Neck circumference in relation to glycemic parameters: a systematic review and meta-analysis of observational studies
Diabetology & Metabolic Syndrome volume 11, Article number: 50 (2019)
Recent studies have suggested that neck circumference (NC) is a supplemental screening measure for diagnosing metabolic complications and might be associated with glycemic parameters. The aim of the present study was to to evaluate the association between NC and glycemic parameters.
We systematically searched the electronic databases (including MEDLINE, Scopus, EMBASE, and Google scholar) up to April 2018. Observational studies that reported correlation coefficient between NC and glycemic parameters were included in the analysis. A random effects model was used to estimate overall Fisher’s Z and 95% confidence interval of glycemic parameters including fasting plasma glucose (FBG), serum fasting insulin level, homeostasis model assessment-estimated insulin resistance (HOMA-IR) and glycated hemoglobin (HbA1c).
A total of 21 studies (44,031 participants) were eligible for including in the systematic review and meta-analysis. Significant correlations were found between NC and FBG (Fisher’s Z = 0.18; 95% CI 0.16, 0.21), serum fasting insulin level (Fisher’s Z = 0.34; 95% CI 0.26, 0.41), HOMA-IR (Fisher’s Z = 0.36; 95% CI 0.29, 0.43) and HbA1c (Fisher’s Z = 0.14; 95% CI 0.09, 0.20). Meta-regression analysis showed that NC were marginally associated with FBG in a linear manner (β = 0.008, P = 0.09); but not related to serum fasting insulin level, HOMA-IR, and HbA1c.
This meta-analysis of cross-sectional studies showed that NC was positively correlated with glycemic parameters including FBG, serum fasting insulin level, HOMA-IR, and HbA1c. Further investigations with prospective design are required to confirm these findings.
Diabetes mellitus is a complex metabolic disease characterized by high serum glucose concentration and insulin resistance in target tissues and/or defects in insulin secretion . It may lead to chronic complications such as nephropathy, retinopathy and neuropathy . During the past decades, global prevalence of diabetes continues to rise in parallel with the rates of obesity . Early detection of diabetes by appropriate screening methods may help to delay the vascular complications, especially in individuals who are at high risk for diabetes .
Obesity may lead to insulin resistance and development of type 2 diabetes . Upper-body subcutaneous adipose tissue may confer additional risk for metabolic disorders beyond overall and abdominal obesity . Neck circumference (NC) is a surrogate marker of upper-body subcutaneous fat distribution and closely correlated with various metabolic risk factors . The associations between NC and components of the insulin resistance and metabolic syndrome have been investigated . Therefore, NC may play a remarkable role in prediction of type 2 diabetes .
In the past decades, accumulating evidence showed that NC was independently associated with glycemic parameters, including fasting blood glucose (FBG), insulin levels, insulin resistance, and glycated hemoglobin (HbA1c). However, the results were inconsistent. In the Framingham Heart Study, Lee et al. found that NC was positively associated with fasting plasma glucose . Likewise, NC was also related to glycemic parameters in a Chines elderly population , Japanese postmenopausal women , Chinese adults  and other populations . However, some studies reported that NC was not significantly associated with fasting plasma glucose, insulin or insulin resistance [13,14,15]. So, it is not clear whether measurement of NC is a better predictor of type 2 diabetes compared with traditional adiposity measure. A recently published meta-analysis has evaluated the association between NC and risk of metabolic syndrome . Although a positive association between NC and FBG—as a component of metabolic syndrome—was reported in this meta-analysis, several relevant studies have been missed in the search process [11, 12, 17]. Also, there was no summarizing report for the association between NC and other glycemic parameters. Therefore, we aimed to conduct a systematic review and meta-analysis to evaluate the correlation between NC and glycemic parameters including FBG, serum fasting insulin, homeostasis model assessment-estimated insulin resistance (HOMA-IR) and HbA1c.
We adhered to the meta-analysis of observational studies in epidemiology (MOOSE) guidelines in this systematic review and meta-analysis . A comprehensive systematic literature search using the MEDLINE (Pubmed) (https://www.ncbi.nlm.nih.gov/pubmed), SCOPUS (https://www.scopus.com), EMBASE (https://www.elsevier.com) and Google scholar (https://scholar.google.com) databases was conducted up to April 2018 covering all published research providing evidence on the association between NC measurement and glycemic indices, including fasting plasma glucose, insulin levels, insulin resistance (or HOMA-IR) and HbA1c. Following terms were used: (“Neck Circumference”[Title/Abstract] AND (“Blood Glucose”[MeSh] OR “Blood Glucose”[Title/Abstract] OR “Fasting Plasma Glucose”[Title/Abstract] OR FBG[Title/Abstract] OR Insulin [MeSh] OR “Insulin Resistance”[MeSh] OR “Insulin Resistance”[Title/Abstract] OR “Insulin Levels”[Title/Abstract] OR HOMA-IR[Title/Abstract] OR “Glycated Hemoglobin A”[MESH] OR “Glycated Hemoglobin A”[Title/Abstract] OR HbA1C[Title/Abstract] OR “Diabetes Mellitus”[MeSh] OR “Diabetes Mellitus”[Title/Abstract] OR “Metabolic Syndrome”[MESH] OR “Metabolic Syndrome”[Title/Abstract]). After removing duplicates, two investigators (S.M. and P.S) independently conducted title and abstract screening and identified potentially relevant articles for the full-text review. No time or language restrictions were applied. In addition, a manual review of reference list of retrieved articles was carried out to identify additional relevant studies. Efforts were made to obtain additional data by contacting the authors.
Studies were included in the meta-analysis if they:  were conducted on adult (> 18 years) participants;  had a cohort or a cross-sectional design;  used NC as the exposure; and  reported Pearson or Spearman correlation coefficients between NC and FBG, insulin levels, HOMA-IR or HbA1C. If a same dataset had been analyzed in more than one publication, only the paper with the largest number of participants was included.
We did not include gray literature including thesis, conference abstracts, qualitative, case-report and review studies. Researches on children and adolescents as well as studies that did not report correlation coefficient were also excluded. The flow diagram of study selection process is indicated in Fig. 1. Among eligible studies, Ben-Noun et al. had 2 reports in 2003 and 2006 from a same population [19, 20]. Therefore, study which enrolled larger sample size was included in the current analysis. Although the study population of 2 reports by Liang in 2014 and 2015 were also same [11, 21], values for correlation coefficient were not provided in 2014. Therefore, this report was excluded. Furthermore, 2 studies have been conducted by Aoi et al. in 2014 and 2016. In 2016, Aoi et al. reported the follow up results of the study that conducted in 2014 after 3 years [10, 22]. The baseline values for HbA1c and HOMA-IR reported by Aoi et al. in 2014 were included in the current analysis. Two reports from Framingham Heart Study cohort by Lee et al. and Preis et al. had a same population [8, 13]. In this case, the study by Lee et al. that had larger sample size was included in the present analysis.
Following data were extracted from each study: the first author’s last name, publication year; study population, study name, location, gender, number of participants, age of participants, race or ethnicity, mean neck circumference and its standard deviation of participants, sampling method, statistical test used, assessment of outcomes, most fully adjusted Pearson or Spearman correlation coefficient between NC and each outcome and statistical adjustment for the potential confounding factors. Study selection and data extraction were conducted independently by two investigators (P.S. and S.M.).
We assessed study quality using the Newcastle–Ottawa quality assessment scale (adapted for cross sectional studies) . This system allowed a total score of up to 10 points as the highest quality. Scores were derived through three aspects of each study including five scores for selection (representativeness of the sample, sample size, non-respondents and ascertainment of the exposure), two scores for comparability (considering confounding factors in study design or analysis) and three scores for outcome (assessment of the outcome and statistical test) in seven questions. Studies with scores above the median were classified as the high quality studies.
To perform the meta-analysis, we used correlation coefficients (reported for the relationship between NC and outcomes of interest) and sample sizes to calculate Fisher’s Z and its Standard Error of mean (SEM). Overall effect was derived from the method of DerSimonian and Laird  by using random effects model, which takes between-study variation into account. To find possible sources of heterogeneity, we conducted meta-regression based on mean NC as an effect modifier. Subgroup analysis based on gender, study location, sampling method, health status of participants, type of correlation coefficient and making adjustment was also done to find possible sources of heterogeneity. Between-subgroup heterogeneity was assessed by a fixed effect model. Statistical heterogeneity between studies was evaluated with Cochran’s Q test and I square (I2). Sensitivity analysis was used to explore the extent to which inferences might depend on a particular study. Publication bias was evaluated by Begg’s funnel plots. Formal statistical assessment of funnel plot asymmetry was done by Egger’s regression asymmetry test and Begg’s adjusted rank correlation test. Statistical analyses were conducted by using Stata version 11.2 (Stata Corp, College Station, TX). P values less than 0.05 were considered statistically significant.
Of 2345 articles identified by the initial search, 21 studies were eligible for including in the current systematic review and meta-analysis. Table 1 summarized the characteristics and quality score of included studies. Papers were published between 2002 and 2017. All studies had a cross-sectional design except for one case–control study. The total number of participants in these studies was 44,031 (19,710 males and 24,321 females) aged from 18 to 65 years. Mean NC were ranged from 31 to 44 cm. Six studies were conducted in the United States [12, 13, 17, 25,26,27], 10 in Asian countries [9, 11, 14, 22, 28,29,30,31,32,33], 2 in Middle-eastern countries [5, 19], 2 in Latin American societies [15, 34], and 1 in a European country . Two investigations were conducted on women; one in men and others in both genders. The participants of 17 studies were healthy population and 3 investigations enrolled overweight or obese, severely obese individuals or clinically patients. One case–control study was conducted on both healthy and human immunodeficiency virus infected populations. Regarding sampling method, 4 studies used a consecutive method, 10 studies used random sampling techniques and 7 studies used a non-random method. In 10 studies, data were reported as age-adjusted. Two studies made further adjustments for gender, smoking status, physical activity, disease status and sex; while nine investigations did not make any adjustment for potential confounders. In case of quality of studies, the score quality of 7 studies was 8 and 12 studies were 9. The quality of 2 remained studies was a maximum of 10.
Four outcomes including FBG, serum fasting insulin level, HOMA-IR and HbA1c were examined in the eligible studies. The association between NC, FBG, serum fasting insulin level, HOMA-IR, and HbA1c were evaluated in 20, 8, 9 and 9 studies, respectively. The range of correlation coefficient for FBG was between 0.025 and 0.48; for serum fasting insulin level was between 0.09 and 0.50; for HOMA-IR was between 0.12 and 0.51, and for HbA1c was from 0.01 to 0.3. The associations between NC and glycemic parameters in 18 studies were evaluated by Pearson correlation and in 3 other studies by Spearman correlation coefficient.
Meta-analysis of the correlation coefficient between NC and FBG
Thirty-five effect sizes on correlation between NC and FBG were derived from 20 studies (Fig. 2). Pooled results from included studies showed a positive correlation between NC and FBG (overall Fisher’s Z = 0.18; 95% CI 0.16–0.21). Heterogeneity was significant among included studies (I2 = 86.7%, P < 0.001). To find the source of heterogeneity, the subgroup analysis based on gender (Fig. 2), race, adjustments, correlation type, health status, and sampling method was conducted (Table 2). Heterogeneity was not completely eliminated in all subgroups; however, there was no heterogeneity between studies in the several subgroups. As shown in Fig. 3a, meta-regression of the studies indicated that NC (as a continuous variable) was marginally associated with FBG values in a dose–response manner (β = 0.008, P = 0.09). Sensitivity analysis showed that the overall estimate was not changed after recalculation of the overall effect size by sequentially elimination of each study at a time. There was no evidence of substantial publication bias (Begg’s test = 0.51 and Egger’s test = 0.63).
Meta-analysis of the correlation coefficient between NC and serum fasting insulin levels
A combination of 15 effect sizes from 8 studies revealed that NC was positively correlated with serum fasting insulin level (overall Fisher’s Z = 0.34; 95% CI 0.26–0.41) (Fig. 4). Because of the significant heterogeneity between studies (I2 = 95.7%, P < 0.001), we performed subgroup analysis based on gender (Fig. 4), race, adjustments, correlation type, health status, and sampling method to investigate its sources (Table 2). Although subgroup analysis could not detect potential sources of observed heterogeneity, the between-studies heterogeneity was eliminated in Asian and European sub-groups and non-random groups. Meta-regression analysis showed that increment in NC values was not significantly associated with serum fasting insulin level in a linear manner (β = 0.012, P = 0.12) (Fig. 3b). Sensitivity analysis revealed that the pooled estimate did not substantially change with the omission of the studies one at a time. No significant evidence of publication bias was found by Begg’s test (P = 0.73) and Egger’s test (P = 0.44).
Meta-analysis of the correlation coefficient between NC and HOMA-IR
Pooling 16 effect sizes from 9 studies, NC was significantly correlated with HOMA-IR (overall Fisher’s Z = 0.36; 95% CI 0.29–0.43) (Fig. 5). A significant heterogeneity was evident between studies (I2 = 95.8%, P < 0.001). The heterogeneity was not completely eliminated by subgroup analysis according to gender (Fig. 5), race, adjustments, correlation type, health status, and sampling method (Table 2). Sub-group analysis by race showed that the heterogeneity was eliminated in all sub-groups except Latin America and Asian populations. In addition, heterogeneity was not observed in non-random and spearman sub-groups. The results of meta-regression illustrated that there was a non-significant linear trend between NC measurements (cm) and HOMA-IR (β = 0.001, P = 0.83) (Fig. 3c). The sensitivity analysis was performed and the pooled estimate did not significantly change after exclusion of each study at a time. Results of Egger’s test (P = 0.382) and Begg’s test (P = 0.528) indicated no evidence of publication bias.
Meta-analysis of the correlation coefficient between NC and HbA1c
The significant positive correlation between NC and HbA1c was suggested by pooled estimate of 15 effect sizes from 9 studies (overall Fisher’s Z = 0.14; 95% CI 0.09–0.20) (Fig. 6). Between-studies heterogeneity was significant (I2 = 87.7%, P < 0.001); thus, the subgroup analysis based on gender (Fig. 6), and other confounders was performed (Table 2). The heterogeneity was not completely eliminated by these stratified analyses, although the heterogeneity was removed in some sub-groups. Meta-regression analysis of studies showed that there was no significant linear association between NC values (cm) and HbA1c (β = 0.007, P = 0.11) (Fig. 3d). Results of sensitivity analysis revealed that overall estimate was not affected by elimination of each study. In addition, no evidence of publication bias was seen (P = 0.22 by Begg’s test).
This meta-analysis of observational studies showed that NC was positively correlated with FBG, serum fasting insulin level, HOMA-IR, and HbA1c. The findings were not varied by gender, race, adjustments, correlation type, health status, and sampling method. Furthermore, meta-regression analysis showed that NC were marginally associated with FBG in a linear manner. These findings suggested that NC, as a simple and appropriate tool, could be used in clinical screening of glycemic parameters and prediction of type 2 diabetes. To our knowledge, the current study was comprehensively reviewed the correlation between NC and glycemic parameters for the first time.
Recently, NC was considered as a useful tool for measurement of overweight and obesity. Hingorjo et al. reported that NC is good predictor of overweight and obesity and suggested that the cut-off point of NC for overweight and obesity in male and female is ≥ 35.5 cm and ≥ 32 cm, respectively . In addition, other studies reported that there are significant positive correlations between NC and weight, BMI, waist circumference, waist to hip ratio as well as metabolic syndrome in different populations [37, 38].
Several studies suggested that NC might have a role in prevalence of chronic disease including cardiovascular diseases, metabolic syndrome, and diabetes. Increasing in NC might result in dyslipidemia and elevated risk of cardiovascular diseases . Also, a number of studies reported that large NC values might increase risk of inflammation and cardio-vascular disease [40, 41]. A recent meta-analysis found no significant association between NC and metabolic syndrome; however, the mentioned study reported significant positive associations between NC and the components of metabolic syndrome . Several eligible studies [11, 12, 17] have been missed by the search in this investigation; so, the results might be distorted by defective search strategy. Another meta-analysis in 2018 assessed the relationship between NC and cardio-metabolic risk factors and reported positive and significant correlations between NC and with two glycemic indices (FBS and HOMA-IR). This analysis was included only 4 studies for FBS and 3 investigations for HOMA-IR in adult population . Several eligible studies were missed in the mentioned meta-analysis and the results might be distorted by defective search strategy. We tried to consider all published data in this field and provided more accurate information in the present study.
The association between NC and glucose intolerance was evaluated in some previous studies. Laakso et al. study reported that the risk of glucose intolerance and hyperinsulinemia was higher in the highest quintile of NC compared with the lowest one . Another study compared the correlation of different anthropometric measurements including BMI, waist, hip and neck circumferences with visceral adiposity and HOMA-IR. This study suggested that NC outstripped other anthropometric measurements in prediction of insulin resistance as well as visceral adipose tissue . These findings were in line with current meta-analysis. In addition, one study assessed the NC and other anthropometric measurements in diabetic and non-diabetic subjects . Although mean NC in diabetic patients was higher than non-diabetic individuals in this study, the cut-off point of NC in diabetic (> 36 cm) was less than non-diabetic subjects (> 37 cm). This finding revealed that a broad assessment of obesity is needed in diabetic subjects .
NC was considered as an estimation of upper-body subcutaneous adipose tissue that might have a role in prediction of insulin resistance and type-2 diabetes [25, 34, 46]. Excess systematic free fatty acid might be one mechanism to explain the correlation between NC and insulin resistance. The concentration of free fatty acids (FFAs) is affected by NC values. In other words, the lipolytic function and FFAs release rate of upper-body subcutaneous fat is more than lower-body subcutaneous fat [47, 48]. The elevated systemic FFAs had a role in increased very low density lipoprotein production and inhibition of insulin clearance that lead to insulin resistance [49,50,51]. Also, NC was positively correlated with whole body and visceral fat that both were associated with biological parameters of insulin resistance [5, 36, 39]. Two perivascular ectopic fat depots were also found in neck region. Secretion of adipokines, such as leptin, adiponectin, and interleukin 6 from these fat depots might result in metabolic dysfunction including insulin resistance [22, 45, 52, 53]. In addition, subjects with large NC had more risk for obesity [37, 38]. Insulin resistance is one of the important complication of obesity that has an ability to engender hyperglycemia and impaired glycemic parameters [54, 55].
Current meta-analysis has some strengths and limitations. First, contribution of a large number of subjects increases the statistical power. Publication bias was not observed in the analysis and the comprehensive subgroup analyses were conducted based on different potential confounders such as gender, race, adjustments, correlation type, health status, and sampling method. Several limitations of the current study merit discussion. All of the included studies in the meta-analysis had observational design, thus we could not infer a causal association between NC and glycemic parameters. In addition, most of included studies did not make adjustment for the potential confounders, especially dietary intakes. So, the residual confounder might influence the correlation between NC and glycemic parameters. In addition, the direct and quantify measure of depot of fat might not be explained by single measurement of NC, because measure of NC involved both adipose and lean tissue. Finally, the heterogeneity between studies was not completely eliminated after subgroup analysis and meta-regression.
In conclusion, this meta-analysis of observational studies showed that neck circumference was positively correlated with glycemic parameters including FBG, serum fasting insulin level, HOMA-IR, and HbA1c. However, further studies with prospective design are required to confirm these findings.
Availability of data and materials
Data are available on request.
fasting blood glucose
free fatty acids
homeostasis model assessment-estimated insulin resistance
Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006;444(7121):840–6.
Schalkwijk CG, Stehouwer CD. Vascular complications in diabetes mellitus: the role of endothelial dysfunction. Clin Sci (Lond). 2005;109(2):143–59.
Gurka MJ, Filipp SL, DeBoer MD. Geographical variation in the prevalence of obesity, metabolic syndrome, and diabetes among US adults. Nutr Diabetes. 2018;8(1):14.
Yajnik CS. The lifecycle effects of nutrition and body size on adult adiposity, diabetes and cardiovascular disease. Obes Rev. 2002;3(3):217–24.
Onat A, Hergenç G, Yüksel H, Can G, Ayhan E, Kaya Z, et al. Neck circumference as a measure of central obesity: associations with metabolic syndrome and obstructive sleep apnea syndrome beyond waist circumference. Clin Nutr. 2009;28(1):46–51.
Laakso M, Matilainen V, Keinanen-Kiukaanniemi S. Association of neck circumference with insulin resistance-related factors. Int J Obes Relat Metabol Disord. 2002;26(6):873–5.
Volaco A, Martins CM, Soares JQ, Cavalcanti AM, Moyses ST, Filho RP, et al. Neck circumference and its correlation to other anthropometric parameters and finnish diabetes risk score (FINDRISC). Curr Diabetes Rev. 2017;14(5):464–71.
Lee JJ, Pedley A, Therkelsen KE, Hoffmann U, Massaro JM, Levy D, et al. Upper body subcutaneous fat is associated with cardiometabolic risk factors. Am J Med. 2017;130(8):958–66.
Zhong P, Sun D, Wu D, Li T, Wu Y, Xu L. Neck circumference can be used as a valuable tool to screen the cardiovascular risk factors in Chinese elderly: a community based study. Int J Clin Exp Med. 2017;10(2):3685–91.
Aoi S, Miyake T, Iida T, Ikeda H, Ishizaki F, Chikamura C, et al. Association of changes in neck circumference with cardiometabolic risk in postmenopausal healthy women. J Atheroscler Thromb. 2016;23(6):728–36.
Liang J, Wang Y, Dou L, Li H, Liu X, Qiu Q, et al. Neck circumference and prehypertension: the cardiometabolic risk in Chinese study. J Hypertens. 2015;33(2):275.
Joshipura K, Muñoz-Torres F, Vergara J, Palacios C, Pérez CM. Neck circumference may be a better alternative to standard anthropometric measures. J Diabetes Res. 2016;2016:6058916.
Preis SR, Massaro JM, Hoffmann U, D’Agostino RB Sr, Levy D, Robins SJ, et al. Neck circumference as a novel measure of cardiometabolic risk: the Framingham Heart study. J Clin Endocrinol Metabol. 2010;95(8):3701–10.
Wang X, Zhang N, Yu C, Ji Z. Evaluation of neck circumference as a predictor of central obesity and insulin resistance in Chinese adults. Int J Clin Exp Med. 2015;8(10):19107.
Baena CP, Lotufo PA, Fonseca MG, Santos IS, Goulart AC, Benseñor IM. Neck circumference is independently associated with cardiometabolic risk factors: cross-sectional analysis from ELSA-Brasil. Metabol Syndr Relat Disord. 2016;14(3):145–53.
Namazi N, Larijani B, Surkan PJ, Azadbakht L. The association of neck circumference with risk of metabolic syndrome and its components in adults: a systematic review and meta-analysis. Nutr Metabol Cardiovasc Dis. 2018;28(7):657–74.
Pokharel Y, Macedo FY, Nambi V, Martin SS, Nasir K, Wong ND, et al. Neck circumference is not associated with subclinical atherosclerosis in retired National football league players. Clin Cardiol. 2014;37(7):402–7.
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.
Ben-Noun LL, Laor A. Relationship of neck circumference to cardiovascular risk factors. Obesity. 2003;11(2):226–31.
Ben-Noun LL, Laor A. Relationship between changes in neck circumference and cardiovascular risk factors. Exp Clin Cardiol. 2006;11(1):14.
Liang J, Teng F, Liu X, Zou C, Wang Y, Dou L, et al. Synergistic effects of neck circumference and metabolic risk factors on insulin resistance: the Cardiometabolic Risk in Chinese (CRC) study. Diabetol Metabol Syndr. 2014;6(1):116.
Aoi S, Miyake T, Harada T, Ishizaki F, Ikeda H, Nitta Y, et al. Neck circumference has possibility as a predictor for metabolic syndrome in postmenopausal women. Hiroshima J Med Sci. 2014;63(4):27–32.
Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M, Tugwell P. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa: Ottawa Hospital Research Institute; 2009.
DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.
Dixon JB, Obrien PE. Neck circumference a good predictor of raised insulin and free androgen index in obese premenopausal women: changes with weight loss. Clin Endocrinol. 2002;57(6):769–78.
Fitch KV, Stanley TL, Looby SE, Rope AM, Grinspoon SK. Relationship between neck circumference and cardiometabolic parameters in HIV-infected and non-HIV-infected adults. Diabetes Care. 2011;34(4):1026–31.
Torriani M, Gill CM, Daley S, Oliveira AL, Azevedo DC, Bredella MA. Compartmental neck fat accumulation and its relation to cardiovascular risk and metabolic syndrome–. Am J Clin Nutr. 2014;100(5):1244–51.
Zhou J, Ge H, Zhu M, Wang L, Chen L, Tan Y, et al. Neck circumference as an independent predictive contributor to cardio-metabolic syndrome. Cardiovasc Diabetol. 2013;12(1):76.
Kumar NV, Ismail MH, Mahesha P, Girish M, Tripathy M. Neck circumference and cardio-metabolic syndrome. JCDR. 2014;8(7):23.
Yan Q, Sun D, Li X, Zheng Q, Li L, Gu C, et al. Neck circumference is a valuable tool for identifying metabolic syndrome and obesity in Chinese elder subjects: a community-based study. Diabetes Metabol Res Rev. 2014;30(1):69–76.
Li Q, Wang N, Han B, Chen Y, Zhu C, Chen Y, et al. Neck circumference as an independent indicator to non-alcoholic fatty liver disease in non-obese men. Nutr Metabol. 2015;12(1):63.
Selvan C, Dutta D, Thukral A, Nargis T, Kumar M, Mukhopadhyay S, et al. Neck height ratio is an important predictor of metabolic syndrome among Asian Indians. Indian J Endocrinol Metabol. 2016;20(6):831.
Jiang J, Cui J, Yang X, Wang A, Mu Y, Dong L, et al. Neck circumference, a novel indicator for hyperuricemia. Front Physiol. 2017;8:965.
Stabe C, Vasques ACJ, Lima MMO, Tambascia MA, Pareja JC, Yamanaka A, et al. Neck circumference as a simple tool for identifying the metabolic syndrome and insulin resistance: results from the Brazilian Metabolic Syndrome Study. Clin Endocrinol. 2013;78(6):874–81.
Assyov Y, Gateva A, Tsakova A, Kamenov Z. A comparison of the clinical usefulness of neck circumference and waist circumference in individuals with severe obesity. Endocr Res. 2017;42(1):6–14.
Hingorjo MR, Qureshi MA, Mehdi A. Neck circumference as a useful marker of obesity: a comparison with body mass index and waist circumference. JPMA. 2012;62(1):36.
Yang G, Yuan S, Fu H, Wan G, Zhu L, Bu X, et al. Neck circumference positively related with central obesity, overweight, and metabolic syndrome in Chinese subjects with type 2 diabetes: beijing Community Diabetes Study 4. Diabetes Care. 2010;33(11):2465–7.
Ben-Noun L, Sohar E, Laor A. Neck circumference as a simple screening measure for identifying overweight and obese patients. Obes Res. 2001;9(8):470–7.
Vallianou NG, Evangelopoulos AA, Bountziouka V, Vogiatzakis ED, Bonou MS, Barbetseas J, et al. Neck circumference is correlated with triglycerides and inversely related with HDL cholesterol beyond BMI and waist circumference. Diabetes Metabol Res Rev. 2013;29(1):90–7.
Medeiros CAM, Bruin VMS, Castro-Silva C, Araújo SMHA, Chaves CM, Bruin PFC. Neck circumference, a bedside clinical feature related to mortality of acute ischemic stroke. Revista da Associação Médica Brasileira. 2011;57:559–64.
Jamar G, Pisani LP, Oyama LM, Belote C, Masquio DC, Furuya VA, et al. Is the neck circumference an emergent predictor for inflammatory status in obese adults? Int J Clin Pract. 2013;67(3):217–24.
Ataie-Jafari A, Namazi N, Djalalinia S, Chaghamirzayi P, Abdar ME, Zadehe SS, et al. Neck circumference and its association with cardiometabolic risk factors: a systematic review and meta-analysis. Diabetol Metab Syndr. 2018;10:72.
Laakso M, Matilainen V, Keinänen-Kiukaanniemi S. Association of neck circumference with insulin resistance-related factors. Int J Obes. 2002;26(6):873.
Yang L, Samarasinghe Y, Kane P, Amiel S, Aylwin S. Visceral adiposity is closely correlated with neck circumference and represents a significant indicator of insulin resistance in WHO grade III obesity. Clin Endocrinol. 2010;73(2):197–200.
Aswathappa J, Garg S, Kutty K, Shankar V. Neck circumference as an anthropometric measure of obesity in diabetics. N Am J Med Sci. 2013;5(1):28–31.
Grunfeld C, Rimland D, Gibert CL, Powderly WG, Sidney S, Shlipak MG, et al. Association of upper trunk and visceral adipose tissue volume with insulin resistance in control and HIV-infected subjects in the FRAM study. J Acquir Immune Defic Syndr. 2007;46(3):283.
Jensen MD. Gender differences in regional fatty acid metabolism before and after meal ingestion. J Clin Investig. 1995;96(5):2297–303.
Nielsen S, Guo Z, Johnson CM, Hensrud DD, Jensen MD. Splanchnic lipolysis in human obesity. J Clin Investig. 2004;113(11):1582–8.
Patti M-E, Kahn CR. Lessons from transgenic and knockout animals about noninsulin-dependent diabetes mellitus. Trends Endocrinol Metab. 1996;7(9):311–9.
Kelley DE, Mokan M, Simoneau J-A, Mandarino LJ. Interaction between glucose and free fatty acid metabolism in human skeletal muscle. J Clin Investig. 1993;92(1):91–8.
Kissebah A, Alfarsi S, Adams P, Wynn V. Role of insulin resistance in adipose tissue and liver in the pathogenesis of endogenous hypertriglyceridaemia in man. Diabetologia. 1976;12(6):563–71.
Iida T, Domoto T, Takigawa A, Nakamura S, Kato Y, Togo M, et al. Relationships among blood leptin and adiponectin levels, fat mass, and bone mineral density in Japanese pre-and postmenopausal women. Hiroshima J Med Sci. 2011;60(4):71–8.
Medeiros CAM, Bruin VMS, Castro-Silva C, Araújo SMHA, Chaves CM, Bruin PFC. Neck circumference, a bedside clinical feature related to mortality of acute ischemic stroke. Rev Assoc Med Bras. 2011;57(5):559–64.
Kahn BB, Flier JS. Obesity and insulin resistance. J Clin Investig. 2000;106(4):473–81.
Kahn SE, Hull RL, Utzschneider KM. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006;444:840.
Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran supported present study.
Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran supported present study
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Saneei, P., Shahdadian, F., Moradi, S. et al. Neck circumference in relation to glycemic parameters: a systematic review and meta-analysis of observational studies. Diabetol Metab Syndr 11, 50 (2019). https://doi.org/10.1186/s13098-019-0445-7
- Neck circumference
- Fasting plasma glucose
- Insulin levels
- Insulin resistance
- Glycated hemoglobin