Association between mid-upper arm circumference and cardiometabolic risk in Chinese population: a cross-sectional study

The relationship between mid-upper arm circumference and blood pressure in Walter Sisulu University community

Prevalence of hypertension is increasing to higher levels in South Africa. Anthropometric measures for obesity are well known to predict the development of hypertension. However, the relationship between mid-upper arm circumference (MUAC) and blood pressure (BP) is scant in South African communities such as universities. Therefore, this study was aimed at investigating the correlation between MUAC and BP among the community of Walter Sisulu University (WSU). A total of 230 participants from WSU (students and staff members), 113 females and 117 males aged ≥ 18 years participated in this cross-sectional study. MUAC, systolic BP (SBP) and diastolic BP (DBP) were measured using standard procedures. In a Pearson's correlation analysis, MUAC was positively correlated with SBP and DBP in both women (SBP; r = 0.53, P< 0.001; DBP; r = 0.45 P < 0.001) and men (SBP; r = 0.29 P = 0.001; DBP; r = 0.25 P = 0.007). Furthermore, in the multivariable-adjusted regression analysis, MUAC was positively associated with SBP in women only (adjusted R2 = 0.489, β = 0.29 (95% CI = 0.16; 2.08)), P =0.023) after adjusted for age, body fat percentage, waist-to-height ratio, smoking and alcohol. MUAC is positively correlated with BP in women, not in men of WSU community. MUAC, as a simple and low-cost quantifiable parameter, could be employed as a risk indicator in the early detection and prevention of cardiovascular diseases (CVDs) in women.

A cohort study on the predictive capability of body composition for diabetes mellitus using machine learning

Purpose

We applied machine learning to study associations between regional body fat distribution and diabetes mellitus in a population of community adults in order to investigate the predictive capability. We retrospectively analyzed a subset of data from the published Fasa cohort study using individual standard classifiers as well as ensemble learning algorithms.

Methods

We measured segmental body composition using the Tanita Analyzer BC-418 MA (Tanita Corp, Japan). The following features were input to our machine learning model: fat-free mass, fat percentage, basal metabolic rate, total body water, right arm fat-free mass, right leg fat-free mass, trunk fat-free mass, trunk fat percentage, sex, age, right leg fat percentage, and right arm fat percentage. We performed classification into diabetes vs. no diabetes classes using linear support vector machine, decision tree, stochastic gradient descent, logistic regression, Gaussian naïve Bayes, k-nearest neighbors (k = 3 and k = 4), and multi-layer perceptron, as well as ensemble learning using random forest, gradient boosting, adaptive boosting, XGBoost, and ensemble voting classifiers with Top3 and Top4 algorithms. 4661 subjects (mean age 47.64 ± 9.37 years, range 35 to 70 years; 2155 male, 2506 female) were analyzed and stratified into 571 and 4090 subjects with and without a self-declared history of diabetes, respectively.

Results

Age, fat mass, and fat percentages in the legs, arms, and trunk were positively associated with diabetes; fat-free mass in the legs, arms, and trunk, were negatively associated. Using XGBoost, our model attained the best excellent accuracy, precision, recall, and F1-score of 89.96%, 90.20%, 89.65%, and 89.91%, respectively.

Conclusions

Our machine learning model showed that regional body fat compositions were predictive of diabetes status.

Association of interarm blood pressure difference with selected body circumferences among Walter Sisulu University community

BACKGROUND

A high interarm blood pressure difference (IAD) has been identified as a precursor of vascular diseases. Anthropometric measures for obesity such as body circumferences including waist circumference (WC), mid-upper arm circumference (MUAC) and neck circumference (NC) have been associated with a high IAD in Western countries. However, the prevalence of IAD and its association with body circumferences in South African communities such as universities is not well established. Therefore, this study aimed at investigating the correlation of IAD with selected body circumferences among the Walter Sisulu University (WSU) community.

METHODS

A total of 230 participants, 117 males and 113 females, consisting of 185 students and 45 staff members from WSU, aged 18-27 and 18-63 years respectively, participated in this cross-sectional study. The selected body circumferences: WC, MUAC, and NC were measured using standard procedures. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured in both arms simultaneously using automated machines. IAD was achieved by calculating differences in SBP and DBP between the left (L) and right (R) arms, (R -L), and getting the absolute value, L-R (|L-R|).

RESULTS

14.78% of the participants had an interarm SBP difference (IASBPD) ≥ 10 mmHg, and 4.35% of participants had an interarm DBP difference (IADBPD) ≥ 10 mmHg. In a Pearson's correlation analysis, IASBPD was positively correlated with the selected body circumferences (WC, r = 0.29; P < 0.001; MUAC, r = 0.35; P < 0.001; NC, r = 0.27; P < 0.001) and mean arterial pressure (MAP) (r = 0.30; P < 0.001). In the multivariable-adjusted regression analyses, IASBPD was positively associated with MUAC (adjusted R2 = 0.128, β = 0.271 (95% CI = 0.09; 0.60), P = 0.008), and NC (adjusted R2 = 0.119, β = 0.190 (95% CI = 0.01; 0.32), P = 0.032) only, adjusted for MAP, age, gender, body mass index, smoking, and alcohol. There was no association of body circumferences with IADBPD.

CONCLUSION

A high IAD is common among students and staff members of the WSU community. Furthermore, IAD showed a positive correlation with MUAC and NC. These body circumferences can serve as indicators of high IAD, aiding in the early detection and prevention of vascular diseases.

Associations between Neck Circumference, Mid-Upper Arm Circumference, Wrist Circumference, and High Blood Pressure among Lithuanian Children and Adolescents: A Cross-Sectional Study

(1) Background: High blood pressure (HBP) and obesity are significant and growing public health issues worldwide. Our study aimed to evaluate the associations of neck circumference (NC), mid-upper arm circumference (MUAC), and wrist circumference (WrC) with HBP among Lithuanian children and adolescents aged 7-17 years. (2) Methods: In this cross-sectional study, data on BP and anthropometric measurements were analysed in 3688 children and adolescents aged 7-17 years. Multivariate logistic regression analysis was used to estimate the associations between anthropometric indices and HBP. (3) Results: Overall, the prevalence rates of elevated BP and hypertension were 13.7% and 12.9%, respectively. After adjustment for age, BMI, and WC, statistically significant elevated aORs were observed for associations between greater NC, MUAC, WrC, and HBP in boys (aORs: 2.13, 2.46, and 2.48, respectively) and in girls (aORs: 2.01, 2.36, and 2.09, respectively). Moreover, per-unit increase in NC, MUAC, and WrC was also associated with greater odds of HBP in boys (aORs: 1.20, 1.21, and 1.37, respectively) and in girls (aORs: 1.10, 1.10, and 1.21, respectively). The analysed anthropometric indices presented higher area under the curve values for predicting HBP in boys than in girls. (4) Conclusions: This study suggests that higher NC, MUAC, and WrC are associated with increased odds of HBP in Lithuanian children and adolescents.

The association of central and extremity circumference with all-cause mortality and cardiovascular mortality: a cohort study

Background

Central obesity increases the risk of several diseases, including diabetes, cardiovascular disease (CVD), and cancer. However, the association between extremity obesity and mortality has not been extensively evaluated. The objective of this study was to investigate the quantitative effects of waist circumference (WC), arm circumference (AC), calf circumference (CC), and thigh circumference (TC) on all-cause mortality and CVD mortality.

Methods

The study used data from the National Health and Nutrition Examination Survey (NHANES) sample survey from 1999 to 2006. A total of 19,735 participants were included in the study. We divided the participants into four groups (Q1-Q4) and used Q1 as a reference to compare the risk of all-cause mortality and CVD mortality in Q2-Q4. COX proportional hazard regression model was used to analyze the relationship between WC, AC, CC and TC on all-cause and CVD mortality. In addition, we conducted a stratified analysis of gender.

Results

After a mean follow-up of 11.8 years, we observed a total of 3,446 deaths, of which 591 were due to cardiovascular disease. The results showed that for both men and women, compared to the first group, the risk of all-cause mortality was significantly higher in the other three groups of WC and significantly lower in the other three groups of AC, CC, and TC. Similar results were observed after adjusting for confounding factors such as demographics.

Conclusions

Our results show that all-cause and CVD mortality are positively associated with measures of central obesity and negatively associated with measures of extremity obesity, and that AC, CC, and TC can be used as potential tools to measure prognosis in the general population.

Body composition predicts hypertension using machine learning methods: a cohort study

We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), and total and regional fat-free mass (FFM) were measured. We used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Bagging, Extra Tree, Ada Boost, Voting, and Stacking to classify the investigated cases and find the most relevant features to hypertension. FATP, AFFM, BMR, FFM, TRFFM, AFATP, LFATP, and older age were the top features in hypertension prediction. Arm FFM, basal metabolic rate, total FFM, Trunk FFM, leg FFM, and male gender were inversely associated with hypertension, but total FATP, arm FATP, leg FATP, older age, trunk FATP, and female gender were directly associated with hypertension. AutoMLP, stacking and voting methods had the best performance for hypertension prediction achieving an accuracy rate of 90%, 84% and 83%, respectively. By using machine learning methods, we found that BIA-derived body composition indices predict hypertension with acceptable accuracy.

Arm Circumference, Arm-to-Waist Ratio in Relation to Cardiovascular and All-Cause Mortality among Patients with Diabetes Mellitus

Among patients with diabetes mellitus, limited studies have investigated the relationship between anthropometric parameters and cardiovascular disease (CVD), with all-cause mortality. We examined the associations of arm circumference (AC), arm-to-waist ratio (AC/WC), and CVD, with all-cause mortality among patients with diabetes. This is a cohort study of 5497 diabetic individuals aged 20 or over who were recruited in the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2014. Cox proportional hazards regression models were used to analyze the associations between AC, AC/WC, and CVD, with all-cause mortality. We also conducted stratified analyses and explored the possible non-linear relation by restricted cubic splines. During a median follow-up of 7.2 years, there were 271 and 1093 cases of CVD and all-cause death. The multivariable adjusted hazard ratios (HRs) with 95% confidence intervals (Cis) of CVD mortality in Q2, Q3, and Q4 groups compared with Q1 group were 0.37 (0.22, 0.62), 0.24 (0.12, 0.48), 0.18 (0.07, 0.46) for AC, and 0.18 (0.07, 0.46), 0.34 (0.20, 0.60), 0.28 (0.15, 0.53) for AC/WC. Similar results were observed in the analysis for all-cause mortality risk. AC and AC/WC were both inversely associated with CVD and all-cause mortality among individuals with diabetes. It is important to pay attention to these anthropometric parameters of diabetic patients.

Study of metabolic syndrome indicators in newly diagnosed diabetes mellitus type 2 patients in Pakistani population

BACKGROUND

Metabolic syndrome (MetS) being a biological abnormality confers the highest risk of type 2 diabetes mellitus (T2DM).

AIM

To assess MetS indicators in the newly diagnosed T2DM Pakistani population.

METHODS

A cross-sectional study (N = 123) with newly diagnosed T2DM patients (gender: both, MeanAge: 49.24 ± 6.84 years) was selected from Medical OPD, Pakistan Institute of Medical Sciences, Islamabad. Basic profile, family history, and physical activity were recorded through a predesigned questionnaire, dietary intake through seven days Food Diary and Food Frequency Questionnaire. Blood pressure and selected clinical signs and symptoms were recorded. Anthropometric measurements included mid-upper arm circumference (MUAC), body mass index (BMI), waist circumference (WC), waist-and-hip ratio (WHR), waist-to-height ratio (WHtR), and conicity index. Lab parameters included fasting blood glucose, HbA1c, insulin levels, and lipid profile. Homeostatic model assessment of insulin resistance (HOMA-IR) was computed to assess insulin resistance.

RESULTS

Almost all parameters related to MetS were higher than normal. The prevalence of MetS was 59.7%. BMI, MUAC, and WHtR were independently associated with HOMA-IR, but WC, WHR, and the conicity index had no relationship MetS indicators.

CONCLUSION

The prevalence rate of MetS was found to be 59.7% in newly diagnosed T2DM patients. MUAC was found to be a better parameter for the diagnosis of central obesity and insulin resistance in the selected population.

Mid-upper arm circumference and neck circumference to screen for overweight-obesity in young adults in South India

Introduction

There are many anthropometric techniques to screen for overweight-obesity like the body mass index (BMI), waist circumference (WC), and waist-hip ratio (WHR). These may be difficult or less acceptable in community and outpatient settings. We determine the cut-offs of mid-upper arm circumference (MUAC) and neck-circumference (NC) to screen overweight (BMI ≥25 kg/m²), abdominal obesity by waist circumference (WC; men: ≥90 cm; women: ≥80 cm) and high percent body fat (PBF) (men: ≥20%; women: ≥30%) and explore participant preference for various anthropometric methods.

Method

ology: We enrolled 282 medical students in South India and performed anthropometry (height, weight, WC, MUAC and NC), bio-impedance analysis (BIA, Inbody 770) to detect PBF. Receiver operator curves were generated and best cut-offs derived using highest Youden Index (sensitivity + specificity-1).

Results

Of the 282 participants, 83 (29.4%) were overweight, 113 (38.7%) had abdominal obesity and 186 (66%) had higher PBF. The MUAC cut-off was 31.3cm for men (sensitivity: 86%; specificity: 74%) to detect overweight and 31.2 cm (sensitivity: 85%; specificity: 73%) to detect abdominal obesity. The corresponding cut-offs in women were 28.5 cm (sensitivity:88%; specificity: 83%) to detect overweight and 28.3 cm (sensitivity: 74%; specificity: 92%) for abdominal obesity. For NC, the proposed cut-off in men was 36.6 cm (sensitivity: 81%; specificity: 82%) for overweight and 37.1 cm (sensitivity:78%; specificity:82%) for abdominal obesity. In women, this was 31.4 cm for both overweight as per BMI (sensitivity: 88%; specificity: 71%) and for abdominal obesity (sensitivity: 75%; specificity: 81%). Neck circumference was preferred by 225 (79.8%) participants.

Conclusion

Both MUAC and NC can be considered for screening overweight and abdominal obesity with good sensitivity and specificity but their sensitivity and specificity for screening high PFB were not very good. Neck circumference was the most preferred anthropometric method.

Mid-upper arm circumference is associated with liver steatosis and fibrosis in patients with metabolic-associated fatty liver disease: A population based observational study

Metabolic-associated fatty liver disease (MAFLD) is a series of liver diseases based on liver steatosis and metabolic disorders. Steatosis, as the core factor in MAFLD diagnosis, and fibrosis, as the major determinant of adverse outcomes of MAFLD, need to be assessed simply and accurately. In this study, we explored the significance of mid-upper arm circumference (MUAC) in evaluating liver steatosis and fibrosis in patients with MAFLD. We included 2397 cases with MAFLD from the 2017-2018 National Health and Nutrition Examination Surveys (NHANES) database. Liver steatosis and fibrosis were measured by vibration controlled transient elastography. Anthropometric parameters and demographic and serological data were obtained from the NHANES database. The association between MUAC and liver steatosis and fibrosis were evaluated by a multivariable linear regression model, a weighted generalized additive model, and smooth curve fitting using R. MUAC was positively associated with liver steatosis in every multivariate linear regression model (model 1: β = 3.3513; 95% confidence interval [CI], 2.7722-3.9304; model 2: β = 3.8492; 95% CI, 3.2441-4.4542; model 3: β = 2.4987; 95% CI, 1.8371-3.1604), and this positive association was consistent in both men and women and among different race groups (Mexican American, other Hispanic, non-Hispanic White, Black, Asian, and other race). On the other hand, MUAC was positively associated with liver fibrosis in every multivariate linear regression model, and this positive association also was consistent in both men and women and among non-Hispanic White and Black populations. Increased MUAC was positively associated with liver steatosis and fibrosis in patients with MAFLD. This was particularly true for MUAC ≥ 42.0 cm. MUAC might be a simple and convenient evaluation tool for MAFLD.

Large mid-upper arm circumference is associated with reduced insulin resistance independent of BMI and waist circumference: A cross-sectional study in the Chinese population

BACKGROUND

Body mass index (BMI) is a common indicator in clinical practice, but it is not sufficient to predict insulin resistance (IR). Other anthropometric methods supplement BMI in the assessment of body composition, which can be predicted more accurately. This cross-sectional study aimed to evaluate the association between mid-upper arm circumference (MUAC), triceps skinfold (TSF) thickness, mid-arm muscle circumference (MAMC) and IR in Chinese adults.

METHODS

This cross-sectional study analyzed data from the 2009 China Health and Nutrition Survey database. The study population was divided into four groups according to the MUAC quartiles, and the homeostasis mode assessment was used to evaluate the degree of IR. Logistic regression analysis was performed to calculate odds ratios (ORs) with 95% confidence intervals (CIs), with adjustments for multiple covariates. Subgroup analyses stratified by age, sex, BMI, waist circumference (WC), smoking status, and alcohol consumption were performed.

RESULTS

In total, 8,070 participants were included in the analysis. As MUAC increased, BMI, TSF thickness, MAMC, and the proportion of IR tended to increase. However, we found that there was a significant negative association between MUAC and MAMC and IR in the logistic regression analysis, independent of BMI and WC, the ORs for the highest quartiles compared with the lowest quartiles were 0.662 (95%CI: 0.540-0.811) and 0.723 (95%CI: 0.609-0.860), respectively. There was no significant association was observed between the TSF thickness and IR (OR=1.035 [95%CI: 0.870-1.231]). The inverse associations were more pronounced among participants with lower BMI and WC. No significant age-specific differences were observed (P-heterogeneity > 0.05).

CONCLUSIONS

After adjusting for BMI and WC, MUAC was negatively associated with IR in Chinese adults, and the association between MUAC and IR was derived from arm muscle instead of subcutaneous fat. MUAC could be an additional predictor of IR besides BMI and WC in clinical practice.

Relationship between arm-to-leg and limbs-to-trunk body composition ratio and cardiovascular disease risk factors

We aimed to analyze the relationship of the distribution of body fat mass (FM) and fat-free mass (FFM) in the limbs and trunk with the prevalence of cardiovascular disease risk factors (CVD-RF). In total, 13,032 adults were selected from the KNHANES (2008–2011). The prevalence of hypertension, diabetes mellitus (DM), dyslipidemia, and metabolic syndrome (MetS) according to the arm-to-leg ratio and limbs-to-trunk ratio for FM and FFM was compared, respectively. The higher the arm-to-leg FM ratio, the higher the prevalence of CVD-RF (DM-male-OR 7.04, 95% CI 4.22–11.74; DM-female-OR 10.57, 95% CI 5.80–19.26; MetS-male-OR 4.47, 95% CI 3.41- 5.86; MetS-female-OR 8.73, 95% CI 6.38–11.95). The higher the limbs-to-trunk FM ratio (DM-male-OR 0.12, 95% CI 0.07–0.21; DM-female-OR 0.12, 95% CI 0.06–0.23; MetS-male-OR 0.06, 95% CI 0.04–0.08; MetS-female-OR 0.02, 95% CI 0.01–0.04), the higher the limbs-to-trunk FFM ratio (DM-male-OR 0.19, 95% CI 0.11–0.31; DM-female-OR 0.46, 95% CI 0.30–0.70; MetS-male-OR 0.39, 95% CI 0.31–0.50; MetS-female-OR 0.62, 95% CI 0.50–0.78), and the higher the arm-to-leg FFM ratio (MetS-male-OR 0.75, 95% CI 0.59–0.94; MetS-female-OR 0.73, 95% CI 0.58–0.92), the lower the prevalence of CVD-RF. The higher the FM of the legs compared to the arms, FFM of the arms compared to the legs, and FM or FFM of the limbs compared to the trunk, the lower the prevalence of CVD-RF.

Large mid-upper arm circumference is associated with metabolic syndrome in middle-aged and elderly individuals: a community-based study

BACKGROUND

The mid-upper arm circumference (MUAC) is a proxy for subcutaneous fat in the upper body and is a reliable screening measure for identifying individuals with abnormal regional fat distribution. The purpose of this study was to evaluate the association between MUAC and metabolic syndrome (MetS) in middle-aged and elderly individuals.

METHODS

We measured the MUAC in a cross-sectional sample with a total of 9787 subjects aged 40 years and older. The measurement of MUAC is performed on the right arm using a non-elastic tape held midway between the acromion and the olecranon processes in duplicate, with the arm hanging loosely at the side of the body. The MetS was defined according to the Joint Statement of the International Diabetes Federation Task Force on Epidemiology and Prevention.

RESULTS

MUAC was positively correlated with waist circumference (r = 0.437, P < 0.001), BMI (r = 0.334, P < 0.001), fasting insulin (r = 0.348, P < 0.001), HOMA-IR (r = 0.134, P < 0.001), triglycerides (r = 0.138, P < 0.001), SBP (r = 0.124, P < 0.001), and DBP (r = 0.123, P < 0.001), and inversely correlated with adiponectin (r = - 0.147, P < 0.001) and HDL-cholesterol (r = - 0.176, P < 0.001) after adjusting for age and gender. Compared with the lowest quartile group, the odds ratios were substantially higher for MetS (OR 1.77; 95% CI 1.51-2.09, P for trend< 0.001) in the highest MUAC quartile group after adjustment for potential cofounder.

CONCLUSION

Large mid-upper arm circumference is significantly associated with metabolic syndrome in middle-aged and elderly individuals.

Mid-upper arm circumference as a simple tool for identifying central obesity and insulin resistance in type 2 diabetes

BACKGROUND

Our research aimed to explore the correlation between mid-upper arm circumference (MUAC) and central obesity and insulin resistance (IR) in Chinese subjects with type 2 diabetes.

MATERIALS

A total of 103 participants (60 men) were recruited in our study. MUAC was measured around the mid-arm between the shoulder and elbow. Waist circumference (WC) was obtained as central obesity parameter, and the IR parameter of Homeostasis Model Assessment-Insulin Resistance (HOMA-IR) was calculated. The subjects were divided into three groups according to the tertiles cut-points of MUAC level.

RESULTS

Body mass index (BMI), WC, the percentages of central obesity and HOMA-IR were significantly higher in the groups with higher MUAC than those in the group with lower MUAC (all P < 0.05). Pearson analysis showed that MUAC was correlated with BMI, WC, waist-to-hip ratio (WHR), logHOMA-IR, low density lipoprotein cholesterol (LDL-C), uric acid (UA) and high density lipoprotein cholesterol (HDL-C) in all subjects. Multivariate linear regression analysis revealed that MUAC was independently associated with logHOMA-IR (β = 0.036, P<0.001) after adjusting for age, gender, WHR, UA, TG, LDL-C and HDL-C. Binary logistic regression analysis revealed that MUAC was an independent predictor of central obesity (OR: 2.129, 95%CI: 1.311-3.457, P = 0.002). Furthermore, MUAC≥30.9cm for male and ≥30.0cm for female were the optimal cutoff values for identifying central obesity.

CONCLUSIONS

Our study indicated that among Chinese subjects with type 2 diabetes, MUAC is a simple and effective tool for the determination of central obesity and IR. Additionally, the larger MUAC is proved to be more associated with metabolic risk factors of higher UA and LDL-C and lowever HDL-C.