Obesity Indices Research Articles

Association between cardiometabolic index (CMI) and periodontitis in US adults: analysis of NHANES data (2009-2014).

The cardiometabolic index (CMI) is a new predictor of obesity-related diseases, but its link to periodontitis is under-researched. This study aims to examine the potential association between the CMI and periodontitis. We conducted a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES) database (2009-2014) to examine the potential association between CMI and periodontitis. The study utilized a weighted multivariate logistic model to assess the association between TG, HDL-C, TG/HDL-C, obesity indices (BMI, WC, WHtR, CMI), and periodontitis, employing the area under the receiver operating characteristic curves (ROC) to estimate areas under the curve (AUC). Furthermore, generalized smooth curve fitting was conducted to examine the relationship between CMI and periodontitis. Finally, the study incorporated subgroup analysis and interaction tests to examine consistency across different populations. TG/HDL-C, WHtR, and CMI were positively associated with periodontitis in the fully adjusted classification model. It was observed that for each unit increase in CMI, there was a corresponding 17.8% increase in the prevalence of periodontitis [1.178 (1.049, 1.322) 0.00562] and 18.7% increase in the prevalence of moderate/severe [1.187 (1.057, 1.334) 0.00376] in the fully adjusted model. When CMI was used as a categorical variable, the adjusted OR for periodontitis prevalence increased significantly with increasing CMI after adjusting for all potential covariates (T3 vs. T1: OR, 1.28 [1.06, 1.55], p < 0.05). The ROC analysis indicated that a larger area under the curve was found in the CMI [0.554 (0.538-0.570)] than in the WC [0.544 (0.528-0.560)] and WHtR [0.544 (0.528-0.561)]. Nonetheless, the discrepancy observed did not reach statistical significance (all p > 0.05). Further generalized smooth curve fitting and threshold effect models indicated a positive linear correlation between CMI and periodontitis. Moreover, there is no interactive association between TG/HDL-C, WHtR, CMI, and periodontitis. This cross-sectional study revealed a positive relationship between CMI and periodontitis. Further prospective studies are warranted to validate our findings.

Prevalence of Subclinical Hypothyroidism in a Non-Diabetic Young Female Population and Its Impact on Diabetes and Cardiometabolic Risk.

We evaluated the influence of subclinical hypothyroidism (SCH) on insulin resistance (IR), cardiometabolic risk, and obesity in childbearing-age women without diabetes. This cross-sectional investigation included 282 women, aged 18 to 35 years, from rural and suburban Sri Lanka. Anthropometric and biochemical parameters, including IR and lipid/thyroid profiles, were recorded. Data were compared between SCH and euthyroidism (EU) for controls (normal weight) and cases (overweight/obese). The overall rates of SCH, EU, IR, and metabolic syndrome (MetS) were 40.42%, 59.57%, 73.40%, and 24.46%, respectively. Both controls and cases included individuals with SCH; overall, 168 participants (59.57%) had EU, while 114 (40.42%) exhibited SCH. IR was significantly associated with SCH in both weight groups (P<0.05). Among those with SCH, the odds ratios (ORs) for IR were >2 (95% confidence interval [CI], 0.45 to 3.87) in controls and >6 (95% CI, 3.52 to 8.41) in cases. Similarly, the ORs for MetS were >1 (95% CI, 0.38 to 4.16) in controls and >11 (95% CI, 8.73 to 15.01) in cases. Dyslipidemia and hypertriglyceridemia were significantly more prevalent in the SCH group (P<0.05). Women with SCH exhibited higher mean values for all obesity indices compared to their EU counterparts, surpassing normal thresholds (P<0.05). Among obesity measures, visceral adiposity index (VAI) demonstrated the highest area under the curve and sensitivity for assessing SCH and cardiovascular disease (CVD) risk. SCH must be identified and managed in young women to help prevent diabetes and cardiometabolic disorders. VAI may aid in precisely detecting SCH and CVD.

Novel Adiposity Indices Are Associated With Poor Prognosis in Heart Failure With Preserved Ejection Fraction Without the Obesity Paradox.

There is limited study that illuminates the relationship between obesity indices and prognosis in patients with heart failure with preserved ejection fraction, nor has it been examined whether the obesity paradox persists when using these metrics. This study is a post hoc analysis of data from the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) trial. A total of 3114 individuals were included in our final analysis, and a total of 481 (15.4%) all-cause deaths, and 389 (12.5%) heart failure hospitalizations were recorded. In a multivariable Cox regression model, compared with patients with a body mass index (BMI) <24.9 kg/m2, those with a BMI of 25.0-29.9, 30.0-34.9, and 35-39.9 kg/m2 were associated with a decreased risk of all-cause death, with hazard ratio (95% CI) of 0.59 (0.45-0.78), 0.61 (0.46-0.82), and 0.66 (0.47-0.92), respectively. Conversely, patients with a BMI ≥40 kg/m2 showed an increased risk of heart failure hospitalization, compared with BMI <24.9 kg/m2. Furthermore, patients in the highest quintile of obesity indices exhibited a significantly elevated hazard ratio for both all-cause death and heart failure hospitalization, compared with the lowest quintile. An elevated BMI over a certain range was associated with a reduced risk of all-cause death in heart failure with preserved ejection fraction, displaying a U-shaped relationship, with no mortality reduction observed in cases of extreme obesity. In contrast, higher values of novel obesity indices were positively correlated with all-cause death and heart failure hospitalization without the obesity paradox.

Effect of abnormal distribution of abdominal adiposity and skeletal muscle on the outcomes of endovascular treatment for emergent large vessel occlusion

BackgroundThe aim of this study was to investigate the relationships between imaging indicators of obesity, as measured by computed tomography (CT), and clinical outcomes at 90 days and 1 year...

Relationship between physical activity and abdominal obesity and metabolic markers in postmenopausal women

This study aimed to investigate the impact of physical activity indicators monitored by the POLAR accelerometer on body obesity indicators, metabolic syndrome parameters, and energy metabolism hormones in postmenopausal women. Was included 71 participants from this study program (68.8 ± 4.3 years). We divided participants into LPA and MVPA groups based on their level of moderate to vigorous physical activity per week; Physical activity levels over 7 days were assessed using a POLAR accelerometer, with daily step counts and sedentary time recorded. Measurements included waist circumference, visceral fat volume, body fat percentage, blood pressure, fasting blood glucose, triglyceride levels, HDL cholesterol, and energy metabolism hormone levels (leptin, resistin, adiponectin). The MVPA group displayed lower waist circumference, body fat percentage, abdominal fat, and BMI compared to the LPA group (p < 0.05). A significant negative correlation was observed between daily step count and obesity indicators, including waist circumference (r = -0.301), body fat percentage (r = -0.295), abdominal fat (r = -0.318), and BMI (r = -0.238). Conversely, sedentary time showed a positive correlation with obesity indicators such as waist circumference (r = 0.258), body fat percentage (r = 0.239), and abdominal fat (r = 0.244). Moreover, daily step count exhibited a significant negative correlation with leptin levels (r = -0.245), while sedentary time was positively correlated with the energy metabolic factor leptin (r = 0.279). Waist circumference demonstrated significant positive correlations with triglycerides, blood glucose, adiponectin, resistin, and leptin levels. Postmenopausal women who engage in at least 150 min of MVPA weekly show lower obesity indices. There is a significant correlation between physical activity levels and obesity indicators, which relate to metabolic syndrome and energy metabolism factors. Thus, increased physical activity may help prevent metabolic syndrome and cardiovascular diseases in this population.

Association of body indices with mortality in older population: Japan Specific Health Checkups (J-SHC) Study.

Obesity indices reflect not only fat mass but also muscle mass and nutritional status in older people. Therefore, they may not accurately reflect prognosis. This study aimed to investigate associations between a body shape index (ABSI), body mass index (BMI), and mortality in the general older population. This nationwide observational longitudinal study included individuals aged between 65 and 74 years who underwent annual health checkups between 2008 and 2014. Exposures of interest were ABSI and BMI, and the primary outcome was all-cause mortality. Association between the ABSI and BMI quartile (Q1-4) and mortality was assessed using Cox regression analysis. A restricted cubic spline was also used to investigate nonlinear associations. The missing values were imputed using multiple imputation by chained equations. Among 315,215 participants, 5074 died during a median follow-up period of 42.5 (interquartile range: 26.2-59.3) months. Compared with ABSI Q1, ABSI Q3 and Q4 were associated with increased risk of mortality, with the adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) of 1.13 (1.05-1.22) and 1.23 (1.13-1.35), respectively. Compared with BMI Q3, BMI Q1 and Q2 were associated with an increased risk of mortality, with aHRs and 95% CIs of 1.51 (1.39-1.65) and 1.12 (1.03-1.22), respectively. The impacts of these indices were greater in male than in female. The heatmap of the aHR for mortality by continuous ABSI and BMI showed that higher ABSI was consistently associated with higher mortality risk regardless of BMI, and that the combination of low BMI and high ABSI was strongly associated with increased mortality risk. High ABSI and low BMI are additively associated with the risk of all-cause mortality in the general older population in Japan. Combination of ABSI and BMI is useful for evaluating mortality risk in older people.

The association between triglyceride-glucose index combined with obesity indicators and stroke risk: A longitudinal study based on CHARLS data

Background and objectiveThe triglyceride-glucose (TyG) index, a surrogate marker for insulin resistance, has been proposed as a predictor of cardiovascular events. However, the combined impact of the TyG index and obesity indicators, such as body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR), on stroke risk was not fully understood. This study aimed to investigate the association between the TyG index combined with these obesity indicators and stroke risk in the Chinese population.Materials and methodsData on 17,708 participants aged 45 years or older for this study were collected from the China Health and Retirement Longitudinal Study (CHARLS) from baseline (2011) to the Wave 5 follow-up (2020). Cox proportional hazards model, restricted cubic spline (RCS) and receiver operating characteristic (ROC) analysis were employed to examine the association between the TyG index and its combined obesity-related indicators with stroke. Mediation analysis was conducted to explore the mutual potential mediating role of TyG and obesity indicators in the above relationships.ResultsA total of 8,207 participants with an average age of 58.2 years were investigated, of which 11.0% were stroke individuals, 44.80% were men and 84.6% were from rural areas. TyG, TyG-BMI, TyG-WHtR, TyG-WC were significantly higher in stroke subjects than in the non-stroke subjects (P < 0.001), and were significantly and positively associated with stroke in all 3 models (P < 0.05). Restricted cubic spline models revealed nonlinear associations between TyG and TyG-BMI with stroke (P-overall < 0.001, P-nonlinear = 0.003 for TyG, and P-overall < 0.001, P-nonlinear = 0.028 for TyG-BMI), while TyG-WC and TyG-WHtR (P-overall < 0.001 and P-nonlinear > 0.05) demonstrated linear associations with stroke after adjusting for covariates. TyG-WHtR, TyG-BMI and TyG-WC had more robust predictive power than TyG for risk of stroke. TyG-WHtR or TyG-WC had the highest predictive power for stroke (AUC:0.696, 95% CI 0.677–0.715), slightly higher than the other indicators. Associations between TyG, TyG-WC, TyG-WHtR, and TyG-BMI with stroke were found to be stronger among individuals who were ≥ 55 years of age, male. The relationship between TyG and stroke was significantly mediated by BMI, WHtR and WC (15.79%, 21.72%, and 24.06% respectively), while the relationship between these obesity measures and stroke was significantly mediated by TyG (18.48%, 14.45%, and 14.70% respectively).ConclusionThe combination of TyG and obesity-related indicators was significantly associated with stroke risk, and could improve predictive power for stroke compared to the single TyG. Obesity indicators and TyG mediated each other in their respective associations with stroke risk.

Atherogenic Index of Plasma (AIP): The Most Accurate Indicator of Overweight and Obesity Among Lipid Indices in Type 2 Diabetes-Findings From a Cross-Sectional Study.

This study aimed to evaluate the relationships of conventional and calculated lipid indices with obesity or overweight in patients with type 2 diabetes (T2D). In this cross-sectional study, all participants were categorised into three groups: normal weight (18 ≤ BMI < 25), overweight (25 ≤ BMI < 30) and obese (BMI ≥ 30). Conventional lipid indices, including triglyceride (TG), total cholesterol, LDL-C and HDL-C, were measured. Lipid indices, including the atherogenic index of plasma (AIP), non-HDL-C, TC/HDL-C, LDL-C/HDL-C, non-HLD-C/HDL-C (atherogenic index, AI) and lipoprotein combine index (LCI), were calculated. The associations of these lipid indices with obesity and overweight status were evaluated using univariate and multivariate multinomial logistic regression analyses. In this study, 2661 patients with T2D, including 651 patients with normal weight, 1144 with overweight, and 866 with obesity, were recruited. According to a multinomial logistic regression analysis after adjustment for multiple confounders, the odds ratio (OR) was greater for the AIP than for other conventional and calculated lipid indices in overweight and obese patients with T2D. The AIP had a significant relationship with overweight, with an OR of 4.79 (95% CI: 1.65-13.85), and it had a significant relationship with obesity, with an OR of 13.64 (95% CI: 3.96-47.04). According to the ROC curve, with a cut-off value of 0.68, AIP could predict obesity with 82% sensitivity and 55% specificity (AUC = 0.770, 95% CI: 0.729-0.812, p < 0.001). Compared with other conventional and calculated lipid markers, the AIP is most strongly associated with obesity and overweight in patients with T2D.

The Role of Body Mass Index on Physical Activity, Symptoms, and Related Outcomes Following Pediatric Concussion

To determine the step count and self-reported activity levels for obese and non-obese pediatric patients following concussion and predict self-reported symptoms, quality of life, and psychological health over time. Participants completed the Post-Concussion Symptom Scale (PCSS), Pediatric Quality of Life (PedsQL), and Behavioral Symptom Inventory-18 (BSI-18) at an initial visit (<72 hours), 3-5 days post-injury, 10-18 days post-injury, and 1 and 2 months post-injury. Physical activity data (eg, step count) were collected via a commercial actigraph. Participants were categorized into obese and non-obese body mass index (BMI) groups based on age- and sex-adjusted growth standards. Mann Whitney U tests compared groups while nonparametric series regression models examined the effect of obese BMI and average daily step count on PCSS, PedsQL, and BSI-18 outcomes at 1- and 2-months post-injury. 194 participants were included: 153 (78.9%; M=14.6 ± 2.4 years; 50% female) in the non-obese group and 41 (21.1%; M=14.2 ± 2.0 years; 44% female) in the obese group. The obese group had a 22.8% lower average daily step count in the week post-injury (p=0.02). At 2-months, there was a significant association between obese BMI and worse PCSS total score (p=0.042), PedsQL (p=0.017), and BSI-18 anxiety (p=0.046). Average daily step count in the first week post-injury was associated with a higher PCSS total score at 2 months (p=0.031). Pediatric patients following concussion with an obese BMI had a lower daily average step count in the week after injury and exhibited worse concussion symptoms, quality of life, and anxiety at 2 months compared with those with a non-obese BMI.

Associations of four surrogate insulin resistance indexes with non-alcoholic steatohepatitis in Chinese patients with obesity: a cross-sectional study.

This study was designed to evaluate the association of four surrogate indexes of IR with NASH in patients with obesity. A total of 270 patients who underwent bariatric surgery, were included in this cross-sectional study. NASH was diagnosed based on liver biopsies. Binary logistics regression analyses were performed to assess the associations of four surrogate indexes of IR (HOMA-IR, Matsuda index, TyG, and TG/HDL-C) with NASH in patients with obesity. The restricted cubic spline was used to assess the dose-response associations of surrogate indexes of IR with NASH after adjusting for confounding factors. NASH was diagnosed in 136 patients, with a prevalence of 50.37%. Compared with tertile 1, the fully adjusted ORs (95% CIs) of NASH for tertile 3 were 2.711(1.113-6.608) and 0.297 (0.152-0.579) for TyG and Matsuda index. Consistently, per SD increment of TyG were still significantly associated with 64% increased risks of NASH, and per SD increment of Matsuda index were still significantly associated with 38% decreased risks of NASH. In contrast, no significant associations were found between HOMA-IR and TG/HDL-C and the risk of NASH in patients with obesity (all P > 0.05). After adjusting covariates in restricted cubic splines, the risk of NASH decreased with the increment of Matsuda Index levels (P-nonlinear = 0.442, P-overall = 0.007) and with the decrement of TyG levels (P-nonlinear = 0.004, P-overall = 0.001). In patients with obesity, TyG and Matsuda index were independently related to the risk of NASH after adjustment for traditional risk factors. In addition, compared with HOMA-IR and TG/HDL-C, the Matsuda index and TyG may be more suitable for NASH prediction in patients with obesity.

Using Different Anthropometric Indices of Obesity as Predictors for Type 2 Diabetes Mellitus in Older Adults

Using Different Anthropometric Indices of Obesity as Predictors for Type 2 Diabetes Mellitus in Older Adults

Triglyceride-glucose Index and Its combination with obesity indicators mediating the association between 2-hydroxyfluorene and the prevalence of cardiovascular disease: Evidence from the NHANES (2005–2018)

Triglyceride-glucose Index and Its combination with obesity indicators mediating the association between 2-hydroxyfluorene and the prevalence of cardiovascular disease: Evidence from the NHANES (2005–2018)

Correlation of obesity indices with metabolic syndrome among Igbos in Enugu and their optimal cut-off points

Background: Metabolic syndrome also known as insulin resistance syndrome or Syndrome X, represents an association of risk factors for cardiovascular disease and type 2 diabetes mellitus. Its central diagnostic criteria include obesity, impaired fasting glucose, low HDL-cholesterol; elevated triglycerides levels and high blood pressure. Insulin resistance is believed to be the main cause of Metabolic Syndrome and is connected to the level of visceral or intra-abdominal adipose tissue, which could be assessed either by calculating body mass index or by measuring waist circumference. Objectives: This study aimed at determining the correlation of indices of obesity with metabolic syndrome among Igbos in Enugu and their optimal cut-off points. Method: This was a cross-sectional community-based descriptive survey carried out in Enugu Metropolis involving 469 apparently healthy adult volunteers age range 18-75 screened for metabolic syndrome using the Joint Interim Statement definition. Stratified random sampling technique was used in the selection of participants. The data obtained were coded and analyzed into percentages, mean and standard deviation using the Statistical Package for Social Sciences (SPSS), version 23. t-test was used to compare continuous variables for associations, while Youden index and Receiver Operating Characteristic analysis were used to determine optimal cut-off point of the obesity indices that best balances sensitivity and specificity of the obesity indices. Pearson correlation coefficient was used to examine correlation between obesity indices and cardiovascular risks. Statistical significance is set at p&lt; 0.05 and 95% confidence interval. Result: The study revealed that all indices of obesity showed positive correlation with metabolic syndrome. BMI showed the least strength of correlation while WC is the best discriminator for predicting Metabolic Syndrome however, the optimal cut-off point of the obesity indices differ from the standard cut-off points. Conclusion: All the obesity indices used in the study showed positive correlation with metabolic syndrome however, WC is to be preferred in screening for metabolic syndrome. The optimal cut-off point obtained can be applied specifically for Igbos in Enugu

The association between low carbohydrate dietary score (LCDS) and cardiovascular risk factors: results from the Shiraz Heart Study (SHS)

BackgroundCardiovascular disease (CVD) is a major cause of death and recent studies have highlighted the potential role of dietary carbohydrate indices in cardiovascular health. Given the controversial results in this field, the aim of this study was to investigate the association between low carbohydrate dietary score (LCDS) and CVD risk factors in a population of Iranian adults.MethodsThis cross-sectional study was conducted within the framework of the Shiraz Heart Study (SHS) including 1982 adults. The validated 168 items food frequency questionnaire (FFQ) was used to assess participants’ dietary intakes. To investigate the association between LCDS and cardiometabolic risk factors, logistic regression, was conducted.ResultsDuring 5 years of follow-up, a total of 1982 adults, with a mean age of 53.07 ± 8.38 years, were included to the analysis. The adjusted model based on known confounding factors (age, sex, smoking, physical activity, energy intake and body mass index) revealed a significant decrease in a body shape index (ABSI) (OR = 0.70, 95% CI= (0.50 to 0.98), P = 0.038) comparing highest LCDS tertile vs. the reference. In contrast, risk of hypertension (HTN), body mass index (BMI), waist-hip ratio (WHR), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), atherogenic index of plasma (AIP), and fasting blood sugar (FBS) were not significantly associated with LCDS.ConclusionCurrent findings suggest that adherence to a low carbohydrate diet reduces ABSI, a main indicator of central obesity.

Analysis of ventricular repolarization parameters and heart rate variability in obesity: a comparative study

Obesity is associated with dysfunctional electrocardiographic and cardiac autonomic parameters, which may lead to increased cardiovascular morbidity. Novel electrocardiographic repolarization markers such as Tpeak-Tend (Tpe) interval have not yet been deeply studied in obese patients. We aimed to investigate the association between ventricular repolarization parameters and heart rate variability (HRV) and how they are affected by changes occurring in the cardiac autonomic nervous system. Ninety subjects categorized by Southeast Asian BMI (kg/m2) standards - normal (18-22.9), overweight (23-24.9), and obese (> 24.9), underwent assessment of ventricular repolarization parameters and HRV. Linear correlation between different parameters was also conducted. Obese subjects exhibited longer QTc and Tpe intervals compared to normal-weight subjects (p-value < 0.001, 0.026 respectively). The QTc interval showed a significant correlation (p < 0.05) with all HRV parameters by linear correlation, while the Tpe interval did not. Anthropometric parameters (BMI, WC, and WHR) were also correlated to both ventricular repolarization variables and HRV. While changes in the QTc interval may be due to obesity and/or autonomic changes occurring in the obese state, the Tpe interval does not show a relation with autonomic parameters. Thus, implicating that a change in the Tpe interval is primarily due to the direct effect of either cardiac or visceral obesity. The observed associations between ECG parameters, obesity indices, and HRV parameters suggest a role for ECG in screening for cardiovascular morbidity.

Applicability of VICTORION-1 PREVENT Criteria to the US Population

Abstract Background VICTORION-1 PREVENT (V-1P) is an ongoing randomized clinical trial comparing inclisiran sodium 300mg every six months with placebo for primary prevention in patients with high cardiovascular (CV) risk. Purpose We evaluated the generalizability of V-1P trial enrollment criteria to the US population to understand the demographic and CV health characteristics of the trial target population. Methods We included data from the National Health and Nutrition Examination Survey (N=25,531, 2015-March 2020), a nationally representative sample of the US population. Enrollment criteria similar to V-1P were applied. Nonpregnant adults 40-79 years with low-density lipoprotein cholesterol (LDL-C) of 70-189 mg/dL were included. 10-year risk for CV disease was determined using the American Heart Association race-free 10-year PREVENT risk equations. Individuals with 10-year risk &amp;gt;20% or 7.5%-19.9% with two risk enhancing factors at baseline were included. Risk enhancing factors included elevated high sensitivity C-reactive protein (≥2 mg/L), inflammatory arthritis, estimated glomerular filtration rate &amp;lt;60 ml/min/1.73 m2, metabolic syndrome, or early family history of CV disease. Patients with history of atherosclerotic CV disease or liver disease were excluded. Estimates of demographics and health characteristics were nationally representative using survey weights. Results Among US adults, 21,576,506 (standard error [SE]: ±1,258,255) meet eligibility criteria for V-1P (weighted estimate). This represents 8.8% of all US adults or 15.1% of US adults 30-79 years. The V-1P eligible population had a mean age of 65.3 years (SE: ±0.5 years), and 55.0% (SE: ±2.5%) were female. Compared with the US adult population (≥18 years), V-1P eligible population had a higher mean body mass index (32.6 ± 0.3 kg/m2 vs 29.5 ± 0.2 kg/m2) and higher rates of obesity (61.0% ± 2.1% vs 40.0% ± 1.1%) (Table). V-1P eligible population also had higher mean systolic blood pressure (133.9 ± 1.0 mmHg vs 121.7 ± 0.3 mmHg), similar mean diastolic blood pressure (72.6 ± 0.8 mmHg vs 71.8 ± 0.2 mmHg), and higher rates of hypertension (85.8% ± 2.0% vs 45.9% ± 1.1%). Mean hemoglobin A1c percent (6.3 ± 0.1 vs 5.7 ± 0.02), fasting blood sugar (127.7 ± 1.9 mg/dL vs 109.2 ± 0.6 mg/dL), and diabetes rates (39.7% ± 2.6% vs 13.1% ± 0.7%) were higher in the V-1P eligible population. Mean low-density lipoprotein cholesterol (115.8 ± 1.5 mg/dL vs 110.2 ± 0.8 mg/dL) and fasting triglycerides (137.6 ± 5.0 mg/dL vs 110.4 ± 1.7 mg/dL) were higher, while mean high-density lipoprotein cholesterol was lower (49.9 ± 0.8 mg/dL vs 54.8 ± 0.4 mg/dL). The distribution of key CV risk factors is shown in the Figure. Conclusion V-1P enrollment criteria apply to many patients. These patients have significant cardiometabolic risk factor burden, and effective preventive therapies could have substantial health impact.

A Multi-Modal Approach for A Healthier Weight and More Active Lifestyle

Abstract Background Obesity and overweight has been increasing globally and in the UK over the last few years, bringing along a wide array of challenges in health and wellbeing. Not only do they take a heavy toll on those affected, but obesity and overweight-related ill-health is costing the UK National Health Service £6.1 with projected costs to run up to £9.7 billion in 2050. This project aimed to apply artificial intelligence to identify the predictive indicators for obesity and co-create an intervention with public health practitioners and stakeholders. Methods Using health and wellbeing public health population data from the 2015, 2018 and 2022 from a city in the East Midlands, we applied artificial intelligence that uses machine learning techniques to discover insights, find hidden patterns and discover relationships in the data about engaging in physical activity and a healthy diet. The data on dietary choices and physical activity preferences were further analysed using advanced machine learning techniques. Results The outcomes from our machine learning process revealed patterns of physical activity engagement and diet from specific locations, along with a range of demographic variables that influence individuals’ patterns of physical ability and dietary practices. These findings, which will be presented in detail, have been integrated into a comprehensive city map. This map not only showcases opportunities for engaging in a healthy lifestyle but also aids in prioritising interventions in areas of greatest need. Conclusions The AI technology we employed allowed us to parameterise and generalise our findings with the possibility of scaling up the machine learning approach to other similar datasets. The findings facilitated the development of bespoke training for public health officers working with underserved communities, further demonstrating the impact of applying AI to inform the design of public health interventions. Key messages • Applying Artificial Intelligence to public health data has the potential to inform the development of effective interventions and increase precision. • Using AI in public health data can generate new insights into population health and well-being and inform public health policy.

Trends in overall and abdominal obesity among Korean adults, 2007-2022: an Age-period-cohort analysis

Abstract Overall and abdominal obesity, often defined as having high body mass index (BMI) and waist circumference (WC), are associated with an increased risk of chronic disease. A comprehensive understanding of trends and contributing factors is necessary to reduce the disease burdens. This study investigated the nationwide trends and independent effects of age, period, and birth cohort on BMI and WC in Korean adults. We conducted hierarchical age-period-cohort (cross-classified random-effects models) analyses using measured BMI and WC data from 85,292 Korean adults (aged ≥19) in the Korea National Health and Nutrition Examination Survey 2007-2022. We also examined whether the age, period, and cohort effects varied by socioeconomic subgroups. In 2007-2022, the age-standardized mean BMI (23.6 to 24.2 kg/m2) and WC (81.7 to 83.5 cm) increased in the overall population. An upward trend was evident among men, while only a slight change was observed in women. The mean BMI was highest at age 47 (25.1 kg/m2) in men and age 60 (24.2 kg/m2) in women and it decreased at older ages. Controlling for age and period effects, mean BMI was lowest among the birth cohorts of 1960-1964 (24.15 kg/m2), with the highest mean BMI observed among the birth cohorts of 1919-1939 (24.97 kg/m2) and 1990-2003 (24.99 kg/m2). The mean WC was highest at age 55 (88.0 cm) in men and age 79 (83.4 cm) in women. Similar U-shaped birth cohort effect was observed with WC, with the lowest mean observed among the birth cohorts of 1960-1964 [82.92 (0.30)]. At most age, period, and birth cohorts, mean BMI and WC were consistently higher among adults at lower income and education. The overall period effect was consistently found in all socioeconomic subgroups, but the gap in BMI between income and education subgroups widened over time. Our findings suggest that BMI and WC slightly increased in overall Korean adults in 2007-2022, with some inequalities among age, sex, birth cohort, and socioeconomic subgroups. Key messages • The obesity indices increased from 2007 to 2022, but the cohort effect on BMI and WC showed a U-shaped pattern with the lowest mean among birth cohorts of 1960-1964. • The obesity prevalence is varied by socioeconomic status, suggesting that more intense interventions may be needed to target the susceptible groups.

Lipids as the link between central obesity and diabetes: perspectives from mediation analysis

BackgroundCentral obesity is a well-recognized risk factor for diabetes, yet the potential role of lipids in the diabetes risk associated with central obesity remains unclear. This study aimed to explore the possible mediating role of 11 lipid parameters [high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), non-high-density lipoprotein cholesterol (Non-HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), remnant cholesterol (RC), and ratios of Non-HDL-C/HDL-C, RC/HDL-C, LDL/HDL-C, TG/HDL-C, TC/HDL-C] in the association of central obesity with diabetes risk.MethodsWe utilized data from 15,453 participants in the NAGALA longitudinal cohort to assess the association of baseline central obesity indicators [waist-height ratio (WHtR), waist circumference (WC)] and the 11 lipid parameters with diabetes risk. Mediation analysis models were constructed to explore the mediating role of lipid parameters in the association of WC/WHtR with diabetes.ResultsConfirmatory associative analysis using multivariable Cox regression showed that, except for Non-HDL-C, TC and LD-C, the remaining eight lipid parameters were significantly associated with WC/WHtR and diabetes risk. Mediation analysis indicated that TG, RC, HDL-C, and lipid ratios such as Non-HDL-C/HDL-C ratio, RC/HDL-C ratio, TG/HDL-C ratio, TC/HDL-C ratio and LDL/HDL-C ratio are potential lipids affecting the diabetes risk related to central obesity. Among these, the RC/HDL-C ratio seemed to contribute the most in the WC/WHtR-related diabetes risk association, with a mediation percentage of about 37%. Additionally, lipid ratio parameters appeared to play a more mediating role in the association of central obesity-related diabetes risk than individual lipids.ConclusionsIn central obesity-related diabetes risk, most lipids, especially lipid ratio parameters, play a significant mediating role. Given these findings, we advocate for increased efforts in multifactorial risk monitoring and joint management of diabetes. The evaluation of lipids, particularly lipid ratio parameters, may be holds substantial value in the prevention and management of diabetes risk under close monitoring of central obesity.

The Effect of a Multidisciplinary Lifestyle Intervention Program on Apelin-12, Vaspin and Resistin Concentrations in Children and Adolescents with Overweight and Obesity

Background: Obesity in childhood and adolescence has reached epidemic proportions in recent decades. Methods: In the present study, we determined the concentrations of apelin-12, vaspin and resistin in 106 children and adolescents with overweight or obesity before and after the implementation of a multidisciplinary, personalized lifestyle intervention program of diet, sleep and exercise for 1 year. All subjects attended our Center for the Prevention and Management of Overweight and Obesity in Childhood and Adolescence. Results: Following the lifestyle intervention, there were significant decreases in BMI (p &lt; 0.01), apelin-12 (p &lt; 0.05) and resistin (p &lt; 0.01) concentrations, and an increase in vaspin (p &lt; 0.01) concentration. Glucose was the best positive predictor of apelin-12 (b = 0.236, p &lt; 0.05), and osteopontin was the best negative predictor of changes in apelin-12 (b = −0.299, p &lt; 0.05). Vaspin correlated positively with adiponectin (b = 0.29, p &lt; 0.05), while vitamin D (b = 0.621, p &lt; 0.05) was the best positive predictor of vaspin. BMI z score (b = −0.794, p &lt; 0.05), HDL (b = −0.284, p &lt; 0.05) and HbA1C (b = −0.262, p &lt; 0.05) were the best negative predictors of changes in vaspin. BMI z score was the best positive predictor of resistin (b = 0.437, p &lt; 0.05). Conclusions: These findings suggest that apelin-12, vaspin and resistin correlate with indices of obesity, glucose, lipids and bone metabolism, while interaction with other proteins, such as osteopontin and adiponectin, was also noted. Therefore, apelin-12, vaspin and resistin may be used as biomarkers in children and adolescents with overweight and obesity.