Neck Circumference Research Articles

The Role of Neck Circumference in Predicting Body Fat Distribution: A Cross-Sectional Study Among Healthy Female Medical Students

Background: Obesity, characterized by excessive fat accumulation, poses significant health risks. Traditional anthropometric measures like BMI, waist circumference (WC), and waist-to-hip ratio (WHR) are commonly used to assess obesity, but they have limitations. This study investigates neck circumference (NC) as an alternative measure for assessing body fat distribution, particularly in the context of the "thin-fat Indian" phenotype, which exhibits central obesity despite a generally lean physique. Methods: This cross-sectional study, conducted at xxx in 2023, included 63 healthy female medical students aged 18-25 years. After obtaining ethical approval and informed consent, participants underwent anthropometric measurements including NC and body fat percentage, the latter determined via bioelectrical impedance analysis (BIA). NC was measured following ISAK guidelines. Statistical analysis was performed using IBM SPSS Statistics (version 25), with Pearson's correlation coefficient and simple linear regression analysis used to explore the relationship between NC and body fat parameters. Results: The mean age of participants was 20.8±8.2 years. Mean NC was 30.03 ± 4.74 cm. The analysis revealed a strong positive correlation between NC and body fat percentage across various body sections (all r > 0.74, all p < 0.001), with the highest correlation observed for arm fat percentage (r = 0.960). Simple linear regression analysis indicated that NC significantly predicts several body fat measures, accounting for 57.9% of the variability in BMI, 68.7% in total body fat percentage, 79.3% in subcutaneous fat percentage, 55.7% in visceral fat percentage, 92.2% in arm fat percentage, 88.9% in trunk fat percentage, and 87.1% in leg fat percentage. Conclusion: The findings suggest that NC is a robust predictor of body fat distribution, particularly for arm and trunk fat percentages. This aligns with previous research highlighting NC as a valuable tool for identifying obesity-related health risks. The strong correlations and high coefficients of determination underscore the potential of NC as a simple, non-invasive, and cost-effective method for assessing body fat composition, especially in community settings.

Evaluatıng osa rısk in drıvers: polysomnography and anthropometric measurements.

Sleepiness significantly contributes to traffic accidents, with obstructive sleep apnea syndrome (OSA) being the leading medical cause. Effective treatment of OSA has been shown to reduce accident-related deaths and injuries. Turkish legislation has been updated to address driver's license issues for individuals with OSA, but implementation challenges and debates persist. Data from 159 patients who applied to obtain or renew their driver's licenses and underwent polysomnography between January 2020 and January 2024 were evaluated retrospectively. The body mass index of 126 patients (79.24%) exceeded 33kg per meter of height squared. OSA (apnea hypopnea index greater than 5) was detected in 85.5% of our patients, and the rate of severe OSA was 31.4%. We evaluated the presence of severe OSA using logistic regression analysis. Among the variables, the most important factor was neck circumference Receiver operating characteristic curve analysis identified severe OSA risk thresholds as neck circumference of 42.5cm or greater and waist circumference of 122cm or greater. Considering neck and waist circumference rather than body mass index may facilitate informed decisions based on empirical measurements to identify OSA in driver's license applicants who require overnight polysomnography assessment.

Cross-Cultural Adaptation of a Health-Related Quality-of-Life Questionnaire for Children with Obstructive Sleep Disorders: Spanish Version of the OSD-6

Background/Objectives: To translate the OSD-6 questionnaire (6-item quality of life questionnaire for children with obstructive sleep disorders) into Spanish and to assess its psychometric properties and clinical usefulness. Methods: We included children with obstructive sleep apnea (OSA). All underwent polysomnography before and after adenotonsillectomy. Study variables included age, sex, symptoms, polysomnography values, body measurements, and Mallampati and Brodsky classification. Parents or caregivers completed the OSD-6 at baseline and 3 to 6 months after adenotonsillectomy. Following translation and back-translation of the instrument, we evaluated its internal consistency, reliability, construct validity, concurrent validity, predictive validity, and sensitivity to change. Results: We included 45 boys and 15 girls. Mean body mass index was 18 (standard deviation [SD] 4) kg/m2 and mean neck circumference was 28 (SD 5) cm. Seven per cent of children had Brodsky grade 0, 12% had grade 1, 27% had grade 2, 45% had grade 3, and 6% had grade 4. Mean apnea-hypopnea index (AHI) was 12 (SD 7) h−1 before adenotonsillectomy. The overall Cronbach’s alpha was 0.8. We found significant concurrent validity in each questionnaire domain and in the overall score. Predictive validity was significant for Mallampati scores (ANOVA p = 0.011) and borderline significant for AHI levels (ANOVA p = 0.069). The study demonstrated excellent sensitivity to change, both in terms of the overall analysis (p < 0.001) and in each domain (p < 0.001). Moreover, the test-retest reliability was found to be equally excellent (global intraclass correlation coefficient = 0.92 [0.89–0.95]). Conclusions: OSD-6 is a valid and reliable instrument to measure quality of life in children with OSA and can be used in Spanish-speaking countries.

Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features

ObjectivesSarcopenic obesity (SO), characterized by the coexistence of obesity and sarcopenia, is an increasingly prevalent condition in aging populations, associated with numerous adverse health outcomes. We aimed to identify and validate an explainable prediction model of SO using easily available clinical characteristics.Setting and participantsA preliminary cohort of 1,431 participants from three community regions in Ziyang city, China, was used for model development and internal validation. For external validation, we utilized data from 832 residents of multi-center nursing homes.MeasurementsThe diagnosis of SO was based on the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) criteria. Five machine learning models (support vector machine, logistic regression, random forest, light gradient boosting machine, and extreme gradient boosting) were used to predict SO. The performance of these models was assessed by the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) approach was used for model interpretation.ResultsAfter feature reduction, an 8-feature model demonstrated good predictive ability. Among the five models tested, the support vector machine (SVM) model performed best in SO prediction in both internal (AUC = 0.862) and external (AUC = 0.785) validation sets. The eight key predictors identified were BMI, gender, neck circumference, waist circumference, thigh circumference, time to full tandem standing, time to five-times sit-to-stand, and age. SHAP analysis revealed BMI and gender as the most influential predictors. To facilitate the utilization of the SVM model in clinical setting, we developed a web application (https://svcpredictapp.streamlit.app/).ConclusionsWe developed an explainable machine learning model to predict SO in aging community and nursing populations. This model offers a novel, accessible, and interpretable approach to SO prediction with potential to enhance early detection and intervention strategies. Further studies are warranted to validate our model in diverse populations and evaluate its impact on patient outcomes when integrated into comprehensive geriatric assessments.

Predictors for cure of obstructive sleep apnea in bariatric patients.

Predictors for cure of obstructive sleep apnea in bariatric patients.

Development and Validation of a Screening Equation for Obstructive Sleep Apnea.

Background: The high prevalence of obstructive sleep apnea (OSA), about 30% of people worldwide over 30 years old, underscores the crucial need for early screening. This study aimed to identify key predictive factors for OSA; use these factors to develop a screening equation for a population at high risk for OSA; and prospectively validate this equation's application. Methods: The study included two phases: a retrospective phase examining anthropometric data, the Epworth sleepiness scale (ESS), and the home sleep apnea test (HSAT) from 200 patients referred to the Respiratory Sleep Disorder Center at Policlinico Umberto I, Rome, Italy (January 2020-January 2023) to create a predictive equation for OSA using multivariate analysis (with the most predictive data according to scientific literature). A prospective phase testing this equation on 53 patients from May 2023 to September 2024. Results: In the retrospective phase, the most predictive variables for the apnea-hypopnea index (AHI) identified were neck circumference (NC) and the Epworth sleepiness scale (ESS). The predictive equation derived from the multivariate analysis was as follows: AHIp = [-70.498 + (2.196 × NC) + (0.809 × ESS)]. In the prospective phase of the study, we compared the AHI predicted by the equation (AHIp) with the AHI measured via the HSAT (AHIm) in 53 patients recruited. The results showed that AHIp had a sensitivity of 95%, a specificity of 28%, a positive predictive value (PPV) of 46%, and a negative predictive value (NPV) of 90%. Conclusions: This study identified NC and ESS as key predictors of OSA, forming a predictive equation. This equation, showing high sensitivity and high NPV, may be useful as a screening method to rule out OSA.

Association between normal tension glaucoma and the risk of obstructive sleep apnoea using the STOP-Bang questionnaire.

To assess the association between patients with normal tension glaucoma (NTG) and the risk of obstructive sleep apnoea (OSA) using the STOP-Bang questionnaire. A population-based cross-sectional analysis was performed using data from the Korean National Health and Nutrition Examination Survey from 2019 to 2021. An overall of 8832 subjects ≥ 40 years of age were investigated. Patients with NTG and controls completed the STOP-Bang questionnaire, a validated tool for identifying high-risk patients for OSA. The patients were divided into low-, intermediate-, and high-risk groups based on their scores. The mean STOP-Bang scores were 2.04 ± 0.02 in the control group and 2.42 ± 0.10 in the NTG group, with significant differences between groups (p < 0.001). In univariate logistic regression models, both intermediate-risk (Odds ratio (OR), 1.42; 95% Confidence intervals (CI), 1.05-1.91) and high-risk (OR, 1.71; 95% CI, 1.22-2.40) for OSA groups revealed a significant relationship regarding NTG compared to low-risk for OSA group. When adjusted for all confounding factors, the high risk for the OSA group was significantly associated with NTG (OR, 1.54; 95% CI, 1.06-2.24). Our results indicate that high-risk OSA, as measured using the STOP-Bang questionnaire, correlates with NTG. Therefore, patients who scored two or more on the STOP-Bang questionnaire and were male, had a BMI > 35 or a neck circumference > 40 cm, or those who scored five or more and were classified as high risk should be carefully monitored because of the significantly higher likelihood of an association with NTG.

Optimizing Obstructive Sleep Apnea Risk Assessment in Hypertension: Development of a Predictive Nomogram in China.

Obstructive sleep apnea (OSA) is common in patients with hypertension. Our study aims to construct and validate an objective nomogram that can accurately predict the risk of OSA in patients with hypertension. Retrospective data were collected from patients with hypertension who underwent polysomnography (PSG) at the Sleep Medicine Center of the First Affiliated Hospital of Guangzhou Medical University, China. All participants were assigned to the training group (used to develop the predictive model). Similarly, data from patients with hypertension who underwent PSG at the Sleep Medicine Center of the Second Affiliated Hospital of Guangdong Medical University, China, were collected, and these participants were assigned to the validation group (used to test the model's performance). Logistic and LASSO regression analyses were used to identify factors and construct the nomogram. C-index, calibration curve, decision curve analysis (DCA) and clinical impact curve analysis (CICA) were used to assess the model. Finally, nomogram validation was performed in the validation group. This study included a training group of 303 patients and a validation group of 217 patients. Based on LASSO and Logistic regression analyses and clinical practicality, we identified gender, age, BMI (body mass index), NC (neck circumference) and ESS (Epworth Sleepiness Scale) as predictors for the nomogram. The C-index is 0.840 in the training group and 0.808 in the validation group. The area under the curve (AUC) of the predictive model and STOP-Bang at the three diagnostic cut-off points of the Apnea-Hypopnea Index (AHI) ≥ 5, AHI ≥ 15 and AHI ≥ 30 were 0.840 vs 0.778, 0.754 vs 0.740, and 0.765 vs 0.751 respectively. The AUC at each intercept point was higher than that of STOP-Bang. DCA and CICA showed that the nomogram is clinically useful. The nomogram predictive model consisting of the five indicators (gender, age, BMI, NC and ESS) can be useful in determining OSA risk in patients with hypertension.

Cashew nut consumption reduces waist circumference and oxidative stress in adolescents with obesity: A randomized clinical trial.

Cashew nut consumption reduces waist circumference and oxidative stress in adolescents with obesity: A randomized clinical trial.

The Effect of Exercise on Spexin and Follistatin in Elderly Individuals.

In adipose tissue-muscle crosstalk mechanisms, the interaction of adipokines and myokines is known to be critical for maintaining the body's metabolic balance in age-related metabolic disorders. The aim of the study investigate the effects of 12 weeks of aerobic and resistance exercise training on spexin and follistatin and their relationship with each other. This study was a multicentre, randomized controlled study conducted at two assisted living facilities with participants aged ≥ 65. Among the 66 subjects, 33 were allocated to the exercise group (E) and 33 to the control group (C). The exercise group was administered 50 min of exercise by expert physiotherapists 1 day a week for 12 weeks. Participants in the intervention groups performed exercise assignments two extra days a week, tailored to their specific circumstances and supervised by the institution's physiotherapists. Spexin, follistatin and measurements of metabolic syndrome parameters were performed at the beginning and after 12 weeks. The mean age of the 62 participants who completed the study (E n = 31, C n = 31) was 73.25 ± 6.44 years, and 62.9% were female. While spexin (E = 1090.94 ± 533.66, C = 1142.91 ± 550.68 pg/mL, p > 0.05) and follistatin (E = 50.52 ± 24.35, C = 50.00 ± 23.52 ng/mL, p > 0.05) values were similar in the two groups at baseline, the values of spexin (E = 1311.32 ± 513.66, C = 1033.27 ± 486.48, p < 0.0001; η2 = 0.387) and follistatin (E = 64.79 ± 32.35, C = 48.16 ± 26.27, p < 0.0001; η2 = 0.267) in the exercise group were higher than in the control group at week 12. At the 12th week, neck circumference (38.32 ± 3.41, 37.16 ± 3.15, p = 0.002), waist circumference (102.64 ± 13.38, 98.54 ± 14.47, p < 0.0001), hip circumference (105.70 ± 15.43, 102.93 ± 13.48, p < 0.0001), body fat mass (22.69 ± 7.39, 20.45 ± 6.22, p < 0.0001) and systolic and diastolic blood pressure (137.19 ± 13.80, 124.9 ± 15.18, p = 0.0001, 77.38 ± 12.10, 72.61 ± 9.26, p = 0.043) decreased, and body muscle mass (46.32 ± 8.43, 49.03 ± 8.58, p < 0.0001) increased in the exercise group compared to baseline. A correlation was observed between the change in follistatin level and the change in spexin level (r = 0.438, p = 0.001). A negative correlation was found between the amount of decrease in body fat mass and the decrease in spexin level (r = -0.380, p = 0.005). A positive correlation was found between the increase in body muscle mass and the increase in spexin and follistatin (r = 0.431, p = 0.001; r = 0.490, p < 0.0001, respectively). It was found that spexin, which provides metabolic homeostasis, and follistatin, which expresses the increase in muscle mass, increased with the implementation of a 12-week aerobic and resistance exercise program in elderly individuals, and these increases were found to be associated with each other. ClinicalTrials.gov identifier: NCT05251597.

Factors Influencing Obstructive Sleep Apnea Among Employees With Metabolic Syndrome: An Example of a District Hospital in Northern Taiwan

Obstructive sleep apnea (OSA) is a common sleep disorder shown to be significantly correlated with metabolic syndrome (MS). Healthcare professionals affected by both MS and OSA may suffer from poor sleep quality, raising potential concerns about patient safety. Currently, there remains a gap in research specifically addressing the relationship between MS and OSA in healthcare professionals. This study was conducted to examine the prevalence of employees with MS concomitant with OSA and to identify the factors of influence affecting MS employees with OSA in the workplace. A cross-sectional study design and convenient sampling were used, and participants were recruited from a district hospital in northern Taiwan. Data were collected using a structured questionnaire and a home sleep apnea test. Multiple logistic regression analysis was used to determine the significant factors influencing OSA in employees with MS. A total of 89 participants completed the assessment, with 65.2% of those with MS also found to have OSA. The main factors found to be associated with these employees with MS having OSA as well include gender, job position, body mass index, neck circumference, waist circumference, oxygen desaturation index, and snoring frequency. In light of the high percentage (65.2% in this study) of MS employees affected by OSA, regular health screenings should include a home sleep apnea test for high-risk employees to assess their risk of developing OSA. In addition, promoting a healthy lifestyle to improve MS and prevent OSA is needed.

Screening prediction models using artificial intelligence for moderate-to-severe obstructive sleep apnea in patients with acute ischemic stroke.

Screening prediction models using artificial intelligence for moderate-to-severe obstructive sleep apnea in patients with acute ischemic stroke.

Prediction of Postoperative Sore Throat in Patients After Day-case Surgery With General Anesthesia: A Retrospective Study

Prediction of Postoperative Sore Throat in Patients After Day-case Surgery With General Anesthesia: A Retrospective Study

Comparison of NoSAS score with STOP-Bang and Berlin scores in predicting difficult airway

BackgroundThis study aimed to compare the effectiveness of the NoSAS, STOP-Bang, and Berlin scoring systems, which are utilized to predict obstructive sleep apnea syndrome (OSAS), in forecasting difficult airway management. Additionally, the study sought to determine which of these scoring systems is the most practical and effective for this purpose.MethodsFollowing the ethics committee approval, preoperative NoSAS, STOP-Bang, and Berlin scores were calculated for 420 patients aged 18 years and older who were scheduled for tracheal intubation. Mask ventilation and intubation were performed by research assistant with a minimum of two years of experience. Detailed examinations and recordings were conducted, including demographic data, neck circumference, OSAS diagnosis, history of difficult intubation, comorbidities, ASA classification, Mallampati classification, and Cormack-Lehane grade. Subsequently, the predictive efficacy of these three scoring systems for difficult mask ventilation and difficult intubation was compared.ResultsIn our study, 83 patients (19.8%) were classified as having difficult mask ventilation, and 101 patients (24.0%) were classified as having difficult intubation. The NoSAS score demonstrated a higher predictive power compared to the other scoring systems for difficult mask ventilation and difficult intubation. The cut-off value for the NoSAS score was determined to be 6.5 for predicting difficult mask ventilation and 7.5 for predicting difficult intubation.ConclusionThe routine implementation of the NoSAS score, an easy-to-use, rapid and objective tool primarily developed for OSAS screening, is likely to be effective in preoperatively identifying difficult airways in patients undergoing general anesthesia.

Individualised Therapy for Obstructive Sleep Apnoea: Predictive Models and Anatomical Phenotyping of Mandibular Advancement Devices Responses.

This non-randomised clinical study aimed to identify the phenotypic characteristics that distinguish responders from non-responders. Additionally, it sought to establish a predictive model for treatment response to obstructive sleep apnoea (OSA) using mandibular advancement devices (MAD), based on the analysed phenotypic characteristics. This study, registered under identifier NCT05596825, prospectively analysed MAD treatment over 6 years using two-piece adjustable appliances according to a standardised protocol. Two response definitions aligned with the latest International Consensus Statement on OSA severity were established. Logistic regression and CHAID models integrated baseline clinical, anthropometric, cephalometric anatomical, soft tissue characteristics and physiological upper airway variables. A total of 112 patients completed the study: 64 responders and 48 non-responders according to response definition 1, and 81 responders and 31 non-responders according to response definition 2. Responders to MAD treatment had lower body mass index (BMI), neck and waist circumference, Epworth Sleepiness Scale scores, apnoea-hypopnea index (AHI), snoring intensity on the Visual Analog Scale, CPAP pressure, and higher T90% and minSaO2. Patients exhibiting greater anatomical imbalance, smaller airway volume, smaller minimum cross-sectional area (CSAmin) and longer airway length demonstrated a poorer response to treatment. Airway length, initial T90% and anterior facial height collectively formed a highly predictive logistic regression model for response definition 1. Jarabak's ratio, gonial angle, CSAmin, airway length, initial BMI and baseline AHI constituted a highly predictive model for the second response definition. Furthermore, the CHAID regression tree established cutoff values for the variables that form the predictive models.

Sleep apnea screening through a news portal using the STOP-bang questionnaire.

Obstructive sleep apnea (OSA) affects up to 936million adults globally and is linked to significant health risks, including neurocognitive impairment, cardiovascular diseases, and metabolic conditions. Despite its prevalence, OSA remains largely underdiagnosed. This study aimed to enhance OSA awareness and risk assessment using the STOP-Bang questionnaire in a telemedicine format. During a six-week campaign on a popular Latin American news portal, 5,966 adults completed the STOP-Bang questionnaire. Participants reporting moderate or severe OSA risk were advised to seek clinical evaluation. Among respondents, 44.7% were identified as having a moderate-to-high risk for OSA. Key risk factors included snoring, witnessed apneas, hypertension, male gender, older age, high BMI, and larger neck circumference. Statistical analyses showed significant associations between these variables and OSA risk. This study highlights the importance of increasing OSA visibility and early detection in the general population. Despite limitations such as selection bias and potential false negatives/positives with the STOP-Bang tool, the findings demonstrate the potential of media campaigns to raise awareness and prompt early medical consultation. Future efforts should include follow-up assessments to evaluate healthcare-seeking behavior and confirm OSA diagnoses, contributing to improved public health outcomes.

Anthropometric and metabolic parameters associated with visceral fat in non-obese type 2 diabetes individuals

Background and AimVisceral fat (VF) was proved to be a more precise predictor of atherosclerotic cardiovascular disease (ASCVD) risk in individuals with type 2 diabetes mellitus (T2DM) than body mass index (BMI) itself. Even when the BMI was normal, visceral fat area (VFA) ≥ 90 cm² could raise the ten-year risk of developing ASCVD. Therefore, it was worth evaluating the association of influencing factors with high VF in non-obese T2DM individuals.MethodsThis study enrolled 1,409 T2DM participants with T2DM, of whom 538 had a normal BMI. Based on VFA, these subjects were divided into two groups: VF (+) (VFA ≥ 90cm2) (n = 110) and VF (-) (VFA < 90cm2) (n = 428). The measurement of VFA was conducted using an Omron VF measuring device. Anthropometric and metabolic parameters were detected. Novel insulin resistance indices, such as lipid accumulation product (LAP) was calculated. Factors associated with VF were screened using univariate analysis, multifactorial binary logistic regression models and chi-squared automatic interaction detector decision tree model.ResultsThe VF (+) OB (-) (BMI ≤ 23.9 kg/m2) prevalence were 7.8% in T2DM subjects (n = 1,409) and 20.4% in T2DM subjects with normal BMI (n = 538), respectively. In T2DM subjects with normal BMI, the logistic regression model suggested that neck circumference (NC) had an odds ratio (OR) of 1.891 (95% CI: 1.165–3.069, P = 0.010). The OR for VF gradually increased from the 1st to the 4th in LAP quartile (P < 0.05). LAP emerged as the root node, followed by NC in the decision tree model. Receiver operating characteristic curve (ROC) analysis demonstrated that the area under the curve (AUC) for NC in predicting high VF levels was 0.640 for males and 0.682 for females. Optimal NC cut-off points were 37.75 cm for males and 34.75 cm for females, respectively. Additionally, the AUC values of LAP in predicting high VF levels were 0.745 for males and 0.772 for females, with optimal LAP cut-off points of 22.64 and 26.45 for males and females, respectively.ConclusionThis study identified NC and LAP can be considered predictors of high VF in T2DM subjects with normal BMI.Graphical

Comparison of Inter-Incisor Distance as a Predictor of Difficult Intubation in Men and Women-A Prospective Observational Study.

Most of the studies on difficult intubation and laryngoscopy focused on American and European populations. However, Indians have distinct anthropometric characteristics compared to these populations. This study aims to determine the gender difference in inter-incisor distance (IID) cut-off marks to assess the ease of intubation in the Indian population. A prospective observational study was conducted on 516 patients scheduled for elective surgery under general anesthesia with endotracheal intubation. Preoperative assessments included IID, thyromental distance, neck circumference, sternomental distance (SMD), thyrohyoid distance, mentohyoid distance, and modified mallampatti grading. Intubation difficulty scores (IDSs) were assessed during intubation. The optimal cut-off values of inter incisor gap and difficult intubation were analyzed by receiver operating characteristic curve analysis. The total cohort was grouped into males (Group 1 [n = 258]) and females (Group 2 [n = 258]). Males had a significantly higher mean age (P = 0.04) and weight (P = 0.00) than females. Difficult intubation incidence was 16.3% of males and 6.2% of females. Increased IID (P = 0.039), decreased SMD (P = 0.046), and males (P = 0.002) are identified as predictors of difficult intubation.IID and IDS showed greater specificity than sensitivity, with significant cut-off values of 4.25 and 1.50, respectively. The cut-off value of IID did not show a significant difference in gender variation. Despite the low IID in females, intubation difficulties were not observed compared to males.

The Relationship Between Body Mass Index, Waist Circumference, Neck Circumference and Cardiovascular Risk Factors

Background: This study aimed to estimate the correlation between body mass index, waist circumference and neck circumference and cardiovascular risk factors in healthy adult men and women with no history of any disease or chronic drug intake from Diyarbakır and Dicle University medical faculties. Materials and methods: The study included 199 subjects, 113 males (mean age 36.5 ± 11.3 years) and 86 females (mean age 33.8 ± 11.7 years) aged between 20 and 73 years. Age, height, weight, blood pressure, pulse rate, anthropometric methods; waist circumference (WC), body mass index (BMI), neck circumference (NC), blood glucose and insulin levels were determined. Insulin resistance was evaluated with the Homeostasis model assessment (HOMA-IR) formula. Total-cholesterol, triglyceride, LDL-cholesterol, HDL-cholesterol levels were measured. Pearson correlation and independent t-test were applied to the obtained study parameters. Results: 47.7% of women were obese, 1.2% were morbidly obese and 59.3% of men were obese. Waist circumference correlated with systolic blood pressure (r=0.426, p&lt;0.001), diastolic blood pressure (r=0.414, p&lt;0.001), total cholesterol (r=0.450, p&lt;0.001), triglycerides (r=0.556, p&lt;0.001) and LDL-cholesterol (i=0.486, p&lt;0.001), and a negative correlation was found between waist circumference and HDL-cholesterol (r=0.261, p&lt;0.001). In women, the relationship between BMI and HOMA-IR (r-0.415, p&lt;0.001) was stronger than the relationship between waist circumference and HOMA-IR (r=0.333, p=0.02). In men, we found a positive correlation between waist circumference and HOMA-IR (r=0.271, p=0.04). In men, we found no correlation between BMI and HOMA-IR. Neck circumference was correlated with waist circumference (i=0.732), total cholesterol (r=0.534), triglycerides (r=0.365), LDL cholesterol (i=0.406), BMI (r=0.568), systolic blood pressure (r=0.200), diastolic blood pressure (r=0. 290), and glucose (r=0.280) (p &lt; 0.001). Conclusions: Waist circumference was more tightly correlated with metabolic parameters. Waist circumference may better reflect the risk of cardiovascular disease than BMI. Neck circumference measurement, a new obesity indicator, can also be used as an obesity assessment method. Keywords: Cardiovascular risk factors, body mass index, waist circumference, neck circumference

PREDICTING DIFFICULT INTUBATION IN ELECTIVE SURGICAL PATIENTS: THE ROLE OF BEDSIDE SONOGRAPHIC MEASUREMENTS

Objective: Predicting difficult intubation remains a pivotal challenge in anesthesia, with traditional methods often failing to identify all at-risk patients. This study investigates the efficacy of bedside sonographic measurements as predictors of intubation difficulty in elective surgical patients. Methods: A prospective observational study was conducted at Gulbarga Institute of Medical Sciences, Kalaburagi, involving 60 adult elective surgery patients. Sonographic measurements, including hyomental distance, pre-epiglottic space, anterior soft tissue distance, and neck circumference were evaluated for their ability to predict difficult intubation. Results: The study found significant associations between all measured parameters and intubation difficulty. Specifically, shorter hyomental and pre-epiglottic spaces were strongly predictive of difficult intubations. Increased anterior soft tissue thickness and larger neck circumference were also linked to higher intubation difficulty. Conclusion: Bedside sonographic measurements provide a reliable method for predicting difficult intubations in elective surgical patients. Their integration into preoperative evaluations can significantly enhance the prediction and management of difficult airways.