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

Yanhua Zhu; Qiongyan Lin; Yao Zhang; Hongrong Deng; Xiling Hu; Xubin Yang; Bin Yao

doi:10.1371/journal.pone.0231308

. 2020 May 21;15(5):e0231308. doi: 10.1371/journal.pone.0231308

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

Yanhua Zhu ^1,^#, Qiongyan Lin ^2,^#, Yao Zhang ¹, Hongrong Deng ¹, Xiling Hu ¹, Xubin Yang ^1,^*, Bin Yao ^1,^*

Editor: Mauro Lombardo³

PMCID: PMC7241705 PMID: 32437358

Abstract

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.

Introduction

Obesity is an international issue related to many serious diseases like diabetes and cardiovascular diseases that impose a huge burden on both individual and public health [1–3]. Based on international reference standards, the body mass index (BMI) is the most common measurement for the determination of obesity both in clinical practice and research [4,5]. However, more and more studies confirmed that central obesity, also known as visceral obesity, offers more predictive power for type 2 diabetes, cardiovascular risk and metabolic dysfunctions than whole-body adiposity [6–8]. Thus, for the failure to evaluate body fat distribution, BMI is replaced by some other anthropometric parameters to predict central obesity and insulin resistance, such as waist circumference (WC) and waist to hip ratio (WHR) [9–12].

Among various methods, WC is used as the most common anthropometric index of abdominal visceral fat accumulation and insulin resistance (IR), which were the indicators of cardiovascular risks in both men and women [3,13]. However, there remain a number of limitations of WC, such as the absence of a standard approach of measurement; the volatility of measuring results from the influence of dining and diverse health conditions [10]. In addition, while it is well documented that adipose tissue, especially volume of visceral adipose tissue was strongly correlated with cardiovascular diseases, insulin resistance and diabetes mellitus [14], WC alone to predict central obesity seems to be not enough since its failure to distinguish whether it is caused by volume of visceral or abdominal subcutaneous adipose tissue. Therefore, some researches were conducted to explore novel indexes to be more accurate and practical [10,15–17]. Recently, arm-fat percentage or mid-upper arm circumference (MUAC) were suggested as novel predictors of central obesity and IR in population with normal weight, overweight or obesity [18–20]. However, there is little data to evaluate the role of MUAC in detecting IR and central obesity in diabetic patients. A large amount of studies had proved that the IR and central obesity in patients with diabetes were quite different from other populations [21,22]. Type 2 diabetes is mainly characterized by insulin resistance. Most recently, in Groop L’s and Ji L’s suggestions of novel diabetes classification, diabetes was stratified into five types (in Groop L’s study) or four types (in Ji L’s study) [23,24]. In their studies, diabetes complications substantially increased in patients with severe insulin-resistant diabetes, which reinforced the importance of IR and central obesity in diabetic patients. As a result, the association of MUAC and IR and central obesity in diabetes might be quite different from other populations as well. Therefore, precisely identifying the central obesity and IR in closely related to type 2 diabetes is of great importance. As the evaluation of MUAC can be easily obtained in clinical practice, our study aimed to evaluate whether the MUAC can be served as an indicator of central obesity and IR in type 2 diabetes. Besides, we further investigate whether MUAC is superior to other anthropometric parameters in measuring central obesity and IR in subjects with type 2 diabetes.

Materials and methods

Study population

From April 2015 to May 2017, 103 subjects were recruited in our study. The patients were selected from inpatient clinics who met the following criteria: aged above 18 years, diagnosed with type 2 diabetes (according to the WHO diabetes criteria) [25], without insulin treatment, Sulphonylurea, sodium-dependent glucose transporters-2 inhibitors (SGLT-2i) or glucagon-like peptide-1 (GLP-1) analog for at least 2 weeks (hypoglycemic agents in S1 Table), not having severe disease and be free of any acute infection during 2 weeks before the inclusion. The medications of hypertension, hyperlipidemia and hyperuricemia were displayed in the S2–S4 Tables, respectively. The protocol was approved by the ethics committee of the Third Affiliated Hospital of Sun Yat-sen University. All subjects provided written informed consent before screening.

Anthropometric measures

Body height and weight were measured by the researchers and BMI was calculated as body weight(kg) divided by the square of the height(m). Hip circumference (HC) was the horizontal length between the most prominent parts of the buttocks, waist circumference (WC) was measured at the mid-position between the iliac crest and the last rib [26], and the waist-to-hip ratio (WHR) was calculated. Mid-upper arm circumference (MUAC) was measured at the mid-arm between the shoulder and elbow [18].

Blood biochemical assays

Venous blood samples were collected from participants to determine metabolic markers, including fasting blood-glucose, total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), triglyceride (TG), and uric acid (UA), using an automated enzymatic method (Hitachi, Japan, 7600–020 autonomic analyzer). High-pressure liquid chromatography (BIO-RAD, USA, D-10 analyzer) was used to measure the HbA1c level. Plasma insulin was evaluated by competitive radioimmunoassay (Centaur XP immunoassay system, Siemens Healthcare Diagnostics, New York, NY). The Homeostasis Model Assessment-Insulin Resistance (HOMA-IR) index, calculated by the formula: fasting plasma insulin (mU/L) x fasting plasma glucose (mmol/L)/22.5 [27], was used to assess Insulin Resistance (IR).

For man, waist circumference ≥ 85cm was used to define central obesity and for women as waist circumference ≥ 80cm [28].

Statistical analysis

The SPSS program for Windows (version 22.0) was utilized to process the analysis. Continuous variables of clinical characteristics were presented as mean±S.D. Differences between groups were evaluated by t-test, chi-square test or Wilcoxon rank-sum test. Data of non-normal distributions were logarithmically transformed before statistical analysis. Pearson or Spearman correlations were conducted to analyze the associations between two variables. Variables that were significantly related to the objective variable were tested for independence using multivariate linear regression analysis. The risk factors of central obesity were evaluated by logistic regression. The accuracy of anthropometric parameters to predict central obesity was evaluated by using receiver operator characteristic (ROC) curve analysis. The specificity and sensitivity of MUAC were calculated for each cut-off point in the sample. P value below 0.05 was examined statistically significant.

Results

Baseline characteristics

The study sample consisted of 103 individuals (mean age: 51.4±13.6 years) with 60 male (58%). The clinical characteristics of the subjects are shown in Table 1. The values of BMI (male, 26.27±4.58kg/m2, female, 24.00±2.73kg/m2), WC (male, 93.78±10.67cm, female, 88.35±6.88cm) and MUAC (male, 32.82±3.38cm, female, 30.11±2.62cm) were significantly higher while TC (male, 4.77±1.28mmol/L, female, 5.47±1.91mmol/L) and HDL-C (male, 0.96±0.23mmol/L, female, 1.21±0.33mmol/L) were significantly lower in male than those in female.

Table 1. General characteristics of study subjects.

Parameters	All(N = 103)	Men(N = 60)	Women(N = 43)	P(men vs women)
Age (years)	51.4±13.6	48.9±14.2	54.9±12.2	0.03
Diabetes duration (years)	8.79±3.13	9.82±3.21	8.13±2.11	0.78
BP (mmHg)	135/83	145/92	134/80	0.23
FBG (mmol/L)	8.70±3.34	8.62±2.79	8.83±4.02	0.75
BMI (kg/m²)	25.32±4.05	26.27±4.58	24.00±2.73	0.005
WC (cm)	91.51±9.62	93.78±10.67	88.35±6.88	0.004
WHR	0.94±0.06	0.95±0.05	0.93±0.06	0.13
MUAC (cm)	31.68±3.35	32.82±3.38	30.11±2.62	<0.001
TG (mmol/L)	2.15±2.55	2.23±2.13	2.04±3.06	0.71
TC (mmol/L)	5.07±1.61	4.77±1.28	5.47±1.91	0.03
HDL-C (mmol/L)	1.06±0.30	0.96±0.23	1.21±0.33	<0.001
LDL-C (mmol/L)	3.17±1.06	3.18±0.99	3.17±1.15	0.95
UA (μmol/L)	354.17±99.49	368.20±100.16	334.60±96.29	0.09
HbA1C (%)	9.04±2.51	9.16±2.30	8.86±2.79	0.56
LogHOMA-IR	0.61±0.23	0.64±0.23	0.57±0.22	0.13

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BMI, Body Mass Index; MUAC, Mid-upper Arm Circumference; WC, Waist Circumstance; WHR, Waist-to-hip Ratio; UA, Uric Acid; TG, Triglyceride; TC, Total Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol; LDL-C, Low-Density Lipoprotein Cholesterol; HbA1C, Hemoglobin A1c; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance.

In addition, the subjects were categorized based on tertiles cut-points of arm circumference level (tertile 1, MUAC < 29.50cm; tertile 2, 29.50cm≤MUAC≤32.60cm; tertile 3, MUAC > 32.60cm). As shown in Table 2, the value of BMI, WC and LogHOMA-IR of patients were significantly higher in the groups with higher MUAC. Moreover, the percentages of central obesity, identified by the measurement of waist circumference, were 55.8%, 85.7% and 97.1% respectively in the three groups (P<0.01).

Table 2. Characteristics of subjects according to mid-upper arm circumference tertiles.

	Tertile 1 (n = 34)	Tertile 2 (n = 35)	Tertile 3 (n = 34)	P value
	Tertile 1 (n = 34)	Tertile 2 (n = 35)	Tertile 3 (n = 34)	1 vs 2	1 vs 3	2 vs 3
Age(years)	55.2±11.7	52.1±12.5	46.9±12.5	0.33	0.01	0.11
BMI(kg/m²)	21.97±1.07	24.51±1.27	29.52±4.21	<0.01	<0.01	<0.01
MUAC (cm)	28.36±0.90	31.26±0.87	35.47±2.62	-	-	-
WC (cm)	84.59±7.07	89.94±5.32	100.04±8.93	<0.01	<0.01	<0.01
WHR	0.92±0.07	0.95±0.04	0.97±0.06	0.09	<0.01	0.13
HbA1C (%)	9.29±3.01	8.98±2.26	8.84±2.15	0.62	0.60	0.83
FBG (mmol/L)	9.40±4.38	8.26±270	8.45±2.64	0.16	0.24	0.83
BP (mmHg)	135/94	142/98	138/92	0.24	0.35	0.48
TC (mmol/l)	4.96±1.32	5.02±1.65	5.22±1.85	0.90	0.61	0.53
TG (mmol/l)	1.36±0.71	2.17±2.01	2.93±3.76	0.18	0.01	0.21
HDL-C (mmol/l)	1.24±0.34	1.01±0.21	0.95±0.26	<0.01	<0.01	0.34
LDL-C (mmol/l)	2.85±0.85	2.93±1.21	3.74±0.85	0.36	<0.01	0.02
UA(umol/L)	312.06±74.04	358.51±106.79	391.82±100.27	0.05	<0.01	0.15
LogHOMA-IR	0.46±0.21	0.63±0.17	0.74±0.21	<0.01	<0.01	0.02

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For list of abbreviations, see Table 1.

P value <0.05 were considered significant.

Correlation of mid-upper arm circumference with central obesity parameters, metabolic variables and insulin resistance

Correlation between the MUAC, central obesity markers and metabolic markers among all subjects were analyzed in our study. The MUAC positively correlated with BMI (r = 0.88, P < 0.001) and the central obesity markers, WC (r = 0.74, P < 0.001) and WHR (r = 0.30, P = 0.002). Among the traditional metabolic risk factors, MUAC showed a positive correlation with UA(r = 0.32, P<0.001) and LDL-C (r = 0.40, P<0.001) and negatively correlated with HDL-C (r = -0.32, P = 0.001) in all patients (Fig 1). Furthermore, the relationships shown in Table 3 were observed differently by gender. In both genders, MUAC correlated positively with BMI, WC and LDL-C, while the positive correlation with WHR, TG, UA and negative correlation with HDL-C were only shown in men. The association between MUAC and glycemic parameters was not found in our research.

Fig 1 — Correlation assessed by Pearson analysis. MUAC, Mid-upper Arm Circumference; WC, Waist Circumstance; WHR, Waist-to-hip Ratio; BMI, Body Mass Index; UA, Uric Acid; LDL-C, Low-Density Lipoprotein Cholesterol; HDL-C, High-Density Lipoprotein Cholesterol.

Table 3. Relationship between MUAC and other measurements by gender.

Variables	MUAC (cm)
	Men		Women
	r	P	r	P
BMI (kg/m²)	0.95	< 0.01	0.92	< 0.01
WC (cm)	0.82	< 0.01	0.48	< 0.01
WHR	0.48	< 0.01	0.10	0.50
TG (mmol/L)	0.30	0.02	0.20	0.20
TC (mmol/L)	0.19	0.13	0.07	0.65
HDL-C (mmol/L)	-0.33	0.01	-0.23	0.14
LDL-C (mmol/L)	0.57	< 0.01	0.31	0.04
UA (μmol/L)	0.39	< 0.01	0.21	0.17
HbA1C (%)	-0.18	0.17	0.07	0.65
FBG (mmol/L)	-0.06	0.67	0.03	0.83
LogHOMA-IR	0.52	< 0.01	0.55	< 0.01

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For list of abbreviations, see Table 1.

P values<0.05 were considered significant.

Our results showed that MUAC (male, r = 0.52, P<0.01, female, r = 0.55, P<0.01), WC(male, r = 0.44, P<0.01, female, r = 0.35, P = 0.02) and BMI (male, r = 0.46, P<0.01, female, r = 0.51, P<0.01) were positively related to LogHOMA-IR in both genders. However, we found that WHR (male, r = 0.33, P = 0.01, female, r = 0.74, P = 0.64) significantly positively correlated with LogHOMA-IR only in male. Additionally, MUAC was more strongly correlated with LogHOMA-IR than WC, BMI and WHR in both genders (Table 4).

Table 4. Relationship between HOMA-IR and other anthropometric measurements by gender.

Variable	LogHOMA-IR
	Men		Women
	r	p	r	p
MUAC (cm)	0.52	< 0.01	0.55	< 0.01
WC (cm)	0.44	< 0.01	0.35	0.02
WHR	0.33	0.01	0.74	0.64
BMI (kg/m²)	0.46	< 0.01	0.51	< 0.01

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For list of abbreviations, see Table 1.

P values<0.05 were considered significant.

Linear regression analysis was conducted to identify the predictive effect of MUAC on the insulin resistance. After adjusting for confounding clinical parameters, including age, gender, WHR, UA, TG, LDL-C and HDL-C, the value of MUAC remained independently associated with LogHOMA-IR (β = 0.036, P<0.001) (Table 5).

Table 5. Linear regression analysis of logHOMA-IR with different clinical characteristics.

		β	P	95% CI	R²
MUAC (cm)	Unadjusted model	0.039	<0.001	0.028,0.051	0.319
	Model 1	0.041	<0.001	0.029,0.054	0.327
	Model 2	0.041	<0.001	0.027,0.054	0.328
	Model 3	0.036	<0.001	0.021,0.050	0.392

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Model 1: adjusted for age and gender.

Model 2: adjusted for age, gender and Waist-to-hip Ratio(WHR).

Model 3: adjusted for model 2 plus Uric Acid(UA), Triglyceride(TG), Low-Density Lipoprotein Cholesterol(LDL-C) and High-Density Lipoprotein Cholesterol(HDL-C).

P values<0.05 were considered significant.

The results of logistic regression analysis presented in Table 6 showed that only MUAC (OR, 2.129; 95% CI, 1.311–3.457; P = 0.002) and LDL-C (OR, 3.023; 95% CI, 1.090–8.383; P = 0.033) were associated with increased odds of central obesity after adjusting for age, gender, TG, AC,UA and use of statin.

Table 6. Logistic regression analysis of risk factors for central obesity.

	Confirmed Central Obesity
	Odds ratio	(95% CI)	P value
MUAC (cm)	2.129	1.311, 3.457	0.002
LDL-C (mmol/l)	3.023	1.090, 8.383	0.033

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Risk factors including Mid-upper Arm Circumference(MUAC), age, gender, Low-Density Lipoprotein Cholesterol(LDL-C), Triglyceride(TG), Uric Acid(UA) and Use of Statin.

P values<0.05 were considered significant.

Optimal cut-off points of mid-upper arm circumference for central obesity

The proportion of central obesity was 81.7% in men and 83.7% in women. The ROC curves are presented in Fig 2, and the area under the curve (AUC) for MUAC as a predictor of central obesity was 0.922 for male and 0.788 for female. The best MUAC cutoff point for defining central obesity was 30.9cm for male (Youden index = 0.746, Sensitivity: 83.7%; Specificity: 90.9%) and 30.0cm for female (Youden index = 0.528, Sensitivity: 52.8%; Specificity: 99.9%).

Discussion

Previous studies [6,7,29–31] in different subjects demonstrated that abdominal distribution of body fat was correlated with IR, type 2 diabetes, cardiovascular diseases and total mortality risk. And the hyperglycemia was more difficult to manage in individuals with both diabetes and central obesity compared with those with only diabetes [32]. As a result, to found out an appropriate and more easily conducted method to identity central obesity and subsequently choose appropriate hypoglycemic agents could lead to better control of hyperglycemia and eventually reduce the mortality in diabetes. In Rerksuppaphol S’ study, MUAC was suggested to be a simple and accurate parameter to identify overweight and obesity in Thai school-age children [33]. In an observational, multinational cross-sectional study with 7337 children aged 9–11 years, MUAC was shown to be a suitable method to detect obesity in children [34]. Moreover, Mazıcıoğlu and his group has demonstrated that MUAC could be a useful measurement in screening body fat distribution in children [35]. However, the subjects of those studies were all not patients with diabetes. In our study, we focused on the MUAC especially in type 2 diabetic patients and found that MUAC could serve as a simple and practical tool to better screen and identify central obesity among type 2 diabetic patients. As shown in the results, both the value of WC and the percentage of patients with central obesity were larger in the group with higher MUAC than those in the group with lower MUAC, which indicated the association between MUAC and central obesity in diabetes. In addition, MUAC correlated well with other anthropometric measurements in central obesity patients with diabetes. In both genders, MUAC was significantly associated with WC and BMI, which indicates that MUAC could be used as an effective indicator for both central obesity and overall obesity. Furthermore, in our study, gender-specific cut-off points of the MUAC for central obesity were also established. Based on our analyses, MUAC≥30.9cm was determined as the best cutoff value for men to define population with central obesity, and≥30cm for women, with 92.2% accuracy for men and 78.8% accuracy for women. To our best knowledge, this was the first study to evaluate the association between MUAC and central obesity and to determine the cut-off value of the MUAC for the prediction of central obesity especially in patients with type 2 diabetes.

Another feature of our research was the positive correlation between the MUAC and IR among Chinese type 2 diabetic patients. In our research, we found that MUAC has a positive correlation with HOMA-IR levels, which has been demonstrated to be the indicator of insulin resistance. The relationship between MUAC and IR was also confirmed in recent studies [18,20]. The study in Pakistan [18] focused on children with majority of normal BMI found that arm-fat percentage is positively correlated with insulin levels. In a study conducted among 147 adults with overweight or obesity, Gómez-García et al [20] made a conclusion that mid arm circumference was a better predictor of IR. The subjects in these studies, however, were all not patients with diabetes. Unlike previous studies, we further explored the relationship between MUAC and IR among type 2 diabetes. As diabetes, a metabolic disorder causing disease, has been proved to be a more important factor than obesity in causing IR [36], results in our study further demonstrated that the increase of MUAC could serve as another important indicator of causing IR among diabetic patients. Furthermore, we found MUAC might be superior to WC (Men: r_MUAC = 0.52, r_WC = 0.44; Women: r_MUAC = 0.55, r_WC = 0.35) in measuring IR in type 2 diabetes which indicated that MUAC could be used as a better screening method of IR in type 2 diabetes.

We also evaluated the association between MUAC and metabolic risk factors including plasma UA and lipid. We found that MUAC was correlated positively with UA, LDL-C and negatively with HDL-C, indicating a higher risk of developing metabolic disorders in a population with a larger MUAC, which reminded us of taking more concern on the metabolic risk factors in this population. It is interesting that while MUAC correlated with BMI, waist circumference and LDL-C in both genders, but the correlation between MUAC and WHR, Tg, Uric acid, and HDL cholesterol in women was not observed. The plausible reason for sex difference of WHR may be due to the different fat distribution during aging. Men have consistent fat distribution during aging, which is always characterized with more visceral fat in the abdomen (apple shape), but women have more subcutaneous fat in the hip and thighs (pear shape) before menopause and have more visceral fat in abdomen (apple shape) after menopause due to the dramatical decline of estrogen [37]. The female patients in our study, however, were mainly during the menopause period. As a result, no correlation was shown between MUAC and WHR in these estrogen-changing women. The differences of Tg, Uric acid, and HDL cholesterol also attributed to the decline of estrogen after menopause for the protective role of estrogen for metabolic diseases [38]. Therefore, using the MUAC as a clinical tool to detect some metabolic risk factors, such as WHR, Tg, Uric acid, and HDL cholesterol in women should be cautious.

In our study, we found the MUAC was a simple and effective measurement for the determination of central obesity, insulin resistance and multiple metabolic risk factors among type 2 diabetic patients in China. Moreover, we also confirmed the cut-off values of MUAC for central obesity evaluation. Recently, Hou Y, et al also conducted a similar study about MUAC in the Chinese population [39]. However, the study was different from ours. Firstly, the population in their study included both participates with diabetes or normal glucose tolerance (NGT). The analysis of association between MUAC and central obesity and IR was conducted in all the participants but not just especially in patients with diabetes. The only analysis especially involved with diabetes was the multivariable logistic regression analysis base on the subgroups of diabetes or not. As we mentioned above, the central obesity, IR and MUAC in diabetes were quite different from those in participates with NGT, then the conclusion and significance of Hou’s could not be drawn to the population with diabetes. Secondly, the aim of the study was to investigate the associations between MUAC and cardiometabolic risk profiles but not mainly the MUAC and central obesity and IR.

Comparing MUAC with WC and WHR, MUAC has some advantages: without influence of the moment of measurement (no influence of dining); simpler for both physicians and subjects, especially in the public and crowded places; more convenient and more socially acceptable, particularly for obese population; additionally, more accurate of the result for the unified method of detection. As a result, MUAC may be a better index in large epidemiological survey about central obesity and insulin resistance in type 2 diabetes.

Our research has some limitations. Firstly, the sample size in our research was relatively small. However, this is just a pilot study evaluating the association between MUAC and central obesity and IR, especially in patients with type 2 diabetes. Therefore, with the suggestive findings from this study, prospectively designed studies with more participants would be conducted in the near future. Secondly, the index of HOMA-IR, which represents insulin resistance levels, remains relatively simple and no international reference values. However, as we know, HOMA-IR has already been proved to be closely correlated with the hyperinsulinemic euglycemic clamp [40], the gold standard to evaluate IR. Therefore, we think the evaluation of IR in our study might be relatively simple but practical. Thirdly, the anti-diabetic agents and anti-hypertensive agents could influence insulin resistance, which might consequently cause effects on our results. It would be most appropriate to include completely drug-naïve subjects into our study. However, this was a cross-sectional study but not a case-control clinical trial, we could not intervene in any way to stop the anti-diabetic agents or any other medications as clinical trials do. We can also see pharmacotherapy of patients with T2DM, hyperlipidemia or hypertention were not stopped in many similar studies involving the investigation of insulin resistance [41–44]. Moreover, the subjects included in our study were mostly in their middle age. Sixty-eight percent of middle-age and elderly population in our society had at least one chronic disease [45]. In addition, the prevalence of hypertension and hyperlipidemia in patients with type 2 diabetes is 51.9% and 30.5% respectively [46–47]. As a society with dramatically increased aging population, we can hardly include the subjects at the middle age without any other diseases or any other drugs. Drug-naïve subjects will be included into our future study to minimize the effects of agents on insulin resistance. Despite the limitations, our results offered evidence that MUAC might be a screening tool that could predict central obesity and IR, which might be superior to WC and WHR in Chinese patients with type 2 diabetes.

In conclusion, higher MUAC is correlated with central obesity, insulin resistance and multiple metabolic risk indicators in Chinese subjects with type 2 diabetes. It could be expectable that MUAC will be widely applied in both research and clinical practice in future for its simplicity and stability.

Supporting information

S1 Table. Hypoglycemic agents.

(DOCX)

Click here for additional data file.^{(14KB, docx)}

S2 Table. Hypotensive agents.

(DOCX)

Click here for additional data file.^{(13.9KB, docx)}

S3 Table. Lipid-lowering agents.

(DOCX)

Click here for additional data file.^{(13.7KB, docx)}

S4 Table. Uric-acid-lowering agents.

(DOCX)

Click here for additional data file.^{(13.8KB, docx)}

Acknowledgments

The authors wish to thank the researchers for their assistance of measurement and samples collection. We also thank all the members of our team for their contribution and the subjects who participated in our study.

Bin Yao and Xu-bin Yang developed the idea and designed the research. Yanhua Zhu and Qiong-yan Lin collected and analyzed the data, wrote the manuscript. Yao Zhang analyzed the data and revised the manuscript. Hong-rong Deng collected the data and contributed to the discussion. Xi-ling Hu helped to analyze the data.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This work was supported by Science and technology planning project of Guangzhou Tian He district(2018YT016); Natural Science Foundation of Guangdong Province [2018A030313915]; Medical Scientific Research Foundation of Guangdong Province of China [A2018286]; the National Key Research and Development Program [2017YFC1309602]; and Science and Technology Program YueXiu District (2018-WS-005).

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PLoS One. doi: 10.1371/journal.pone.0231308.r001

Decision Letter 0

Mauro Lombardo

14 Oct 2019

PONE-D-19-19096

Mid-upper Arm Circumference as a Simple Tool for Identifying Central Obesity and Insulin Resistance in Type 2 Diabetes

PLOS ONE

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Reviewer #1: This paper investigated the utility of measuring mid-upper arm circumference (MUAC) as a surrogate for measuring waist circumference or waist to hips ratio when assessing insulin resistance in patients with type 2 diabetes. It is concluded that MUAC is a simple tool to measure central obesity and insulin resistance. It is of interest that while MUAC correlated with BMI, waist circumference and and LDL-C in both genders, It failed to correlate with WHR, Tg, Uric acid, and HDL cholesterol in women. The implication of this failure in women was not discussed adequately. The possible reason (women are pear or apple shape while men are always apple shape) and possible consequence of using the MUAC as a clinical tool in women was not discussed.

On Page 19 Line 3 it is stated that "Furthermore we found MUAC might be superior to WC in measuring IR in type 2 diabetes..." It would be helpful to the reader to quote here the data and statistical result that support this statement.

Minor Points

Page 4 Para 2 Line 3 "...it remains a number of limitations od WC.." should be "....there remain a number of limitations of WC.."

Page 4 Para 2 Line 11 "Therefore some Researches were conducted....." should be "Therefore som research was conducted..."

Page 4 Para 2 Line 15 "However it remains little data to..." should be "However there is little data to..."

There are many other examples where a native English speaker might help.

Reviewer #2: Dear authors,

This article is interesting because authors investigated the importance MUAC. However I think this article has unavoidable problems to be confirmed.

<1> Considering this study was performed for T2DM patients, authors should investigate the effect of T2DM itself to results in detail. Authors wrote “without insulin treatment or other medications that could alter insulin secretion (such as Sulphonylurea) for at least 2 weeks, not having severe disease and be free of any acute infection during 2 weeks before the inclusion” at MATERIALS and METHODS and patients’ HbA1c levels in Table 1. At first, authors should write details of agents for T2DM which patients were administered when they were investigated. For instance, almost all agents for T2DM affected to insulin profile, DPP4Is and/or SGLT2Is particularly (of course, glinide were not used, I think). If patients used these two agents in particular, it must affect the results (If patients did not use these two agents, authors should write detail of agents for T2DM in Table 1). Indeed, authors should investigate the association between MUAC and glycemic parameters because authors targeted T2DM patients. If authors are thinking there is no necessity to consider glycemic parameters in this study, this study’s aim could not be understood by many of readers (including me) as they will not understand the difference between this study and the previous article. Moreover, authors must see the article, [Hou Y, et al. BMJ Open 2019; 9: e028904. doi:10.1136/bmjopen-2019-028904]. This article included 6287 participants with or without diabetes aged 40 years or older and was investigated from very various viewpoints. This article has already revealed almost all of this study’s results except the participants for not only T2DM. Actually, I think this article did not have enough novelty, and this study cohort was not large. So what I wrote above must be investigated and mentioned in order to gain novelty; this study focused only T2DM patients.

In addition, medications of HT can also affect the results. Moreover, if patients were medicated by agents against hyperlipidemia and hyperuricemia, these must affect the results definitely. Authors should also investigate and mention them.

<2> Authors should explain log HOMAIR were used to investigation of association with MUAC. I would like to know why authors did not use HOMAIR itself. I would like to know whether authors considered statistical problem with using HOMAIR itself or not.

In summary, this article surely targeted T2DM patients, but more novelty were required. Statistical analysis itself was done properly, but considering novelty of this article or descriptions, it is insufficient, I think. Almost all of this article were very similar to the previous reports of non T2DM patients. Readers might think targeting only T2DM patients did not make sense at investigations like this. So, authors should reveal the impotence of this study properly. If authors could not confirm above all, this article is difficult to be accepted.

Regards,

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PLoS One. 2020 May 21;15(5):e0231308. doi: 10.1371/journal.pone.0231308.r002

Author response to Decision Letter 0

31 Dec 2019

Reviewer #2: Dear authors,

This article is interesting because authors investigated the importance MUAC. However I think this article has unavoidable problems to be confirmed.

1、 Considering this study was performed for T2DM patients, authors should investigate the effect of T2DM itself to results in detail. Authors wrote “without insulin treatment or other medications that could alter insulin secretion (such as Sulphonylurea) for at least 2 weeks, not having severe disease and be free of any acute infection during 2 weeks before the inclusion” at MATERIALS and METHODS and patients’ HbA1c levels in Table 1. At first, authors should write details of agents for T2DM which patients were administered when they were investigated. For instance, almost all agents for T2DM affected to insulin profile, DPP4Is and/or SGLT2Is particularly (of course, glinide were not used, I think). If patients used these two agents in particular, it must affect the results (If patients did not use these two agents, authors should write detail of agents for T2DM in Table 1).

Answer:

Thank you for your comment.

We’ve added the details of hypoglycemic agents in the MATERIALS AND METHODS and supplemental table 1 as you suggested. All the patients were treated without insulin treatment, Sulphonylurea, sodium-dependent glucose transporters-2 inhibitors (SGLT-2i) or glucagon-like peptide-1 (GLP-1) analog for at least 2 weeks. (see Paragraph 2 in Page 5 of the resubmitted manuscript with tracked changes)

2、Indeed, authors should investigate the association between MUAC and glycemic parameters because authors targeted T2DM patients. If authors are thinking there is no necessity to consider glycemic parameters in this study, this study’s aim could not be understood by many of readers (including me) as they will not understand the difference between this study and the previous article. Moreover, authors must see the article, [Hou Y, et al. BMJ Open 2019; 9: e028904. doi:10.1136/bmjopen-2019-028904]. This article included 6287 participants with or without diabetes aged 40 years or older and was investigated from very various viewpoints. This article has already revealed almost all of this study’s results except the participants for not only T2DM. Actually, I think this article did not have enough novelty, and this study cohort was not large. So what I wrote above must be investigated and mentioned in order to gain novelty; this study focused only T2DM patients.

Answer:

Thank you for your great comment.

We’ve added the analysis of the association between MUAC and glycemic parameters in the RESULTS, table 1, table 2 and table3 (see Paragraph 2 in Page 8 of the resubmitted manuscript with tracked changes). However, the association between MUAC and glycemic parameters was not observed in our research.

We acknowledged that Hou’s study also focused on MUAC in the Chinese population. However, the study was different from ours. Firstly, the population in their study included both participates with diabetes or normal glucose tolerance (NGT). The analysis of association between MUAC and central obesity or IR was conducted in all the participants but not just in patients with diabetes. The only analysis specially involved with diabetes was the multivariable logistic regression analysis base on the subgroups of diabetes or not. The key points that two studies concerned were different. As the importance of IR in diabetes was reinforced in the most recent studies1-2 and the central obesity, IR and MUAC in diabetes were quite different from those in participants with NGTs3, the conclusion and significance of Hou’s study could not be drawn to the population with diabetes. Secondly, the aim of Hou’s study was to investigate the associations between MUAC and cardiometabolic risk profiles but not mainly the MUAC and central obesity and IR.

The relevant contents were added in the INTRODUCTION (see Paragraph 1 in Page 4 of the resubmitted manuscript with tracked changes) and DISCUSSION (see Paragraph 2 in Page 17 of the resubmitted manuscript with tracked changes).

We acknowledged that the sample size in our research was relatively small. However, this is just a pilot study evaluating the association between MUAC and central obesity and IR specially in patients with diabetes. With the suggestive findings from this study, prospectively designed studies with more participants would be conducted in the near future. The relevant contents were added in the DISCUSSION (see Paragraph 3 in Page 18 of the resubmitted manuscript with tracked changes).

[1] AHLQVIST E, STORM P, KARAJAMAKI A, et al. 2018. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables[J]. Lancet Diabetes Endocrinol, 6(5): 361-369.

[2] ZOU X, ZHOU X, ZHU Z, et al. 2019. Novel subgroups of patients with adult-onset diabetes in Chinese and US populations[J]. Lancet Diabetes Endocrinol, 7(1): 9-11.

[3] YANG Q, GRAHAM T E, MODY N, et al. 2005. Serum retinol binding protein 4 contributes to insulin resistance in obesity and type 2 diabetes[J]. Nature, 436(7049): 356-362.

3、In addition, medications of HT can also affect the results. Moreover, if patients were medicated by agents against hyperlipidemia and hyperuricemia, these must affect the results definitely. Authors should also investigate and mention them.

Answer:

Thank you for your comment.

We have added the details of the medications of hypertension, hyperlipidemia and hyperuricemia in the supplemental table 2, table3 and table4, respectively.

4、Authors should explain log HOMAIR were used to investigation of association with MUAC. I would like to know why authors did not use HOMAIR itself. I would like to know whether authors considered statistical problem with using HOMAIR itself or not.

Answer:

Thank you for your comment.

As we mentioned in the Statistical analysis (see Paragraph 1 in Page 7 of the resubmitted manuscript with tracked changes), data of non-normal distributions were logarithmically transformed before statistical analysis.

As a result, to ensure normality of distribution and to meet the criteria for regression analysis, we used log HOMAIR to investigate the association of HOMAIR and MUAC as many other studies did1-3.

[1] SANTHANAM P, ROWE S P, DIAS J P, et al. 2019. Relationship between DXA measured metrics of adiposity and glucose homeostasis; An analysis of the NHANES data[J]. PLoS One, 14(5): e0216900..

[2] MENTE A, MEYRE D, LANKTREE M B, et al. 2013. Causal relationship between adiponectin and metabolic traits: a Mendelian randomization study in a multiethnic population[J]. PLoS One, 8(6): e66808.

[3] ISHIMURA S, FURUHASHI M, WATANABE Y, et al. 2013. Circulating levels of fatty acid-binding protein family and metabolic phenotype in the general population[J]. PLoS One, 8(11): e81318.

Reviewer #1:

1、This paper investigated the utility of measuring mid-upper arm circumference (MUAC) as a surrogate for measuring waist circumference or waist to hips ratio when assessing insulin resistance in patients with type 2 diabetes. It is concluded that MUAC is a simple tool to measure central obesity and insulin resistance. It is of interest that while MUAC correlated with BMI, waist circumference and and LDL-C in both genders, It failed to correlate with WHR, Tg, Uric acid, and HDL cholesterol in women. The implication of this failure in women was not discussed adequately. The possible reason (women are pear or apple shape while men are always apple shape) and possible consequence of using the MUAC as a clinical tool in women was not discussed.

Answer:

Thank you for your excellent comment.

It is interesting that while MUAC correlated with BMI, waist circumference and and LDL-C in both genders, but the correlation betwee MUAC and WHR, Tg, Uric acid, and HDL cholesterol in women was not observed. The plausible reason for this sex difference may be due to the different fat distribution during aging. Men have consistent fat distribution during aging, which is always characterized with more visceral fat in the abdomen (apple shape), but women have more subcutaneous fat in the hip and thighs(pear shape) before menopause and have more visceral fat in abdomen (apple shape) after menopause due to the dramatical decline of estrogen1. The female patients in our study, however, were mainly during the menopause period. As a result, no correlation was shown between MUAC and WHR in these estrogen-changing women. The differences of Tg, Uric acid, and HDL cholesterol also attributed to the decline of estrogen after menopause for the protective role of estrogen for metabolic diseases2. Therefore, using the MUAC as a clinical tool to detect some metabolic risk factors, such as WHR, Tg, Uric acid, and HDL cholesterol in women should be cautious.

The relevant contents were added in the DISCUSSION (see Paragraph 2 in Page 16 and Paragraph 1 in Page 17 of the resubmitted manuscript with tracked changes).

[1] KARASTERGIOU K, SMITH S R, GREENBERG A S, et al. 2012. Sex differences in human adipose tissues - the biology of pear shape[J]. Biol Sex Differ, 3(1): 13.

[2] TRAMUNT B, SMATI S, GRANDGEORGE N, et al. 2019. Sex differences in metabolic regulation and diabetes susceptibility[J]. Diabetologia.

2、On Page 19 Line 3 it is stated that "Furthermore we found MUAC might be superior to WC in measuring IR in type 2 diabetes..." It would be helpful to the reader to quote here the data and statistical result that support this statement.

Answer:

Thank you for your comment.

We’ve added the data and statistical result on 16 Page Line 1.

3、Minor Points

Page 4 Para 2 Line 3 "...it remains a number of limitations od WC.." should be "....there remain a number of limitations of WC.."

Page 4 Para 2 Line 11 "Therefore some Researches were conducted....." should be "Therefore som research was conducted..."

Page 4 Para 2 Line 15 "However it remains little data to..." should be "However there is little data to..."

There are many other examples where a native English speaker might help.

Answer:

We are sincerely sorry about the language issue. We have revised some mistakes as you suggested.

We’ll seek help from English editing company and thoroughly review and revise the manuscript in the next revision.

Attachment

Submitted filename: Supplemental file for review.docx

Click here for additional data file.^{(18.2KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0231308.r003

Decision Letter 1

Mauro Lombardo

9 Jan 2020

PONE-D-19-19096R1

Mid-upper Arm Circumference as a Simple Tool for Identifying Central Obesity and Insulin Resistance in Type 2 Diabetes

PLOS ONE

Dear Dr Yao,

We would appreciate receiving your revised manuscript by Feb 23 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

Please include the following items when submitting your revised manuscript:

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We look forward to receiving your revised manuscript.

Kind regards,

Mauro Lombardo

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Please answer to the 1st reviewer's comments and resubmit

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Thank you for addressing my concerns. Therefore it is now suitable for publication. I don't know if raw data has been made available.

Reviewer #2: Dear authors,

I think authors reviewed and answered against my comment carefully. Meanwhile, there are some remaining important problems, especially in the data authors added.

I commented to authors about the novelty of this study considering the previous article with large numbers. Actually I understand there is the difference between this and the previous article with regard to the subjects (the subjects of this study were only patients of T2DM). However, partly because this study cohort were not large, I commented to authors about various points.

At first, authors should consider the effect to results by the administration of subjects more. The supplemental table which authors added showed the difficulty of proving correctness of authors’ investigation and conclusion. I wrote that almost all agents for T2DM affected to HOMA-IR, especially not only sulphonylurea/glinide but also DPP4i /SGLT2i affected to insulin profile itself previously. Actually, authors could not remove the possibility of effect of anti-diabetic agents to the results and analysis of HOMA-IR. I even think it is possible diuretics or ARB/ACEi administration affected HOMA-IR.

Considering authors’ opinion of the novelty of this study (the subjects of this study were only patients of T2DM), authors must remove the possibility of effect of anti-diabetic agents at least.

Same as above, authors should reconsider table 6 and related results. LDL-C must be affected by statin. 70 of 103 patients were with statin in this study. Authors must consider the effect of statin to the results and analysis.

Honestly, I think all subjects should be without anti-diabetic agents. Furthermore, if authors would like to mention about the relationships including LDL-C, the subjects should be without agents against hyperlipidemia or authors should investigate and analyze considering the effect of agents against hyperlipidemia.

I would like authors to understand my previous and present comments adequately.

Authors’ revision considering these important points must be needed for this article to be accepted.

Regards,

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

PLoS One. 2020 May 21;15(5):e0231308. doi: 10.1371/journal.pone.0231308.r004

Author response to Decision Letter 1

20 Feb 2020

Point-by –Point Answers to

the Editor’s and Reviewers’ comments

Reviewer #2: Dear authors,

I think authors reviewed and answered against my comment carefully. Meanwhile, there are some remaining important problems, especially in the data authors added.

Considering authors’ opinion of the novelty of this study (the subjects of this study were only patients of T2DM), authors must remove the possibility of effect of anti-diabetic agents at least.

I would like authors to understand my previous and present comments adequately.

Authors’ revision considering these important points must be needed for this article to be accepted.

Regards,

Answer:

Thank you so much for your excellent comments.

First，honestly, we were very clear that all the pharmacotherapy of T2DM, hyperlipidemia or hypertention could definitely influenced insulin resistance. However, this was a cross-sectional study but not a case-control clinical trial, we could not intervene in any way to stop the anti-diabetic agents or any other medications as clinical trials do [1-2]. We can also see pharmacotherapy of patients with T2DM, hyperlipidemia or hypertention were not stopped in many similar studies involving the investigation of insulin resistance [3-5].

Second, we should admit that we could not completely eliminate all the influencing factors. For example, if we stopped the pharmacotherapy before evaluating HOMA-IR (note that the mean diabetes duration of patients in our study was 8~9 years and the mean HbA1c was 9%), the plasma glucose would elevate, sometimes to a very high level. As is well known, the strongest insulin secretion influencing factor is glucose [6]. As a result, the elevating glucose would definitely influence the levels of insulin, and consequently influence HOMA-IR. Therefore, stopping the pharmacotherapy will still influence HOMA-IR.

Besides, in the guidelines of American Diabetes Association (ADA), European Association for the Study of Diabetes (EASD) or Chinese Diabetes Society (CDS), lifestyle intervention was recommended throughout the management of T2DM as a fundamental treatment [7-8]. The lifestyle management can also significantly change the insulin resistance [9]. However, subjects in studies with pharmacotherapy or not could not stop lifestyle management, which consequently could not avoid its effect on HOMA-IR.

That may be the reason why the real-world study, with more diverse settings but less intervention, is gaining more and more concern [10].

Third, as you pointed out that it is possible diuretics or ARB/ACEi administration could affect HOMA-IR, it would be most appropriate to include completely drug-naïve subjects into our study. However, the subjects included in our study were mostly in their middle age. Sixty-eight percent of middle-age and elderly population in our society had at least one chronic disease[11]. In addition, the prevalence of hypertension and hyperlipidemia in patients with diabetes is 51.9% and 30.5% respectively. More than 50% diabetic patients had at least one chronic diabetes complication [12-13]. As a society with dramatically increased aging population, we can hardly include the subjects at the middle age without any other diseases or any other drugs.

For the reasons above, we can not eliminate all the affecting factors including the effects of drugs or lifestyle intervention whether the cohort is large or not. As we mentioned in the discussion “ this is just a pilot study evaluating the association between MUAC and central obesity and IR, specially in patients with diabetes. Therefore, with the suggestive findings from this study, prospectively designed studies with more participants would be conducted in the near future” (see Paragraph 3 in Page 18 of the resubmitted manuscript with tracked changes), prospectively designed studies with drug-naive participants would be conducted in the near future at your great suggestions.

For the question of LDL-C affected by statin, besides the reasons mentioned above, we also conducted a logistic regression to adjust for using statin or not and find that using statin or not did not affect the results (OR：3.023 and P：0.033). (see Paragraph 3 in Page 11 of the resubmitted manuscript with tracked changes)

[1] SEDGWICK P. 2015. Bias in observational study designs: cross sectional studies[J]. Bmj, 350: h1286.

[2] Kesmodel, Ulrik, S. Cross‐sectional studies – what are they good for?[J]. Acta Obstetricia et Gynecologica Scandinavica: Official Publication of the Nordisk Forening for Obstetrik och Gynekologi, 2018.

[3] Mamtani M , Kulkarni H , Dyer T D , et al. Waist Circumference Independently Associates with the Risk of Insulin Resistance and Type 2 Diabetes in Mexican American Families[J]. PLOS ONE, 2013, 8.

[4] Lim J S , Choi Y J , Kim S K , et al. Optimal Waist Circumference Cutoff Value Based on Insulin Resistance and Visceral Obesity in Koreans with Type 2 Diabetes[J]. Diabetes & Metabolism Journal, 2015, 39(3).

[5] Elkeles R S , Godsland I F , Feher M D , et al. Coronary calcium measurement improves prediction of cardiovascular events in asymptomatic patients with type 2 diabetes: the PREDICT study[J]. European Heart Journal, 2008, 29(18):2244-2251.

[6]Jackson R , Rudelt C , Willaime J P . Effects of prolonged glucose infusion on insulin secretion, clearance, and action in normal subjects.[J]. American Journal of Physiology, 1996, 270(2 Pt 1):E251

[7]2020. 6. Glycemic Targets: Standards of Medical Care in Diabetes-2020[J]. Diabetes Care, 43(Suppl 1): S66-s76.

[8]Bailey, Timothy. Options for Combination Therapy in Type 2 Diabetes: Comparison of the ADA/EASD Position Statement and AACE/ACE Algorithm[J]. The American Journal of Medicine, 2013, 126(9):S10-S20.

[9]SAMPATH KUMAR A, MAIYA A G, SHASTRY B A, et al. 2019. Exercise and insulin resistance in type 2 diabetes mellitus: A systematic review and meta-analysis[J]. Ann Phys Rehabil Med, 62(2): 98-103.

[10] Sherman R E , Anderson S A , Dal Pan G J , et al. Real-World Evidence — What Is It and What Can It Tell Us?[J]. New England Journal of Medicine, 2016, 375(23):2293-2297.

[11] Cheng Y，Cao Z，Hou J , et al. Investigation and association analysis of multimorbidity in middle-aged and elderly population in China, 2019, 23( 6):625-629.

[12] Ji L , Hu D , Pan C , et al. Primacy of the 3B Approach to Control Risk Factors for Cardiovascular Disease in Type 2 Diabetes Patients[J]. The American Journal of Medicine, 2013, 126(10):925.e11-925.e22.

[13] Liu Z , Fu C , Wang W , et al. Prevalence of chronic complications of type 2 diabetes mellitus in outpatients - a cross-sectional hospital based survey in urban China[J]. Health & Quality of Life Outcomes, 2010, 8(1):62-0.

Reviewer #1: Thank you for addressing my concerns. Therefore it is now suitable for publication. I don't know if raw data has been made available.

Answer:

Thank you for your comment.

Raw data has been available now.

Attachment

Submitted filename: Supplemental file for review .docx

Click here for additional data file.^{(16.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0231308.r005

Decision Letter 2

Mauro Lombardo

27 Feb 2020

PONE-D-19-19096R2

Mid-upper Arm Circumference as a Simple Tool for Identifying Central Obesity and Insulin Resistance in Type 2 Diabetes

PLOS ONE

Dear Dr Yao,

We would appreciate receiving your revised manuscript by Apr 12 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

Please include the following items when submitting your revised manuscript:

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An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Mauro Lombardo

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: (No Response)

Reviewer #2: Dear authors,

I am glad to see authors careful and proper answers to my comment.　

Firstly, authors performed modified logistic regression (adjusting for using statin or not) adequately. This must be needed and I am really glad to see this result.

In addition, authors wrote “this is just a pilot study evaluating the association between MUAC and central obesity and IR, specially in patients with diabetes” in discussion. I have been thinking authors should mention this, and authors did in this revised document. I am also glad to see the description.

Regarding effect of antidiabetic agents to results, I understand authors’ reply. As authors wrote in answers to my comments, this study was cross-sectional study. I think the study cohort was small as cross-sectional study. Of course, I understood anti-diabetic agents (as well as anti-hypertensive agents) should not be stopped in patients of T2DM. On the other hand, the effect to results should be considered because of this small cohort especially.

Considering above, authors should mention the limitation of this study more specifically. In detail, authors should add the description about the possibility of effect of anti-diabetic agents and anti-hypertensive agents to results, same as authors wrote in answers to my comments. Limitations should be written adequately in order to be understood by readers properly.

The revision of this article must be meaningful for authors as well as readers of this article. I really think authors considered my comments devotedly and modified properly. This must be the last recommendation to authors. I am looking forward to seeing authors’ revised document considering my comments.

[additional]

The description which I mentioned above, “this is just a pilot study evaluating the association between MUAC and central obesity and IR, specially in patients with diabetes”, should be modified to “this is just a pilot study evaluating the association between MUAC and central obesity and IR, “especially” in patients with “type 2 diabetes””. Same as this, the other descriptions, “patients with diabetes”, should be modified to “patients with type 2 diabetes”.

Sincerely,

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

PLoS One. 2020 May 21;15(5):e0231308. doi: 10.1371/journal.pone.0231308.r006

Author response to Decision Letter 2

6 Mar 2020

Thank you for your comments.

1.We have added the limitation of the effects of anti-diabetic agents and anti-hypertensive agents on our results in Discussion.(see Paragraph 1 in Page 19 of the resubmitted manuscript with tracked changes)

2.We have revised some mistakes as you suggested in [additional].

Regards.

Attachment

Submitted filename: Supplemental file for review.doc

Click here for additional data file.^{(25.5KB, doc)}

PLoS One. doi: 10.1371/journal.pone.0231308.r007

Decision Letter 3

Mauro Lombardo

23 Mar 2020

Mid-upper Arm Circumference as a Simple Tool for Identifying Central Obesity and Insulin Resistance in Type 2 Diabetes

PONE-D-19-19096R3

Dear Dr. Yao,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Mauro Lombardo

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: Dear authors,

I am very glad to see your modified article. Your subimission should be accepted.

I would like you to perform this investigation with large cohort or patients at least not medicated by anti-diabeteic agents in future. Based on this pilot study, I hope this new available method is known widely and finally become routine tests in patients with type 2 diabetes by the future study from you and co-workers.

Sincerely,

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0231308.r008

Acceptance letter

Mauro Lombardo

4 May 2020

PONE-D-19-19096R3

Mid-upper Arm Circumference as a Simple Tool for Identifying Central Obesity and Insulin Resistance in Type 2 Diabetes

Dear Dr. Yao:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Mauro Lombardo

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Hypoglycemic agents.

(DOCX)

Click here for additional data file.^{(14KB, docx)}

S2 Table. Hypotensive agents.

(DOCX)

Click here for additional data file.^{(13.9KB, docx)}

S3 Table. Lipid-lowering agents.

(DOCX)

Click here for additional data file.^{(13.7KB, docx)}

S4 Table. Uric-acid-lowering agents.

(DOCX)

Click here for additional data file.^{(13.8KB, docx)}

Attachment

Submitted filename: Comments to Editor.docx

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Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

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

Yanhua Zhu

Qiongyan Lin

Yao Zhang

Hongrong Deng

Xiling Hu

Xubin Yang

Bin Yao

Roles

Abstract

Background

Materials

Results

Conclusions

Introduction

Materials and methods

Study population

Anthropometric measures

Blood biochemical assays

Statistical analysis

Results

Baseline characteristics

Table 1. General characteristics of study subjects.

Table 2. Characteristics of subjects according to mid-upper arm circumference tertiles.

Correlation of mid-upper arm circumference with central obesity parameters, metabolic variables and insulin resistance

Fig 1. Relationship between MUAC (cm) and other anthropometric measurements in all subjects.

Table 3. Relationship between MUAC and other measurements by gender.

Table 4. Relationship between HOMA-IR and other anthropometric measurements by gender.

Table 5. Linear regression analysis of logHOMA-IR with different clinical characteristics.

Table 6. Logistic regression analysis of risk factors for central obesity.

Optimal cut-off points of mid-upper arm circumference for central obesity

Fig 2. The receiver operating characteristic (ROC) curves for men and women to identify central obesity.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Mauro Lombardo

Roles

Author response to Decision Letter 0

Decision Letter 1

Mauro Lombardo

Roles

Author response to Decision Letter 1

Decision Letter 2

Mauro Lombardo

Roles

Author response to Decision Letter 2

Decision Letter 3

Mauro Lombardo

Roles

Acceptance letter

Mauro Lombardo

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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