Neck circumference and its association with cardiometabolic risk factors: a systematic review and meta-analysis

Ataie‐Jafari, Asal; Namazi, Nazli; Djalalinia, Shirin; Chaghamirzayi, Pouria; Abdar, Mohammad Esmaeili; Zadehe, Sara Sarrafi; Asayesh, Hamid; Zarei, Maryam; Gorabi, Armita Mahdavi; Mansourian, Morteza; Qorbani, Mostafa

doi:10.1186/s13098-018-0373-y

Cited by 37 publications

(36 citation statements)

References 46 publications

(70 reference statements)

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“…However, we agree with Caro et al, who suggested that NC measurement may be an opportunity in clinical practice when it is difficult to measure WC [36]. Interestingly, in this study, NC was the only anthropometric parameter to show a significant correlation with total LDL-cholesterol, and this result is in line with a recent systematic review and meta-analysis [37]. On the basis of all our findings, NC, combined with BMI and WC or WHtR, should be used in clinical practice to quantify the cardio-metabolic risk in individuals with excess body weight.…”

Section: Plos Onesupporting

confidence: 93%

Cross-sectional relationship among different anthropometric parameters and cardio-metabolic risk factors in a cohort of patients with overweight or obesity

et al. 2020

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Background Body fat distribution influences the risk of cardio-metabolic disease in people with overweight. This study was aimed at identifying the anthropometric parameters more strongly associated with the majority of cardio-metabolic risk factors. Methods This study included 1214 subjects (840 women), with a body-mass-index (BMI) ≥ 25 Kg/m2, aged 39.2 ± 13 years. Fasting blood glucose (FBG), triglycerides (TG), total, HDL- and LDL-cholesterol, uric acid, vitamin D, high-sensitive C-reactive protein (hs-CRP), white blood cells (WBC), platelets, insulin and insulin resistance (HOMA-IR), systolic (SBP) and diastolic blood pressure (DBP), smoking habit and snoring were evaluated as cardio-metabolic risk factors.We also included the Systematic COronary Risk Evaluation (SCORE) to estimate cardiovascular risk in our study population. BMI, waist circumference (WC), waist-to-height-ratio (WHtR) and neck circumference (NC) were evaluated as anthropometric parameters. Results All four anthropometric parameters were positively associated to SBP, DBP, TG, FBG, insulin, HOMA-IR, WBC, and snoring (p<0.001), and negatively associated with HDL-cholesterol (p<0.001). NC showed a positive association with LDL-cholesterol (β = 0.76; p = 0.01; 95% C.I. 0.19 to 1.32), while vitamin D was negatively associated to WC (β = -0.16; p<0.001; 95% C.I. -0.24 to -0.09), BMI (β = 0.42); p<0.001; 95% C.I. -0.56 to -0.28) and WHtR (β = -24.46; p<0.001; 95% C.I. -37 to -11.9). Hs-CRP was positively correlated with WC (β = 0.003; p = 0.003; 95% C.I. 0.001 to 0.006), BMI (β = 0.01; p = 0.02; 95% C.I. 0.001 to 0.012) and WHtR (β = 0.55; p = 0.01; 95% C.I. 0.14 to 0.96). SCORE was associated to NC (β = 0.15; 95% CI 0.12 to 0.18; p<0.001), BMI (β = -0.18; 95% CI -0.22 to 0.14; p<0.001) and WHtR (β = 7.56; 95% CI 5.30 to 9.82; p<0.001). Conclusions NC, combined with BMI and WC or WHtR could represent an essential tool for use in clinical practice to define the cardio-metabolic risk in individuals with excess body weight.

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Section: Plos Onesupporting

confidence: 93%

Cross-sectional relationship among different anthropometric parameters and cardio-metabolic risk factors in a cohort of patients with overweight or obesity

et al. 2020

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“…Neck circumference has not showed a significant difference between older adults who had MetS in comparison with those who did not have MetS. Ataie-Jafari in a systematic review and meta-analysis demonstrated a significant association between NC and other cardiometabolic risk factors; and pointed to a research in which an odds ratio of 11.53 and 7.69 for high NC in prediction of MetS presence in elderly men and women has been reported [21]. Baena et al study on people aged 35-74 years concluded that NC was significantly and independently associated with adverse cardiometabolic risk factors in adult men and women and also represented optimal values for identifying those individuals who might have a combination of two or three of the cardiometabolic risk factors including insulin resistance, low HDL, elevated blood pressure, and high triglycerides [22].…”

Section: Discussionmentioning

confidence: 95%

“…Neck circumference has been reported to be more accurate than BMI, because a significant correlation exists between neck circumference and abdominal obesity. Several studies have shown that in comparison with BMI and waist circumference, neck circumference may be independently associated with metabolic risk factors [21,22]. Conicity Index (CI) and Abdominal Volume Index (AVI) are also new indicators of abdominal obesity and seem to be good predictors for metabolic disorders [23].…”

Section: Introductionmentioning

confidence: 99%

Comparison of anthropometric indices for predicting the risk of metabolic syndrome in older adults

Khosravian

Bayani

Hosseini

et al. 2021

Romanian Journal of Internal Medicine

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Background: The prevalence of obesity and metabolic syndrome (MetS) are increasing, worldwide. Using a simple, efficient and reliable tool for predicting MetS is an essential approach in preventive health programs. The aim of this study was to compare the different anthropometric indices in predicting metabolic syndrome in older adults.Methods: This cross-sectional study is a part of the Amirkola Health and Ageing cohort Project (2011-2016). Of total, 1488 older people aged 60-92 years were entered to the study. Medical and personal information of participants were collected by a questionnaire. After measuring the height, weight, waist circumference, hip circumference and neck circumference, body mass index, waist to hip ratio, waist to height ratio, abdominal volume index and conicity index were calculated. Independent t-test, chi-square and ROC curve were used to analyze the data.Results: Based on ATPIII-2005 diagnostic criteria, the prevalence of metabolic syndrome was 71.57%.The prevalence in female was higher than male. All of examined anthropometric indices, except neck circumference (p=0.10), showed a significant difference in people with MetS compared to the individuals without metabolic syndrome (p<0.001). Waist to height ratio showed the largest area under the curve for predicting MetS (0.786; 95% CI: 0.76-0.81) followed by BMI (0.746; 95% CI: 0.71-0.77), AVI (0.745; 95% CI: 0.71-0.77), and waist circumference (0.743; 95% CI: 0.71-0.77).Conclusion: Waist to height ratio was the best predictor of MetS in older adults.

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“…The relationships between WHtR, BMI, WC and WHR have been previously reported by various studies. [6,[8][9][10][11]20] Neck circumference as an indicator of cardiometabolic risk has also been reported, [21][22][23] but it is relatively recent and an emerging topic which should be better explored.…”

Section: Discussionmentioning

confidence: 99%

Cardiometabolic risk and its association with dietary diversity, activity patterns and the nutritional status of workers in tertiary educational institutions in Southwestern Nigeria

Adeomi

Akande

Obiajunwa

et al. 2020

Preprint

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Introduction Waist-to-height ratio (WHtR) is increasingly being reported as a simple, but accurate measure of cardiometabolic risk, even better than BMI, WHR and even waist circumference. Therefore, the objective of this study was to determine the cardiometabolic risk using WHtR, and its association with dietary diversity, activity patterns and the nutritional status of workers in tertiary educational institutions in Southwestern Nigeria. Materials and Methods This was a descriptive cross-sectional study carried out among workers in 3 randomly selected tertiary educational institutions in Osun State, Nigeria who were adults (18 years and above). Only apparently healthy people were recruited for the study. Cardiometabolic risk was assessed using WHtR, and respondents were categorized into low risk (WHtR < 0.5) or high risk (WHtR ≥ 0.5). Bivariate and multivariate (logistic regression model) analyses were used to determine the significant predictors of cardiometabolic risk. Level of significance was set at p-value less than 0.05. Results The mean age of the respondents was 45.8 ± 10.4 years, with a male:female ratio of 1:1.1. The mean WHtR among the respondents was 0.53 ± 0.08, and 36.5% had low cardio-metabolic risk while 63.5% had high cardiometabolic risk. The prevalence of overweight and obesity were 44.0% and 25.8% respectively using the BMI categories. At bivariate level, there were statistically significant associations between WHtR and age (p < 0.001), gender (p = 0.002), educational level (p = 0.046), marital status (p = 0.022), dietary diversity (p = 0.027) and activity patterns (p = 0.030). When these were entered into a binary logistic regression model, age (p = 0.028; CI = 1.003 to 1.060) and gender (p = 0.010; CI = 1.149 to 2.784) remained the only statistically significant explanatory variables for cardiometabolic risk using WHtR. Conclusion The study found a relatively high prevalence of cardiometabolic risk, overweight and obesity among the respondents, with age and gender being the main predictors of cardio-metabolic risk using WHtR. Therefore, there is a need for early screening for cardiometabolic risk using WHtR, especially among the females.

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Neck circumference and its association with cardiometabolic risk factors: a systematic review and meta-analysis

Cited by 37 publications

References 46 publications

Cross-sectional relationship among different anthropometric parameters and cardio-metabolic risk factors in a cohort of patients with overweight or obesity

Cross-sectional relationship among different anthropometric parameters and cardio-metabolic risk factors in a cohort of patients with overweight or obesity

Comparison of anthropometric indices for predicting the risk of metabolic syndrome in older adults

Cardiometabolic risk and its association with dietary diversity, activity patterns and the nutritional status of workers in tertiary educational institutions in Southwestern Nigeria

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