (Non-infectious CRP level)-0.2933 (Adult age group)-0.0976 (rural location). logit (π)=0.27695-0.1491 (Non-anemic Hemoglobin level)-0.1721 (Normal Ferritin level)+0.741 (Non-infectious CRP level)-0.2933 (Adult age group)-0.0976 (rural location). The largest odds ratio The largest odds ratio was produced by the CRP level variable, indicating that CRP level is the most influential factor affecting nutritional status based on BMI in WRA, where non infectious CRP compared to infectious CRP increases the likelihood of WRA having an ideal BMI.

Self-awareness Regarding Obesity and Specific Therapy in Patients with Overweight or Obesity and Cardiovascular Diseases from Moscow Region

Objective: Despite the proven effectiveness of lifestyle interventions and specific medications in the treatment of obesity, little is known about their use in real-world practice. . Aims: To study the awareness of patients about the problem of overweight/obesity, as well as the clinical practice of non-drug and drug therapy according to the survey Material and Methods: Eligible patients were recruited from the prospective outpatient registry of patients with cardiovascular diseases. The design of the study was a cross-sectional cohort singlecentre. All of the included patients completed a specifically designed questionnaire. The study included 295 patients (mean age 66.8 ± 11.8 years) with a body mass index (BMI) of ≥ 25 kg/m2. One hundred eight (36.6%) individuals were overweight, 124 (42.2%) had first-class obesity, 42 (14.2%) had second-class obesity, and 21 (7.1%) patients had third-class obesity. Results: 252 patients (85.4%) were informed of being overweight/obese, and all of them received non-drug recommendations for the treatment of obesity. Anti-obesity medications (AOM) were recommended only to 25 (8.5%) patients: 3 – overweight, 11 – obesity class I, 6 obesity class II, and 5 – obesity class III. Twenty-one (7.1%) patients took the prescribed medications (84% adherence). The drugs were more often taken by patients with class II and III disease (40.5% and 57.1% of patients, respectively). Overweight (8.5%) and class I obesity (18.0%) patients took these drugs less often (p &lt; 0.0001). Conclusion: The results of the survey have demonstrated good awareness of patients about their obesity/overweight and quite frequent use of non-drug obesity therapy. However, the rate of AOM prescription was extremely low.

Echocardiographic Assessment of Cardiac Remodeling According to Obesity Class

Evidence supports the existence of cardiac remodeling in obesity, but no standard diagnostic criteria has been proposed or validated.The objective of this study was to identify echocardiographic features of cardiac remodeling according to obesity class and assess the effect of non-surgical weight loss on cardiac structure and function.A total of 120 patients were divided according to their obesity class (Group 1: BMI 18.5-24.9; Group 2: 25-29.9; Group 3: 30-39.9; Group 4: >40) and underwent cross-sectional transthoracic echocardiography. Echocardiographic parameters of cardiac chamber quantification and function were compared between the 4 groups. Echocardiographic parameters were compared pre- and post-non-surgical weight loss in a subgroup of patients. Overall, there was an incremental increase in left ventricular (LV), left atrial (LA), and right ventricular dimensions, LV mass and LV stroke volume (all p<0.0001) across obesity classes. There was no significant difference in LV ejection fraction or right ventricular systolic function as assessed by tricuspid annular plane systolic excursion (TAPSE) but a significant decrease in global longitudinal strain (BMI 18.5-24.9: 22.8%±1.7%; BMI 25-29.9: 22.0%±1.4%; BMI 30-39.9: 20.8%±1.1%; BMI >40: 20.6%±1.3%, p<0.0001) and left atrial strain (BMI 18.5-24.9: 37.7%±2.3%; BMI 25-29.9: 32.8%±2.1%; BMI 30-39.9: 31.5%±1.8%; BMI >40: 29.0%±2.8%, p<0.0001). Allometric height-indexed LV and LA dimensions increased with increasing BMI class (p<0.0001). Echocardiographic parameters did not change significantly after non-surgical weight loss.In conclusion, echocardiographic features can be described according to obesity class. Allometric height indexation may better reflect cardiac remodeling in obesity in comparison to BSA indexation. Non-surgical weight loss was not associated with significant changes in cardiac chamber dimensions and function.

Early adverse outcomes remain challenging to prevent in morbidly obese patients undergoing total hip arthroplasty.

The prevalence of obesity is increasing substantially around the world. Elevated BMI increases the risk of complications following total hip arthroplasty (THA). We sought to evaluate trends in BMI and complication rates of obese patients undergoing primary THA over the last 30 years. Through our institutional total joint registry, we identified 15,455 primary THAs performed for osteoarthritis from 1990 to 2019. Patients were categorized according to the World Health Organization (WHO) obesity classification and groups were trended over time. Cox proportional hazards regression analysis controlling for confounders was used to investigate the association between year of surgery and two-year risk of any reoperation, any revision, dislocation, periprosthetic joint infection (PJI), venous thromboembolism (VTE), and periprosthetic fracture. Regression was stratified by three separate groups: non-obese; WHO Class I and Class II (BMI 30 to 39 kg/m2); and WHO Class III patients (BMI ≥ 40 kg/m2). There was a significant increase in the proportion of all obesity classes from 1990 to 2019, and the BMI values within each WHO class significantly increased over time. Risk of any reoperation did not change over time among non-obese or WHO Class I/II patients, but increased for WHO Class III patients (hazard ratio (HR) 1.04; p = 0.044). Risk of dislocation decreased over time for non-obese (HR 0.96; p < 0.001) and WHO Class I/II (HR 0.96; p = 0.002) patients, but did not change over time for WHO Class III (HR 0.94; p = 0.073) patients. Risks of any revision and PJI did not change over time for any group. The proportion of patients undergoing THA who are obese has increased dramatically at our institution between 1990 and 2019. Despite BMI values increasing within all WHO classes over time, two-year complication risks have remained stable or decreased in WHO Class I/II patients. However, continued efforts will be required to mitigate risks in the heaviest WHO Class III patients.

Impact of obesity class on sentinel lymph node mapping and postoperative complications after robotic surgery for endometrial cancer

Impact of obesity class on sentinel lymph node mapping and postoperative complications after robotic surgery for endometrial cancer

THE RELATIONSHIP OF BODY MASS INDEX (BMI) AND THE INCIDENT OF PRE-ECLAMPSIA IN PREGNANT WOMEN AT BHAYANGKARA HOSPITAL

Introduction: Pre-eclampsia is one of the causes of death in mothers and babies. One of the factors related to the occurrence of pre-eclampsia is obesity. Obesity can be identified using the Body Mass Index (BMI). This study aims to analyze the relationship between Body Mass Index (BMI) and the incidence of pre-eclampsia in pregnant women at Bhayangkara Hospital. Method: This type of research is observational and uses a cross-sectional research design. The total sample was 40 pregnant women determined using the total sampling technique. The research instrument used an observation sheet. Data were analyzed using the Spearman rank correlation test with a significance value &lt;α=0.05. Results: The study showed that most of the body mass index was in the overweight or obese category, amounting to 32 respondents (80%). The incidence of pre-eclampsia in almost all respondents with severe pre-eclampsia was 35 respondents (87.5%). The Spearman rank test results showed a P-value = 0.000 &lt; α 0.05, meaning there is a relationship between body mass index and the incidence of pre-eclampsia. Conclusion: There is a relationship between body mass index and the incidence of pre-eclampsia. The suggestion is the need for health workers to make innovations related to preventing pre-eclampsia.

Extent and impact of obesity in patients with diabetes and acute myocardial infarction - experience from a national cohort

Abstract Background Elevated body mass index (BMI) has an adverse effect on cardiovascular riskfactors where diabetes and coronary artery disease increases with obesity. However, less in known about the prevalence and the grades of obesity in a diabetes population with acute myocardial infarction (AMI) and the impact on prognosis. Further, for the moment there is a shortage of access to cardioprotective weight loss medications as Glucagon Like Peptide Receptor Agonists (GLP-1 RA). Purpose To explore features and grades of obesity in patients with diabetes and AMI and impact on outcome in a national perspective. Methods All patients with diabetes and AMI admitted for coronary angiography in Sweden the years 2010-2021 were included. Information on BMI was collected at the time of angiography and grouped into obesity categories: low BMI (&amp;lt;18.5 kg/m2), normal BMI (18.5-24.9 kg/m2), overweight (25.0-29.9 kg/m2), obesity grade 1 (30.0-34.9 kg/m2), obesity grade 2 (35.0-39.9 kg/m2) and obesity grade 3 (≥40.0 kg/m2). Information on baseline characteristics and outcome was collected from national registries. Mortality was followed until March 2022. Restricted cubic spline analysis of association between continuous BMI and age-adjusted mortality was performed where normal BMI served as a reference with Hazard Ratio (HR) 1.0. Results Of 19 378 patients with diabetes, 67% were men and 46% suffered a STEMI. Normal BMI was present in 21%, overweight in 41%, obesity grade 1 in 25%, obesity grade 2 in 9% and obesity grade 3 in 3% (Figure 1). Age was decreasing with grade of obesity (72 years in normal BMI vs. 62 years in obesity grade 3, p&amp;lt;0.001). Hypertension and hyperlipidaemia were more common in patients with more severe obesity than in those with normal BMI (67% vs. 80%, p&amp;lt;0.001, and 44% vs. 50%, p&amp;lt;0.001, in normal BMI and obesity grade 3 respectively) while a higher proportion of those with normal BMI had suffered a previous MI (10% vs. 6% in obesity grade 3, p&amp;lt;0.001) and a previous stroke (10% vs. 5% in obesity grade 3, p&amp;lt;0.001). In patients with normal BMI STEMI was more common (51% vs. 39%, p&amp;lt;0.001) than in those with obesity grade 3. In patients with obesity grade 3 GLP-1 RA was used in 14% compared to 4% in those with normal BMI. Figure 2 depicts age-adjusted HR and 95% Confidence Interval for mortality for different BMI levels with a U-shaped pattern illustrating that associated mortality risk is higher when obesity is present especially grade 2 and 3. Conclusion Obesity is common affecting around one third of patients with diabetes and AMI and impacts survival. This strengthens that after AMI use of effective weight loss cardioprotective treatment should be prioritised in patients with diabetes and obesity especially if severe.

The impact of body mass on cardiovascular outcomes in patients with acute coronary syndrome

Abstract Background Patients with acute coronary syndrome (ACS) and extreme body weights exhibit varying risks of bleeding and thrombotic outcomes. Furthermore, extreme body weights can influence the pharmacodynamic response to potent P2Y12 inhibitors (ticagrelor/prasugrel) and clopidogrel. Purpose This study aims to assess the impact of extreme body weights on cardiovascular outcomes, as well as the comparative efficacy and safety of potent P2Y12 inhibitors versus clopidogrel. Methods Data was extracted from the FORCE-ACS registry, a prospective, multicentre, registry, enrolling patients with ACS. Patients were stratified (in kg/m2) into the categories: underweight (body mass index [BMI] ≤20.0), normal weight (20.1–24.9), overweight (25.0–29.9), class 1 (30.0–34.9) and class 2 obesity (≥35.0). The primary thrombotic endpoint was major adverse cardiac events (MACE), a composite of cardiovascular death, myocardial infarction (MI), or stroke. The primary bleeding endpoint was defined as Bleeding Academic Research Consortium (BARC) 2, 3 or 5 bleeding. Cox proportional hazard models and Kaplan-Meier survival curves were used to estimate the risk of clinical events across the BMI categories. Restricted cubic splines were made to assess the continuous association of BMI with the risk of the primary endpoints, stratified for P2Y12-inhibitor therapy (potent vs. clopidogrel) and gender. Results The total included population consisted of 5,864 patients with ACS (mean age 65.2 years, 72.4% men), and were stratified into underweight (N=112 [2%]), normal weight (N=1,627 [28%]), overweight (N=2,697 [46%]), class 1 obesity (N=1,081 [18%]) and class 2 obesity (N=347 [6%]). Compared with normal weight (10.8%), the rate of primary bleeding outcome was higher in underweight (15.2%) patients, but lower in the overweight (8.9%), class 1 obesity (7.9%) and class 2 obesity (6.3%) categories. MACE occurred in 9.8% for the underweight group, 7.9% for normal weight, 7.0% for overweight, 8.0% for class, and 6.1% for class 2 obesity. After adjusting for confounding factors such as age, gender, cardiovascular risk factors, access site, and medical therapy, each unit increase in BMI was associated with a decreased bleeding risk (hazard ratio [HR] 0.97, 95% CI 0.95-0.99, p = 0.02), with no significant association found for MACE. Similar findings are demonstrated in the Kaplan Meier analysis (Figure 1A-1B). Subgroup analysis regarding bleeding risk and BMI did not show a significant interaction for potent P2Y12-inhibitor therapy versus clopidogrel and gender (P-value for interaction: 0.04 and 0.32, respectively)(Figure 2A-2D). Conclusions Our findings indicate a stronger correlation between BMI and bleeding risk compared to thrombotic risk, showing an increased risk for bleeding in patients with a low BMI. These results underscore the importance of careful monitoring and tailored interventions to mitigate bleeding risk in underweight patients.

Trend of body mass index and its impact on glycemic control in Finnish patients with type 2 diabetes

Abstract Background Obesity prevalence has increased in Finland and is prevalent in patients with type 2 diabetes (T2D). Understanding the trend in body mass index (BMI) and its impact on glycemic control helps in the assessment of treatment strategies for T2D. We aimed to investigate the current trend in BMI among Finnish patients with T2D and the glycated hemoglobin (HbA1c) control across different BMI categories. Methods Regional data on electronic health records (EHRs) covering all public health care in North Karelia, Finland, were used. Patients with T2D (ICD-10 code E11) in 2012-2022 were identified from EHRs. In each study year, patients with ≥1 measurement of BMI and HbA1c were included in data analysis. Generalized estimating equations regression analyses were performed to evaluate the trends. Results The annual number of patients in the analyses ranged from 5149 to 10216 in 2012-2022. The unadjusted mean BMI remained stable. However, an increasing trend in BMI was observed in the age-adjusted model and remained unchanged with further adjustment for sex, duration of diabetes, and antidiabetic medication use, reflecting the aging of the patient population and lower BMI in aged patients. Patients with higher BMI had higher HbA1c levels in 2012-2022. The increasing trends in HbA1c over time were observed across different BMI categories (except for the obsess class III) accounting for age, sex, and duration of diabetes. However, a decreasing trend in HbA1c was found in the obese class III category with further adjustment for antidiabetic medication use, along with the highest usage rates of the new antidiabetic medications during the follow-up. Conclusions Our findings suggest that weight management requires increased attention in Finnish patients with T2D and is also important for better glycemic control. Multimodal treatment, including effective lifestyle counseling, self-care support, and medication management, is needed. Key messages • More attention and resources are needed to achieve optimal weight control. • Weight management could aid in better glycemic control.

Impact of Obesity Severity on Hepatic Steatosis and Systemic Inflammatory Markers: A Comparative Analysis Across Obesity Classes

Introduction: Obesity has become a global health issue, with its prevalence steadily increasing. It is closely linked to several metabolic disorders, cardiovascular diseases, non-alcoholic fatty liver disease, and chronic low-grade inflammation and can progress to more severe liver conditions. This study evaluates the relationship between obesity and inflammatory markers in individuals with different obesity levels. Methods: A cross-sectional study was conducted among 50 patients categorized into three obesity classes based on body mass index (BMI). Blood samples were taken to evaluate inflammatory and metabolic markers, including white blood cell (WBC) count, neutrophil-to-lymphocyte ratio (NLR), and C-reactive protein (CRP). Results: There were no statistically significant differences in inflammatory markers such as WBC, NLR, or CRP; a trend toward higher CRP levels was observed in Class 3 obesity. Conclusion: In our study, no statistically significant association was observed between inflammatory markers and the degree of obesity. Although the sample size was relatively small, it is essential to acknowledge that obesity is a multifaceted condition, and genetic variations may play a role in these results.

Increased Body Mass Index is Independently Associated with Chronic Kidney Disease among People with Type 2 Diabetes

Background The alarming increase in the prevalence of obesity has implications for chronic kidney disease (CKD) progression in type 2 diabetes (T2D). This study aimed to assess if increased body mass index (BMI) can be an independent risk factor for CKD and T2D in the Indian context. Materials and Methods In this cross-sectional study, 602 (M:F = 378:224) participants were screened using Kidney Disease Improving Global Outcomes (KDIGO) from January to October 2023 in Chennai. Demographic, anthropometric, biochemical, clinical details, and comorbidities were recorded. T2D with CKD low risk was taken as control group, and CKD moderate and high risks were the study groups. BMI was classified based on the Asian criteria into normal (18.5–22.9), overweight (23–24.9), and obese (≥25 kg/m2). Results Majority of participants in moderate and high risk categories were obese compared to the low risk category (60.5% and 66.4% vs. 39.1%; p &lt; 0.001). A higher proportion of participants was on antihypertensive drugs in the high risk group and in the obese category (p &lt; 0.001). Comorbidities and diabetic complications were higher in the high risk group (p &lt; 0.001). Multivariate logistic regression revealed that age of ≥ 60 years [OR(95% CI); 6.3(2.2–18); p = 0.009]; increased BMI as overweight [3.6(2.1–6.3); p &lt; 0.001] and obese [5.2(3.3–8.3); p &lt; 0.001]; smoking [4.2(1.7–10.2); p = 0.002]; increased duration of diabetes of 5–15 years [2.3(1.2–4.5); p = 0.013], 16–25 years [4.8(2.2–10.4); p &lt; 0.001], and &gt;25 years [4.2(1.4–13); p = 0.011]; systolic blood pressure [1.01(1.0–1.03); p = 0.02]; and hemoglobin A1c [1.2(1.1–1.3); p &lt; 0.001] were independent risk factors for the progression of CKD. Conclusion Increased BMI was independently associated with CKD in T2D. Overweight and obese individuals are four to five times at risk for CKD progression. Early identification, lifestyle intervention, and weight-lowering drugs may reduce the complications of obesity in T2D and CKD.

Demographic, Social, Behavioral, and Clinical Characteristics Associated with Long-Term Opioid Therapy and Any Opioid Prescription in High-Risk VA Patients.

Social risks (individual social and economic conditions) have been implicated as playing a major role in the opioid epidemic and may be more prevalent in the most medically vulnerable patients. However, the extent to which specific social risks and other patient factors are associated with opioid use among high-risk patients has not been comprehensively assessed. To identify patient-reported and electronic health record (EHR)-derived demographic, social, behavioral/psychological, and clinical characteristics associated with opioid use in Veterans Affairs (VA) patients at high risk for hospitalization or death. We used generalized estimating equations to calculate the probability of long-term opioid therapy (LTOT) and the probability of filling any opioid prescription (regardless of duration) over five intervals during a 4-year period (12/2016-12/2020). Prospective cohort of 4121 medically high-risk VA patients not receiving palliative or end-of-life care, and who responded to a survey mailed to a nationally representative sample of 10,000 high-risk VA patients. Patient-reported demographic, social risk, behavioral/psychological, and clinical measures, and linked EHR-derived data. The average age was 69.8years, 6.7% were female, and 17.5% were Non-Hispanic Black race/ethnicity. The majority had diagnosed chronic pain (76.1%). LTOT and any opioid prescription were positively associated with the following: younger age, non-Hispanic White race/ethnicity (compared to non-Hispanic Black race/ethnicity), male sex assigned at birth (LTOT only), not being currently employed, current tobacco use, no alcohol use, higher grit (any opioid prescription only), functional limitations, diagnosed chronic pain, lower comorbidity burden (LTOT only), obesity class I or class II/III (any opioid prescription only), undergoing surgery (any opioid prescription only), and diagnosed cancer (any opioid prescription only). Multifactor screening could help identify individuals at elevated risk for adverse opioid-related outcomes and augment current multifaceted initiatives, as several social risks and patient characteristics were predictors of LTOT and any opioid prescription.

Preeclampsia Risk in Bali, Indonesia: The Role of Maternal Body Mass Index (BMI) and Gestational Weight Gain (GWG)

Background: Preeclampsia, a hypertensive disorder of pregnancy, significantly contributes to maternal and infant mortality and morbidity globally, including in Indonesia. Pre-pregnancy obesity and excessive gestational weight gain (GWG) are established risk factors for preeclampsia. This study investigated the association between pre-pregnancy body mass index (BMI) and GWG with the incidence of preeclampsia in Bali, Indonesia. Methods: A case-control study was conducted at a Maternal Hospital in Bali, Indonesia, from May 2023 to May 2024. The study included 429 pregnant women: 143 with preeclampsia (cases) and 286 without (controls). Data were collected from medical records. BMI was categorized using Asia-Pacific criteria, and GWG was assessed based on the 2009 Institute of Medicine (IOM) guidelines. Logistic regression analysis was performed to determine the associations, adjusting for maternal age, education, and occupation. Results: Obesity class II (BMI ≥ 30 kg/m²) and obesity class I (BMI 25.0-29.9 kg/m²) were significantly associated with an increased risk of preeclampsia, with adjusted odds ratios (AOR) of 2.90 (95% CI: 1.19-7.08) and 2.78 (95% CI: 1.60-4.81), respectively, compared to women with normal weight. Excessive GWG was also significantly associated with preeclampsia (AOR 2.53; 95% CI: 1.48-4.33) compared to adequate GWG. Conclusion: Maternal obesity and excessive GWG are significant risk factors for preeclampsia in Bali, Indonesia. These findings underscore the importance of pre-pregnancy counseling and interventions to manage weight and promote healthy GWG to reduce the burden of preeclampsia.

Metabolic obesity phenotypes and the risk of cancer: a prospective study of the Kailuan cohort.

Obesity is as an important risk factor for chronic diseases. Metabolically healthy obesity (MHO) is considered a benign state. The association between metabolic health and obesity categories and cancer risk remains unclear. This study aimed to investigate the relationship between metabolic health status combined with obesity phenotypes and the risk of cancer. Data from 91,834 participants in the Kailuan cohort were analyzed, excluding individuals with a body mass index (BMI) < 18.5 kg/m² and those with a history of cancer. Obesity phenotypes were classified based on BMI and waist circumference (WC) combined with metabolic health status, resulting in six phenotypes. Cox proportional hazard regression models were used to assess the association between metabolic health and obesity phenotypes with cancer risk and all-cause mortality. The prevalence of metabolically healthy obesity and metabolically unhealthy obesity defined by BMI was 6.86% and 12.18%, while that defined by WC was 20.79% and 25.76%, respectively. Compared to metabolically healthy participants, individuals with an unhealthy metabolic status had a significantly higher risk of cancer (HR, 1.09; 95% CI, 1.03-1.15; p=0.004). The hazard ratios for cancer were 1.19, 1.23, 1.20, and 1.55 for individuals with one, two, three, and four metabolic disorders, respectively. Among those classified as metabolically unhealthy, both overweight and obesity were associated with a protective effect on cancer risk (HR, 0.88; 95% CI, 0.80-0.96; p=0.006 for overweight; HR, 0.87; 95% CI, 0.78-0.97; p=0.010 for obesity). However, abdominal obesity significantly increased cancer risk in both metabolically healthy and unhealthy participants. In subgroup analysis, simple obesity showed a protective trend against cancer in those with respiratory cancers, while abdominal obesity consistently posed a risk for various cancer types. Metabolically unhealthy status and abdominal obesity are risk factors for cancer and all-cause mortality, whereas simple obesity offers protective effects against cancer and all-cause mortality in metabolically unhealthy individuals. These findings suggest that maintaining metabolic health and reducing the metabolic risks associated with abdominal obesity should be key targets for cancer prevention.

Complementary Role of BMI and EOSS in Predicting All-Cause and Cause-Specific Mortality in People with Overweight and Obesity.

To assess the complementary role of the Body Mass Index (BMI) and Edmonton Obesity Staging System (EOSS) in predicting all-cause and cause-specific mortality in people living with overweight and obesity (PLwOW/O). A longitudinal analysis of prospectively collected data from the 1999-2018 cycles of the National Health and Nutrition Examination Survey (NHANES) was conducted. The association between BMI, EOSS, and mortality was evaluated through Cox regression models, adjusted for confounders. The analysis included 36,529 subjects; 5329 deaths occurred over a median follow-up of 9.1 years (range: 0-20.8). An increased mortality risk was observed for obesity class II and III (HR = 1.21, 95% CI 1.08-1.36, p = 0.001 and HR = 1.58, 95% CI 1.39-1.80, p < 0.001; compared to overweight), and for EOSS stage 2 and 3 (HR = 1.36, 95% CI 1.16-1.58, p < 0.001 and HR = 2.66, 95% CI 2.26-3.14, p < 0.001; compared to stage 0/1). The prognostic role of BMI was more pronounced in younger patients, males, and non-Black individuals, while that of EOSS was stronger in women. Both BMI and EOSS independently predicted cardiovascular- and diabetes-related mortality. EOSS stage 3 was the only predictor of death from malignancy or renal causes. BMI and EOSS independently predict all-cause and cause-specific mortality in PLwOW/O. Their integrated use seems advisable to best define the obesity-related mortality risk.

Exploring the Influence of BMI on Gait Metrics: A Comprehensive Analysis of Spatiotemporal Parameters and Stability Indicators

Background: Gait analysis is a vital tool for evaluating overall health and predicting outcomes such as mortality and cognitive decline. This study explores how normal and obese BMI categories impact gait dynamics, addressing gaps in understanding the effect of body composition on specific gait parameters. Research Question: The primary objective is to investigate differences in spatiotemporal gait parameters—specifically, gait speed, step length, cadence, and double support time—between normal and obese BMI groups to understand the effects of obesity on gait. Methods: This observational case-control study analyzed spatiotemporal gait metrics from 163 participants, using inertial measurement units (IMUs) to collect data on various gait parameters. Statistical analyses explored the relationship between BMI categories and these metrics. Results: No significant differences were found in gait speed, cadence, stride duration, or double support time between the normal and obese groups. However, significant differences were identified in age, hypertension prevalence, balance problems, and the incidence of falls, emphasizing the complex effects of obesity on factors influencing gait stability. Significance: This study contributes to our understanding of obesity’s impact on gait by highlighting the need to consider associated health and stability parameters. These findings prompt a re-evaluation of how BMI is integrated into clinical gait assessments and emphasize the necessity for personalized healthcare strategies. This research highlights the importance of future studies with larger, more diverse populations and a wider array of biomechanical measures to dissect the relationship between BMI, body composition, and gait dynamics.