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

Luisa Lampignano; Roberta Zupo; Rossella Donghia; Vito Guerra; Fabio Castellana; Isanna Murro; Carmen Di Noia; Rodolfo Sardone; Gianluigi Giannelli; Giovanni De Pergola

doi:10.1371/journal.pone.0241841

. 2020 Nov 5;15(11):e0241841. doi: 10.1371/journal.pone.0241841

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

Luisa Lampignano ¹, Roberta Zupo ¹, Rossella Donghia ¹, Vito Guerra ¹, Fabio Castellana ¹, Isanna Murro ², Carmen Di Noia ², Rodolfo Sardone ¹, Gianluigi Giannelli ³, Giovanni De Pergola ^2,^*

Editor: Michele Vacca⁴

PMCID: PMC7644056 PMID: 33152746

Abstract

Background

Body fat distribution influences the risk of cardio-metabolic disease in people with overweight. This study was aimed at identifying the anthropometric parameters more strongly associated with the majority of cardio-metabolic risk factors.

Methods

This study included 1214 subjects (840 women), with a body-mass-index (BMI) ≥ 25 Kg/m2, aged 39.2 ± 13 years. Fasting blood glucose (FBG), triglycerides (TG), total, HDL- and LDL-cholesterol, uric acid, vitamin D, high-sensitive C-reactive protein (hs-CRP), white blood cells (WBC), platelets, insulin and insulin resistance (HOMA-IR), systolic (SBP) and diastolic blood pressure (DBP), smoking habit and snoring were evaluated as cardio-metabolic risk factors.We also included the Systematic COronary Risk Evaluation (SCORE) to estimate cardiovascular risk in our study population. BMI, waist circumference (WC), waist-to-height-ratio (WHtR) and neck circumference (NC) were evaluated as anthropometric parameters.

Results

All four anthropometric parameters were positively associated to SBP, DBP, TG, FBG, insulin, HOMA-IR, WBC, and snoring (p<0.001), and negatively associated with HDL-cholesterol (p<0.001). NC showed a positive association with LDL-cholesterol (β = 0.76; p = 0.01; 95% C.I. 0.19 to 1.32), while vitamin D was negatively associated to WC (β = -0.16; p<0.001; 95% C.I. -0.24 to -0.09), BMI (β = 0.42); p<0.001; 95% C.I. -0.56 to -0.28) and WHtR (β = -24.46; p<0.001; 95% C.I. -37 to -11.9). Hs-CRP was positively correlated with WC (β = 0.003; p = 0.003; 95% C.I. 0.001 to 0.006), BMI (β = 0.01; p = 0.02; 95% C.I. 0.001 to 0.012) and WHtR (β = 0.55; p = 0.01; 95% C.I. 0.14 to 0.96). SCORE was associated to NC (β = 0.15; 95% CI 0.12 to 0.18; p<0.001), BMI (β = -0.18; 95% CI -0.22 to 0.14; p<0.001) and WHtR (β = 7.56; 95% CI 5.30 to 9.82; p<0.001).

Conclusions

NC, combined with BMI and WC or WHtR could represent an essential tool for use in clinical practice to define the cardio-metabolic risk in individuals with excess body weight.

Introduction

The obesity epidemic is recognized as one of the most important public health problems in the world today; in most European countries, the prevalence of overweight and obesity exceeds 60% [1]. Obesity has been defined as a risk factor for several cardiovascular (CV) risk factors, including hypertension, type II diabetes, and dyslipidemia [2], and shown to be responsible for higher morbidity and mortality rates in cardiovascular disease (CVD) [3]. Accordingly, a recent systematic review and meta-analysis examining 95 cohorts showed that obesity was associated with a nearly 60% higher prevalence of CVD, as compared to normal weight figures [4]. While U.S. CVD mortality rates have declined overall in the past decades, the rate of decline has recently decelerated, possibly due to the obesity epidemic, contributing to reverse the CVD progress previously obtained [5]. Several studies showed that a subgroup of subjects with obesity may be at significantly lower risk than usually estimated from obesity-related CVDs [6]. This subset has been described as Metabolically healthy obesity (MHO) [6]. Compared to patients with metabolically unhealthy obesity, individuals with MHO are distinguished by lower liver and visceral fat but higher subcutaneous leg fat content, higher cardiorespiratory fitness and physical activity, insulin sensitivity, lower levels of inflammatory markers, and normal adipose tissue function [6].

BMI is commonly used to define the diagnosis of obesity but alone, it is not sufficient to properly assess or manage the cardio-metabolic risk associated with increased adiposity in adults [7,8]. In fact, it is well known that body fat distribution (BFD) is more important than BMI in defining the CVD risk. In particular, despite decades of unequivocal evidence that waist circumference (WC) provides additional, independent information to BMI in predicting morbidity and risk of death, only recently was a suitable Consensus Statement proposed, advising routine measurement of WC by practitioners, as an important opportunity to improve patients management and health [8]. The same Consensus recommended that a decrease in waist circumference, and not in body weight or in BMI, is the most important treatment target, reducing adverse health risks in both men and women [8].

Interestingly, additional anthropometric measurements have been implemented to describe BFD and examine the relationship between BFD and CVD risk. It would be worthwhile verifying whether these parameters are more informative than WC in predicting the cardio-metabolic risk. Excluding methods requiring specific instruments (ultrasounds CT, MRI), the waist to hip ratio (WHR), waist to height ratio (WHtR) and neck circumference (NC) are alternative methods to WC which can be applied to examine anthropometric factors. However, WHR has been progressively abandoned since WC has been demonstrated to be more accurate in quantifying the CVD risk in patients with obesity [9].

NC is commonly utilized as an anthropometric marker to detect patients at higher risk of developing the obstructive sleep apnea syndrome (OSAS) [10], whereas few studies have used it to identify patients affected by metabolic disorders [11]. Recently, WHtR was suggested as a simpler indicator of abdominal obesity, with greater practical advantages than BMI and WC [12]. Moreover, some reviews highlighted the superiority of WHtR in predicting cardio-metabolic risks among adults and adolescents, while its interpretation can be applied to different ethnic groups and does not require sex-dependent or age-dependent cut-offs [13,14]. Despite this, other reviews showed no differences in predictive powers for CVD risk factors among the various anthropometric indices [15,16].

Only one study (the SOON cohort) has yet compared anthropometric parameters with the aim of identifying which is the best cardio-metabolic risk marker in subjects with obesity, and it showed that neck circumference was the most appropriate anthropometric marker [15]. However, this study investigated only women and only patients with severe obesity and, therefore, a population that is not representative of the whole population with overweight and obesity [14]. Moreover, it did not investigate WHtR [17].

To the best of our knowledge, no study has ever compared BMI, WC, WHtR and NC in relation to CV risk factors in a wide population of men and women affected by excess body weight. For this reason, the present study was focused on exploring the cross-sectional association among the most commonly used anthropometric parameters, namely BMI, WC, WHtR and NC and the CV risk factors mainly evaluated in clinical practice, such as systolic and diastolic blood pressure, and fasting glucose, lipid (triglycerides, total, HDL and LDL cholesterol), insulin, HOMA_IR [18], uric acid, 25-hydroxyvitamin D (25(OH)D) [19], C-reactive protein (CRP), white blood cells and platelets numbers in a population of 1214 apparently healthy subjects with overweight and obesity. We further explored the association of the anthropometric parameters using the Systematic COronary Risk Evaluation (SCORE), consisting in charts evaluating the cardiovascular risk by gender, age, total cholesterol, systolic blood pressure and smoking status, provided by the European Society of Cardiology [20].

Materials and methods

Study population and design

This cross-sectional study included 1214 consecutive patients (840 females and 374 males, aged 39.2 ± 13 years, all Caucasian) enrolled from January 2018 to December 2019 at the Outpatients Clinic of Nutrition of the Medical Oncology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari, School of Medicine, Policlinico, Bari, Italy and at the “Population Health Unit” of the National Institute of Gastroenterology "S. de Bellis," Research Hospital, Castellana Grotte, Apulia, Italy. Inclusion criteria were a condition of overweight or obesity (BMI ≥ 25 Kg/m2), and taking no medication, including oral contraceptives or drugs for osteoporosis. Exclusion criteria were any history of endocrinological diseases (diabetes mellitus, hypo or hyperthyroidism, hypopituitarism, etc.), chronic inflammatory diseases, stable hypertension, angina pectoris, stroke, transient ischemic attack, heart infarction, congenital heart disease, any malignancies, renal and liver failure, inherited thrombocytopenia.

Prior approval by the Institutional Review Board of the “National Institute of Gastroenterology “S. De Bellis” of this study protocol (Clinical Trial NCT04318288), with its measurements and data collections, was obtained in accordance with the 1964 Helsinki Declaration and subsequent revisions. All participants provided written informed consent to enter the study. The study adhered to the “Strengthening the Reporting of Observational Studies in Epidemiology” (STROBE) guidelines (https://www.strobe-statement.org/).

Clinical, anthropometric and biochemical parameters assessment

All subjects were closely examined for medical history, hormonal, metabolic and routine hematochemical parameters. Extemporaneous ambulatory diastolic (DBP) and systolic blood pressure (SBP) was determined in a sitting position after at least a 10-min rest, three different times, using an OMRON M6 automatic Blood Pressure monitor. The final values of blood pressure (SBP and DBP) were the mean of the last two of three measurements.

All anthropometric measurements were taken with participants wearing lightweight clothing and no shoes. All variables were collected at the same time between 8:00 and 10:00 a.m., following an overnight fast. Height was measured to the nearest 0.5 cm using a wall-mounted stadiometer (Seca 711; Seca, Hamburg, Germany). Body weight was determined to the nearest 0.1 kg using a calibrated balance beam scale (Seca 711; Seca, Hamburg, Germany). BMI was calculated by dividing body weight (Kg) by the square of height (m²) and classified according to World Health Organization criteria for normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), grade I obesity (30.0–34.9 kg/m2), grade II obesity (35.0–39.9 kg/m2), and grade III obesity (≥40.0 kg/m2) [21]. Waist circumference (WC) was measured at the narrowest part of the abdomen, or in the area between the tenth rib and the iliac crest (minimum circumference). Neck circumference (NC) was measured below the laryngeal prominence and perpendicular to the long axis of the neck, and the minimal circumference was recorded to the nearest 0.1 cm [22]. Both circumferences were measured with a Seca 201 ergonomic circumference measuring tape. Waist to height ratio WHtR was calculated by dividing WC (cm) by height (cm) [23].

Blood samples were drawn between 08:00 h and 09:00 h after overnight fasting. Blood glucose (FBG), insulin, 25(OH)D, total cholesterol, high- and low-density lipoprotein (HDL, LDL) cholesterol, triglycerides, Red Blood Cells (RBC), White Blood Cells (WBC), platelets, uric acid, and high sensitive C-reactive protein (hsCRP) serum levels were assayed. Serum insulin concentrations were measured by radioimmunoassay (Behring, Scoppito, Italy). Serum 25(OH)D levels were quantified by chemiluminescence (Diasorin Inc., Stillwater, OK, USA) and all samples were analyzed in duplicate. Plasma glucose was determined using the glucose oxidase method (Sclavus, Siena, Italy), while the concentrations of plasma lipids (triglycerides, total cholesterol, HDL cholesterol) were quantified by automated colorimetric method (Hitachi; Boehringer Mannheim, Mannheim, Germany). LDL cholesterol was calculated by applying the Friedewald equation. Serum uric acid was measured by the URICASE/POD method implemented on an autoanalyzer (Boehringer Mannheim, Mannheim, Germany). Blood cell count was determined using an XT-2000i hematology analyzer (Sysmex, Dasit, Cornaredo, Italy). Hs-CRP was measured on a Cobas Integra 400 Plus using a latex particle-enhanced immunoturbidimetric assay following the manufacturer's instructions (Roche Diagnostics, Indianapolis, IN) [24]. Insulin resistance was assessed with the Homeostasis Model Assessment–Insulin Resistance (HOMA-IR) [25]. Smoking status was assessed on the single question “Are you a current smoker?”, categorized as yes or no. Snoring status was assessed by asking the partner or the patient under observation the question “do you usually snore?”, categorized as yes or no. Metabolic Syndrome was assessed with International Diabetes Federation (IDF) criteria [26] MHO was identified in people who had BMI of over 30, but they do not have Metabolic Syndrome [6].

Statistics

Mean and standard deviation (M±SD) for continuous variables, and frequency for categorical variables were used, as indices of centrality. Linear and logistic regression models, adjusted for age and sex, were used to evaluate the association between single variables and different anthropometric parameters. We applied Poisson regression to test the association with the ordinal dependent variable (SCORE). To explore the association between all anthropometric parameters and the risk of cardiovascular events, we used a stepwise regression method, applying backward selection of associated variables. This method also allowed us to estimate the association of all those variables independently from the mutual collinearity. For those patients below the age limit (40 years) we attributed a risk of "0" for this calculator. When testing the null hypothesis, the probability level of α error was 0.05, two-tailed. All statistical analyses were performed using STATA 16, StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC.

Results and discussion

Table 1 shows the general, anthropometric, hormone, metabolic and routine biochemical characteristics of the enrolled population. Our study population consisted of 31% men,mean age was about 40 years (range 14–70 years) and mean BMI was 34 kg/m²(range 25–64.4).

Table 1. Characteristics of the study population (n = 1214).

Parameters*		Range (min–max)
Sex (M) (%)	375 (30.89)	--
Age (years)	39.50±12.61	14.00–74.00
Smoking (Yes) (%)	246 (20.26)	--
Snoring (Yes) (%)	649 (53.46)	-
Neck (cm)	41.01±4.01	30.00–57.00
BMI (Kg/m²)	33.89±6.08	25.00–64.60
Waist (cm)	108.57±14.09	69.00–158.00
Waist to Height Ratio	0.66±0.08	0.25–1.12
DBP (mmHg)	81.33±9.85	55.00–120.00
SBP (mmHg)	125.76±14.41	90.00–180.00
Total Cholesterol (mg/dL)	192.49±38.89	51.00–372.00
Triglyceride (mg/dL)	107.49±61.49	23.00–541.00
FBG (mg/dL)	91.90±12.55	65.00–125.00
HDL Cholesterol (mg/dL)	48.35±12.47	19.00–116.00
Insulin (mg/dL)	22.82±16.18	2.40–128.00
HOMA-IR	5.29±4.11	0.52–34.70
LDL Cholesterol (mg/dL)	123.40±33.75	23.00–262.00
Platelets (10³/μL)	264.49±59.43	249–368.00
WBC (10³/μL)	7.05±1.63	3.26–13.44
Vitamin D (ng/dL)	19.16±5.81	4.00–50.40
hs-CRP (mg/dL)	0.45±0.45	0.00–6.30

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* Mean and standard deviation (M±SD) for continuous variables. Percentage (%) for categorical variables.

Abbreviations: BMI, Body Mass Index; DBP, Diastolic Blood Pressure; SBP, Systolic Blood Pressure; FBG, Fasting Bllod Glucose; HDL, Hight Density Lipoprotein; HOMA-IR, Homeostasis Model Assessment-Insulin Resistance; LDL, Low Density Lipoprotein; WBC, White Blood Cell; hs-CRP, high sensitive C-Reactive Protein.

Table 2 shows the association between each single anthropometric parameter with the different variables investigated in the study, evaluated by linear and/or logistic regression models after adjustment for age and gender. All four anthropometric parameters were positively associated to DBP, SBP, triglycerides, FBG, insulin, HOMA-IR, WBC, and negatively associated with HDL-cholesterol (p<0.001). Only NC showed a positive association with LDL-cholesterol (p = 0.01), while Vitamin D was negatively associated to WC (p<0.001), BMI (p<0.001) and WHtR (p<0.001), but not to NC. Lastly, CRP was positively correlated with WC (p = 0.003), BMI (p = 0.02) and WHtR (p = 0.01), but not with NC. At logistic regression, all the anthropometric parameters were positively associated with snoring (p<0.0001). No further associations were evident in the models.

Table 2. Multivariate regression models ^# between continuous ^§ and categorical ^ψ variables and anthropometric parameters.

NC (cm)

BMI (Kg/m²)

WC (cm)

WHtR

Parameters ^*

p-value

C.I. (95%)

p-value

C.I. (95%)

p-value

C.I. (95%)

p-value

C.I. (95%)

Continuous Variables ^§

DBP (mmHg)

0.40

<0.001

0.23 to 0.57

0.16

<0.001

0.07 to 0.24

0.09

<0.001

0.05 to 0.13

11.96

<0.001

5.98 to 17.94

SBP (mmHg)

0.58

<0.001

0.33 to 0.84

0.35

<0.001

0.23 to 0.47

0.16

<0.001

0.11 to 0.22

22.06

<0.001

13.43 to 30.70

Total Cholesterol (mg/dL)

0.49

0.14

-0.16 to 1.14

0.02

0.89

-0.30 to 0.35

0.04

0.59

-0.11 to 0.19

9.89

0.41

-13.45 to 33.23

Triglyceride (mg/dL)

3.67

<0.001

2.64 to 4.70

2.04

<0.001

1.52 to 2.56

0.88

<0.001

0.65 to 1.11

132.64

<0.001

95.48 to 169.79

FBG (mg/dL)

0.58

<0.001

0.37 to 0.78

0.32

<0.001

0.21 to 0.42

0.14

<0.001

0.09 to 0.19

19.74

<0.001

12.13 to 27.35

HDL (mg/dL)

-0.92

<0.001

-1.14 to -0.71

-0.42

<0.001

-0.53 to -0.32

-0.19

<0.001

-0.24 to -0.15

-28.84

<0.001

-36.16 to -21.53

Insulin (mg/dL)

1.80

<0.001

1.54 to 2.06

1.10

<0.001

0.97 to 1.24

0.45

<0.001

0.39 to 0.51

65.75

<0.001

56.00 to 75.51

HOMA-IR

0.46

<0.001

0.39 to 0.53

0.27

<0.001

0.24 to 0.31

0.11

<0.001

0.10 to 0.13

16.11

<0.001

13.60 to 18.62

LDL Cholesterol (mg/dL)

0.76

0.01

0.19 to 1.32

0.07

0.63

-0.22 to 0.37

0.07

0.25

-0.05 to 0.20

13.44

0.20

-7.33 to 34.22

Platelets (10³/μL)

1.11

0.09

-0.17 to 2.39

0.96

0.001

0.39 to 1.54

0.40

0.002

0.14 to 0.65

56.54

0.007

15.74 to 97.33

WBC (10³/μL)

0.04

0.02

0.01 to 0.08

0.02

0.01

0.01 to 0.04

0.01

0.004

0.003 to 0.019

1.41

0.02

0.21 to 2.60

Vitamin D (ng/dL)

-0.32

0.12

-0.74 to 0.09

-0.42

<0.001

-0.56 to -0.28

-0.16

<0.001

-0.24 to -0.09

-24.46

0.001

-37.00 to -11.93

hs-CRP (mg/dL)

0.005

0.27

-0.004 to 0.014

0.01

0.02

0.001 to 0.012

0.003

0.001 to 0.006

0.55

0.01

0.14 to 0.96

Neck (cm)

BMI (Kg/m²)

Waist (cm)

Waist to Height Ratio

Parameters ^*

p-value

C.I. (95%)

p-value

C.I. (95%)

p-value

C.I. (95%)

p-value

C.I. (95%)

Dichotomic Variables ^ψ

Snoring

1.16

<0.001

1.11 to 1.21

1.09

<0.001

1.06 to 1.11

1.04

<0.001

1.03 to 1.05

217.23

<0.001

43.36 to 1088.34

Smoking

1.01

0.66

0.96 to 1.06

1.01

0.56

0.98 to 1.03

1.00

0.34

0.99 to 1.01

1.96

0.40

0.41 to 9.31

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^§ Multivariate Linear Regression Model

^ψ Multivariate Logistic Regression Model.

^# Adjusted for Age and Gender.

* Abbreviations: β, coefficient; C.I., Coefficient Interval at 95%; OR, Odds Ratios; NC, neck circumference; BMI, Body Mass Index; WC, waist circumference; WHtR, waist to height ratio; DBP, Diastolic Blood Pressure; SBP, Systolic Blood Pressure; FBG, Fasting Blood Glucose; HDL, HighDensity Lipoprotein; HOMA-IR, Homeostasis Model Assessment-Insulin Resistance; LDL, Low Density Lipoprotein; WBC, White Blood Cell; hs-CRP, high sensitive C-Reactive Protein.

Table 3 shows the association between the SCORE and all anthropometric parameters evaluated (A), also applying a backward stepwise method for SCORE on all variables included together in the model (B). In the final model the SCORE was significantly associated with NC (β = 0.15; 95% CI 0.12 to 0.18; p<0.001), followed by BMI (β = -0.18; 95% CI -0.22 to 0.14; p<0.001) and WHtR (β = 7.56; 95% CI 5.30 to 9.82; p<0.001). We also performed the same models in two different subgroups of our study population: subjects with Metabolic Syndrome in (representing 36.9% of the total group) and people with MHO (48.3%). In the first subgroup (Table 4) SCORE was associated to NC (β = 0.06; 95% CI 0.01 to 0.10; p = 0.02), BMI (β = -0.11; 95% CI -0.15 to -0.06; p<0.001) and WC (β = 0.03; 95% CI 0.004 to 0.049; p = 0.02), while in the second subgroup (Table 5) SCORE was associated to NC (β = 0.14; 95% CI 0.09 to 0.19; p<0.001), BMI (β = -0.31; 95% CI -0.39 to -0.23; p<0.001) and WHtR (β = 10.11; 95% CI 6.47 to 13.74; p<0.001).

Table 3. Multivariate Poisson linear regression models of SCORE and all anthropometric parameters included in the model (A) Final model applying a backward stepwise method for SCORE on all variables included together in the model (B)-).

Parameters *	SCORE ^§
Parameters *	β	p-value	C.I. (95%)
A)
NC (cm)	0.10	<0.001	0.05 to 0.14
BMI (kg/m²)	-0.19	<0.001	-0.24 to -0.14
WC (cm)	0.02	0.05	-0.0004 to 0.0507
WHtR	6.51	0.001	2.82 to 10.19
B)
NC (cm)	0.15	<0.001	0.12 to 0.18
BMI (kg/m²)	-0.18	<0.001	-0.22 to -0.14
WHtR	7.56	<0.001	5.30 to 9.82

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* Abbreviations: β, coefficient; C.I., Coefficient Interval at 95%; SCORE, Systematic COronary Risk Evaluation; NC, neck circumference; BMI, Body Mass Index; WC, waist circumference; WHtR, waist to height ratio.

Table 4. Multivariate Poisson linear regression models of SCORE and all anthropometric parameters included in the model (A). Final model in stepwise method in backward of SCORE on all variables included together in the model (B) in subjects with Metabolic Syndrome (36.9%).

Parameters ^*	SCORE ^§
Parameters ^*	β	p-value	C.I. (95%)
A)
NC (cm)	0.05	0.15	-0.02 to 0.11
BMI (kg/m²)	-0.12	0.002	-0.19 to -0.04
WC (cm)	0.03	0.15	-0.01 to 0.06
WHtR	1.75	0.55	-3.97 to 7.48
B)
NC (cm)	0.06	0.02	0.01 to 0.10
BMI (kg/m²)	-0.11	<0.001	-0.15 to -0.06
WC (cm)	0.03	0.02	0.004 to 0.049

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Table 5. Multivariate Poisson linear regression models of SCORE and all anthropometric parameters included in the model (A). Final model in stepwise method in backward of SCORE on all variables included together in the model (B) in subjects with MHO (48.3%).

Parameters ^*	SCORE ^§
Parameters ^*	β	p-value	C.I. (95%)
A)
NC (cm)	0.09	0.03	0.01 to 0.17
BMI (kg/m²)	-0.31	<0.001	-0.40 to -0.21
WC (cm)	0.03	0.26	-0.02 to 0.07
WHtR	8.44	0.006	2.48 to 14.40
B)
NC (cm)	0.14	<0.001	0.09 to 0.19
BMI (kg/m²)	-0.31	<0.001	-0.39 to -0.23
WHtR	10.11	<0.001	6.47 to 13.74

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The present study, performed in a population of apparently healthy subjects but with overweight and obesity, with a high prevalence of MHO (48.3%), was aimed at identifying the anthropometric parameters most clearly associated with cardio-metabolic risk factors such as glucose, lipids, insulin, insulin resistance, vitamin D, hs-CRP, WBC and platelets count, blood pressure, smoking and snoring.

It showed that BMI, WC, WHtR are all very strongly associated with all the parameters studied. The only parameter showing a slight difference was NC; in fact, this parameter was the only one to show a significant, positive association to LDL-cholesterol, but also the only variable not to show a significant association with vitamin D and hs-CRP. Moreover, after a stepwise approach, also NC, followed by BMI and WHtR (or WC in people with Metabolic Syndrome) were associated to the SCORE, the official European cardiovascular disease risk assessment model. In particular, in our population, BMI was inversely associated with SCORE. This inverse association was confirmed also in MHO and Metabolic Syndrome subgroups. This seemingly inconsistent result could be due to the wide age range of our population (with a high proportion of subjects <40 years) and the lack of obesity indices among the parameters considered in the SCORE assessment (gender, age, total cholesterol, systolic blood pressure and smoking habit). Moreover, it is well known that BMI is not a good predictor of BFD [7], which is more important than BMI in defining the CVD risk [7]. Furthermore, even though the so-called obesity paradox in CVD has been well described, where CVD patients with obesity have a greater prognosis than their normal weight counterparts do [27], this paradox has not been confirmed when estimating the risk of having CVD [28].In fact, maintaining a healthy weight is one of the main indications in all guidelines for the prevention of CVDs, including those drawn up by the European Society of Cardiology [20]. For this reason, further studies, including an assessment of body composition, are needed to investigate this inverse association found in our population. To the best of our knowledge, the only study (the SOON cohort) which compared anthropometric parameters to identify the most appropriate cardio-metabolic risk marker in subjects with obesity found that NC was the best one [17]. However, firstly this study investigated only women and only patients with severe obesity and, therefore, a community that is not representative of the whole population with overweight and obesity [16]. Secondly, it included patients affected by hypertension and/or type 2 diabetes and/or OSAS, and taking drugs. Thirdly, in the SOON cohort WHtR was not examined [17]. When evaluating our findings that BMI, WC and WHtR showed a similar statistical power in association to cardio-metabolic risk factors, it is important to remember that BMI does not take into account BDF, as opposed to WC or WHtR. Therefore, both BMI and WC (or WHtR) should be measured in subjects with excess body weight. Moreover, although some studies showed that WHtR was more predictive of CVD than BMI and WC [13,14], the present study does not seem to confirm this finding. Thus, WHtR may be a useful further anthropometric parameter to define a patient with overweight or obesity, but does not seem to be superior to WC for this purpose. In addition, it is noteworthy that studies emphasizing the role of WHtR more than BMI and WC were performed in people from Asia and in Caucasian subjects [13,14]. On the other hand, studies in Western populations showed that WC is the best adiposity measure in predicting CVD risk factors [29,30], although a study of a Spanish Mediterranean population continued to support the BMI [31]. Moreover, a systematic review and meta-analysis of Caucasian populations, also assessing WHtR, concluded that WC was more strongly associated with CVD risk factors, and therefore recommended the use of WC in both the clinic and research studies [32]. In addition, NC, that is the anthropometric parameter commonly used in patients with snoring and suspected OSAS, was shown to have a strong statistical power such as BMI, WC and WHtR, in the stepwise model in association to the SCORE, but not in relationship to each single variable evaluated. Moreover, it was the only parameter not to show a significant correlation with vitamin D and CRP. Even though recent studies, performed in a Chinese population [33] and in young Spanish adults [34], showed that NC has the same power as waist circumference in identifying metabolic disorders and quantifying cardiovascular risk, it has been stated that an increased NC is positively associated with the metabolic syndrome factors; thus the risk of coronary heart disease is likely to increase [35]. However, we agree with Caro et al, who suggested that NC measurement may be an opportunity in clinical practice when it is difficult to measure WC [36]. Interestingly, in this study, NC was the only anthropometric parameter to show a significant correlation with total LDL-cholesterol, and this result is in line with a recent systematic review and meta-analysis [37].

On the basis of all our findings, NC, combined with BMI and WC or WHtR, should be used in clinical practice to quantify the cardio-metabolic risk in individuals with excess body weight. It is notable that several studies have suggested that both BMI and WC should be measured, since the full strength of the association between waist circumference with morbidity and mortality is observed only after adjustment for BMI [38,39]. Indeed, the Consensus by IAS and ICCR et al suggested that the measurement of both BMI and WC should be included when stratifying obesity- related health risk [8].

As regards the strong points of this study, in our opinion, the first is the consistency of the anthropometric data with most of the biomarkers evaluated, an evident sign of the internal validity of the study. Moreover, we examined a large population, involving 1214 subjects, who were not taking any medication that could interfere with anthropometric and biological markers. There are few studies available with all these features. On the other hand, we did not include WHR in this study, since unfortunately this parameter was not available in all the subjects under study. Two limitations of our study are the imbalance between the number of men and women, and the broad age range of the study population. These could be due to the fact that the subjects spontaneously presented to our clinic for reasons of excessive weight, thus representing a selection bias. Moreover, the SCORE, having an age range starting from 40 years, is not entirely applicable to our study population, that includes many young subjects. Another weak point of this study is that it was a cross-sectional investigation, while prospective studies should be carried out to state whether WHtR and NC add information as compared to WC and BMI.

Conclusion

In conclusion, the present study, performed in a large group of uncomplicated subjects with overweight or obesity, suggests that NC, combined with BMI and WC or WHtR could represent essential tools for use in clinical practice to define the cardio-metabolic risk in individuals with excess body weight. To confirm these findings, the same measures should be used in a prospective study, assessing standardized populations, and with standardized outcomes such as death or cardiovascular disorders, with a precise time-event relationship.

Data Availability

The Institutional Review Board of the “National Institute of Gastroenterology “S. De Bellis” (at Istituto Tumori "Giovanni Paolo II" I.R.C.C.S) states that it contains potentially identifying and sensitive patient information. For this reason, any request may be addressed directly to the IRB comitatoetico@oncologico.bari.it.

Funding Statement

The authors received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0241841.r001

Decision Letter 0

Michele Vacca

21 Jul 2020

PONE-D-20-14485

Cross-Sectional Relationship Among Different Anthropometric Parameters and Cardio-metabolic Risk Factors in a Cohort of Patients with Overweight or Obesity

PLOS ONE

Dear Dr. De Pergola, Dear Giovanni,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. You'll find below the comments of the reviewers and this Editor. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Academic Editor

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Dear Authors,

please accept our apologises for the slight delay in the revision process.

The editorial team has extensively reviewed the manuscript and the reviewers and myself see merit in the data; however additional analyses have been suggested (that are largely reasonable) and it has been suggested that you invest more efforts in clearly pointing to the novelty of your data: as the reviewer 2 has pointed there is extensive literature in the filed and, despite PlosOne is more interested to scientific rigour than novelty, better defining what the angle that the authors consider more novel is really important. It is also important to clearly state what are the limitations of the study in the discussion.

Moreover, I think that the reviewers have provided useful suggestions: given the young age of the population and the relatively mild disease, we expect that the number of CVD cases will be really limited so as the manuscript is, it is difficult to dissect the "cardio" from the "metabolic" angle; however, as the reviewer 1 has suggested, CVR can be estimated using Framingham or Progetto Cuore. There will be a proportion of patients falling below the age limits (30 and 35 yo, respectively) for these calculators but those patients can be either excluded from the calculations or be attributed a risk of "0" (assuming the authors clearly state in the methods how missing data are treated). Also, NAFLD is an indipendent CVD risk factor: is liver US available for those subjects? Otherwise the authors can consider to use the Fatty Liver Index or similar risk scores to provide an "estimate".

Please add the beta coefficient in the abstract when discussing the multivariate results. There is only the C.I. and P at the moment.

I think that most of the suggestions of the reviewers are easily addressable. We thus look forward for receiving a revised version of the manuscript with those addressed.

Best Wishes

Michele Vacca

Reviewers' comments:

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Reviewer #2: Yes

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Reviewer #2: I Don't Know

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Reviewer #2: No

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Reviewer #1: The manuscript by Lampignano and colleagues reports the results of a cross-sectional study investigating the correlation between anthropometric parameters of obesity/adiposity (i.e. neck circumference, BMI, waist circumference, waist-to-height-ratio) with clinical parameters associated with incident risk of cardiovascular events. By studying a large cohort (n=1214) of overweight and obese individuals, the authors have identified significant age- and gender-adjusted associations between the investigated anthropometric parameters and blood pressure, lipid profile, insulin and HOMA index, leukocyte counts and snoring. The authors conclude that “BMI and WC seem to be the essential anthropometric parameters for use in clinical practice to quantify the cardio-metabolic risk in individuals with overweight and obesity” (as per abstract).

The topic of the study has clinical relevance. It investigates anthropometric parameters, which are easy to implement in clinical practice, with the aim of better grading the cardiometabolic risk in obese patients. A clear strength of the study is the recruitment of a large cohort which is very well characterized for cardiometabolic parameters. However, some aspects limit the current submission and could be addressed to improve the manuscript. Namely:

1) The authors conclude that “…BMI and WC seem to be the essential anthropometric parameters for use in clinical practice to quantify the cardio-metabolic risk…”. However, the current study is only observational and exploited pair-wise associations – yet, age- and gender-corrected - between the anthropometric and cardiovascular parameters. How did the authors identify BMI and WC (among the investigated parameters) as the “essential ones”? Analyses and comparison of the strength of associations with the clinical parameters could be performed to support this conclusion.

2) The authors have assessed pairwise correlations with clinical parameters which are associated with incidence of future cardiovascular events. It would be interesting to know whether these anthropometric parameters also associate with the cardiovascular risk of these patients computed using standardized algorithms (e.g. FRS, SCORE, “Progetto Cuore”). Moreover, the individuation of the antropometric parameter with the strong association (thus, potentially more relevant for clinical application) would significantly improve the findings of the authors.

3) Is there collinearity among the tested parameters? If collinearity is not relevant, the authors could also consider enter all the anthropometric parameters into the regression models (also with a stepwise approach) to verify whether some of them could independently associate with the clinical parameters (and/or the estimated cardiovascular risk, see point #2).

4) Which is the percentage of patients fulfilling the diagnostic criteria for Metabolic Syndrome and Metabolically healthy obesity? Would the association still be significant in these two subpopulations?

5) ll.87-88: HOMA-IR and 25-OH-VitD are included among the parameters associated with CV risk. A reference should be included here.

Reviewer #2: The authors present a cross-sectional analysis of four anthropometric indices of body fat distribution and a range of cardiometabolic risk factors amongst over 12 hundred overweight/obese patients. The study aimed to identify the anthropometric parameter(s) most closely associated with cardiometabolic risk, and therefore best predictive of cardiovascular disease. Using a combination of linear and logistic regression models for each anthropometric parameter, the authors demonstrated that BMI, waist circumference, weight-height ratio and neck circumference were positively associated with the majority of cardiometabolic variables, including blood pressure, fasting glucose and insulin/HOMA-IR. A number of other associations were explored. The authors concluded that BMI and WC are the most appropriate anthropometric indices to use in clinical practice to quantify cardiovascular risk.

This is a solid dataset of drug-naïve individuals with overweight or obesity and no documented cardiac or metabolic disease. All individuals have undergone comprehensive anthropometric and biochemical evaluation. The selection and determination of analytes is appropriate. The study is relatively small (n= 1214) for the question being asked, however it is appropriately designed to meet the stated objectives. The authors have used simple linear regression (or logistic regression for snoring/smoking) to model the relationship between anthropometric indices and cardiometabolic risk factors. Confounders such as age and sex were appropriately adjusted for. The conclusions drawn are consistent with the results of these analyses.

Major issues (in order of importance)

1. The use of anthropometric indices of body fat distribution to identify healthy individuals at increased cardiometabolic risk has been widely evaluated in a range of studies, most of them cross-sectional, many significantly larger that this one, and collectively spanning a range of ethnic groups. A subset of these studies are referenced in this paper. Many of these studies have demonstrated that measurement of waist circumference, waist-hip ratio or weight-height ratio affords superior predictive value than BMI alone. I am not therefore convinced that this study adds usefully to the existing literature.

2. Given the question being asked and the clinical context in which anthropometric measurements are used, the use of receiver operating characteristic (ROC) analyses would be appropriate to evaluate the discriminatory power of BMI, WC, WtHR and NC.

3. More information should be presented about the study participants. What was the ethnic composition? What was the age range? Indeed, it is not clearly stated that all participants were adults. What was the BMI range? For many of these parameters, simply reporting mean and SD is insufficient and makes it difficult have confidence in the statistical approach.

4. As the authors already point out, this is a cross sectional study that does not capture cardiovascular outcomes.

Minor issues

1. Graphical representations of the data/regression models would enhance the manuscript.

2. Depending on the BMI distribution of the participants, it would interesting to see if the models hold up at the higher BMI ranges (e.g. BMI >30).

3. The male/female imbalance in this study is unexpected. The authors have not commented on potential reasons for this. There is an opportunity here to repeat the analyses in males vs females and see if any differences arise.

4. There are some sections, particularly in the discussion, in which the wording could be improved to improve clarity.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2020 Nov 5;15(11):e0241841. doi: 10.1371/journal.pone.0241841.r002

Author response to Decision Letter 0

30 Aug 2020

Answers to Reviewer #1

Firstly, we would like to thank the reviewer for having carefully reviewed our manuscript and for the useful advice given.

We totally agree with the reviewer. For this purpose, we extensively modified the text, in the light of the new analysis performed. Please read the next points, in which all the changes made are explained

2) The authors have assessed pairwise correlations with clinical parameters which are associated with incidence of future cardiovascular events. It would be interesting to know whether these anthropometric parameters also associate with the cardiovascular risk of these patients computed using standardized algorithms (e.g. FRS, SCORE, “Progetto Cuore”). Moreover, the individuation of the anthropometric parameter with the strong association (thus, potentially more relevant for clinical application) would significantly improve the findings of the authors.

2-3. For this purpose, we estimated cardiovascular risk by the SCORE Risk Charts and we combined all the anthropometric parameters into the regression models with a stepwise approach to verify whether some of them could be independently associated with the SCORE Risk Charts. We used these charts because they are the most commonly used, and validated in European populations. We also decided to add and discuss the new results of this analysis in our manuscript (see Result section lines 193-197 and Discussion section lines 210-211 and 213-219).

4. The prevalence of Metabolic Syndrome was 36.9% and prevalence of MHO (obese subject without Metabolic Syndrome) was 48.3%. We have also carried out the analyses in the sub-groups you mentioned. Please find the results shown at the end of this document (Table rev1a and Table rev1b)

5) ll.87-88: HOMA-IR and 25-OH-VitD are included among the parameters associated with CV risk. A reference should be included here.

5. We agree with the reviewer. Two references have been inserted:

- regarding HOMA-IR, in line 86 (17) Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):122. Published 2018 Aug 31. doi:10.1186/s12933-018-0762-4

- regarding Vitamin D, in line 87 (18) Muscogiuri G, Annweiler C, Duval G, et al. Vitamin D and cardiovascular disease: From atherosclerosis to myocardial infarction and stroke. Int J Cardiol. 2017;230:577-584. doi:10.1016/j.ijcard.2016.12.053

Table rev1a. Univariate linear regression models of SCORE and anthropometric parameters in patients with Metabolic Syndrome.

Parameters * SCORE §

β p-value C.I. (95%)

Neck (cm) 0.03 0.20 -0.01 to 0.07

BMI (kg/m²) -0.04 0.001 -0.07 to -0.02

Waist (cm) -0.005 0.34 -0.01 to 0.005

Waist to Height Ratio -2.02 0.02 -3.70 to -0.34

§ Poisson regression Model.

* Abbreviations: β, coefficient; C.I., Coefficient Interval at 95%; Systematic COronary Risk Evaluation (SCORE); FLI, BMI, Body Mass Index; BMI, Body Mass Index.

Table rev1b. Univariate linear regression models of SCORE and anthropometric parameters in MHO patients

Parameters * SCORE §

β p-value C.I. (95%)

Neck (cm) 0.06 0.04 0.002 to 0.113

BMI (kg/m²) -0.14 <0.001 -0.20 to -0.08

Waist (cm) 0.005 0.53 -0.01 to 0.02

Waist to Height Ratio 0.26 0.84 -2.32 to 2.84

§ Poisson regression Model.

* Abbreviations: β, coefficient; C.I., Coefficient Interval at 95%; Systematic COronary Risk Evaluation (SCORE); BMI, Body Mass Index; BMI, Body Mass Index.

Answers to Reviewer #2

Firstly, we would like to thank the reviewer for having accurately reviewed our paper and for giving useful advices.

Major issues:

1. The use of anthropometric indices of body fat distribution to identify healthy individuals at increased cardiometabolic risk has been widely evaluated in a range of studies, most of them cross-sectional, many significantly larger than this one, and collectively spanning a range of ethnic groups. A subset of these studies are referenced in this paper. Many of these studies have demonstrated that measurement of waist circumference, waist-hip ratio or weight-height ratio affords superior predictive value than BMI alone. I am not therefore convinced that this study adds usefully to the existing literature.

We appreciate the frank comment. However, at variance with most of the other papers, we simultaneously examined 4 anthropometric parameters, and NC had the strongest association with SCORE. In addition, this study was performed in a specific population of people with overweight or obesity, none of which was taking any kind of drug. The sum of these four aspects makes this study quite original.

Unfortunately, we cannot proceed with the ROC analysis because of the design of our study, since we do not have a dichotomic variable as outcome.

We have added ranges to Table 1. Moreover, we have added the ethnic composition in the methods section (line 98, “all Caucasian”) and the range information you requested in the Results section (lines 168-170) and Table 1 “Table 1 shows the general, anthropometric, hormone, metabolic and routine biochemical characteristics of the enrolled population. As reported in the new text, our study population consisted of 31% men, mean age was about 40 years (range 14-70 years) and mean BMI was 34 kg/m2 (range 25-64.4)”

Minor issues:

1. Graphical representations of the data/regression models would enhance the manuscript.

Unfortunately, we cannot display graphic output of the analysis since they are linear regression models.

2. Depending on the BMI distribution of the participants, it would be interesting to see if the models hold up at the higher BMI ranges (e.g. BMI >30).

Below we show the table that explains the models considering only those subjects with a BMI over 30. All the models hold up, except those concerning inflammation parameters. The table (Table rev2) is shown at the end of this document.

We have added some comments on this imbalance in the final part of the discussion section (lines 263-266): “Two limitations of our study are the imbalance between the number of men and women and the broad age range of the study population. These could be due to the fact that the subjects spontaneously presented to our clinic for reasons of excessive weight, thus representing a selection bias”

Moreover, all association models were adjusted for gender. This means that all associations were estimated ceteris paribus of gender.

4. There are some sections, particularly in the discussion, in which the wording could be improved to improve clarity.

Following your advice, we have proceeded to have the manuscript again corrected by our native English speaker collaborator.

Table rev2. Multivariate regression models # between continuous § and categorical ψ variables and anthropometric parameters in patients with BMI>30.

Neck (cm) BMI (Kg/m2) Waist (cm) Waist to Height Ratio

Parameters * β p-value C.I. (95%) β p-value C.I. (95%) β p-value C.I. (95%) β p-value C.I. (95%)

Continuous Variables §

DBP (mmHg) 0.35 0.001 0.13 to 0.57 0.13 0.02 0.02 to 0.25 0.10 0.001 0.04 to 0.14 10.18 0.01 2.47 to 17.89

SBP (mmHg) 0.52 0.001 0.21 to 0.82 0.37 <0.001 0.21 to 0.53 0.17 <0.001 0.10 to 0.24 20.60 <0.001 9.53 to 31.64

Total Cholesterol (mg/dL) 0.40 0.32 -0.39 to 1.19 -0.05 0.82 -0.49 to 0.39 -0.001 0.99 -0.19 to 0.19 4.42 0.77 -25.35 to 34.20

Triglyceride (mg/dL) 3.55 <0.001 2.20 to 4.90 2.02 <0.001 1.30 to 2.75 0.85 <0.001 0.54 to 1.17 117.65 <0.001 68.42 to 166.88

FBG (mg/dL) 0.57 <0.001 0.29 to 0.84 0.20 0.01 0.04 to 0.35 0.10 0.003 0.03 to 0.16 11.63 0.02 1.44 to 21.82

HDL (mg/dL) -0.88 <0.001 -1.13 to -0.64 -0.33 <0.001 -0.47 to -0.20 -0.16 <0.001 -0.22 to -0.10 -21.21 <0.001 -30.34 to -12.07

Insulin (mg/dL) 1.83 <0.001 1.46 to 2.21 1.11 <0.001 0.91 to 1.31 0.43 <0.001 0.34 to 0.52 57.55 <0.001 43.66 to 71.44

HOMA-IR 0.47 <0.001 0.37 to 0.57 0.27 <0.001 0.22 to 0.32 0.10 <0.001 0.08 to 0.13 14.00 <0.001 10.41 to 17.58

LDL Cholesterol (mg/dL) 0.64 0.08 -0.08 to 1.37 -0.16 0.44 -0.55 to 0.24 -0.005 0.95 -0.18 to 0.17 0.03 0.99 -26.62 to 26.67

Platelets (103/µL) 1.06 0.17 -0.45 to 2.57 0.82 0.04 0.04 to 1.61 0.35 0.04 0.01 to 0.69 38.12 0.15 -14.44 to 90.69

WBC (103/µL) 0.05 0.02 0.01 to 0.09 0.02 0.10 -0.004 to 0.041 0.01 0.06 -0.0003 to 0.019 0.90 0.23 -0.59 to 2.39

Vitamin D (ng/dL) -0.01 0.94 -0.37 to 0.35 -0.17 0.12 -0.39 to 0.05 -0.06 0.22 -0.16 to 0.04 -3.77 0.62 -18.69 to 11.14

CRP (mg/dL) 0.003 0.67 -0.01 to 0.02 0.006 0.13 -0.002 to 0.014 0.003 0.05 -0.0004 to 0.007 0.49 0.07 -0.03 to 1.01

Neck (cm) BMI (Kg/m2) Waist (cm) Waist to Height Ratio

Parameters * OR p-value C.I. (95%) OR p-value C.I. (95%) OR p-value C.I. (95%) OR p-value C.I. (95%)

Dichotomic Variables ψ

Snoring 1.17 <0.001 1.11 to 1.25 1.10 <0.001 1.06 to 1.13 1.04 <0.001 1.03 to 1.05 341.58 <0.001 45.94 to 2539.82

Smoking 1.03 0.29 0.97 to 1.09 1.01 0.45 0.98 to 1.04 1.00 0.38 0.99 to 1.02 2.50 0.39 0.31 to 19.93

§ Multivariate Linear Regression Model; ψ Multivariate Logistic Regression Model.

# Adjusted for Age and Gender.

* Abbreviations: β, coefficient; C.I., Coefficient Interval at 95%; OR, Odds Ratios; BMI, Body Mass Index; IMT, Intima-media thickness; DBP, Diastolic Blood Pressure; SBP, Systolic Blood Pressure; FBG, Fasting Blood Glucose; HDL, Hight

Density Lipoprotein; HOMA-IR, Homeostasis Model Assessment-Insulin Resistance; LDL, Low Density Lipoprotein; WBC, White Blood Cell; CRP, C-Reactive Protein.

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PLoS One. doi: 10.1371/journal.pone.0241841.r003

Decision Letter 1

Michele Vacca

5 Oct 2020

PONE-D-20-14485R1

Cross-Sectional Relationship Among Different Anthropometric Parameters and Cardio-metabolic Risk Factors in a Cohort of Patients with Overweight or Obesity

PLOS ONE

Dear Prof. De Pergola, Dear Giovanni,

Thank you for submitting your manuscript to PLOS ONE. Please accept my apologise for the delay but, to ensure that your manuscript was seen by the original reviewers, we had to wait for one of them that asked for an extension of the deadline.

I am happy to confirm that the reviewers have found the manuscript largely improved. However, the Reviewer 1 has still a couple of minor (but important) queries that should not take long for you to address before the manuscript can be formally accepted. Therefore, we invite you to submit a revised version of the manuscript that addresses these comments: we will then proceed with an expedite acceptance at the next round.

Please submit your revised manuscript by Nov 19 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're 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.

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We look forward to receiving your revised manuscript.

Kind regards,

Michele Vacca, M.D., Ph.D.

Academic Editor

PLOS ONE

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

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: (No Response)

Reviewer #2: (No Response)

**********

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

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

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

Reviewer #1: (No Response)

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

The revised manuscript is more complete and includes new analyses for evaluating the association of anthropometric parameters with the SCORE risk chart categories of cardiovascular risk. The overall manuscript is improved however there are some aspects that would require further clarification:

1) The authors have employed Poisson regression to regress the score risk category with the anthropometric parameters (new table 3). The authors conclude “… when associated to the SCORE, the official European cardiovascular disease risk assessment model, NC had the strongest association (beta=0.15; 95% CI 0.12 to 0.18; p<0.001), followed by BMI (beta=-0.18; 95% CI -0.22 to 0.14; p<0.001) and WHtR (beta=7.56; 95% CI 5.30 to 9.82; p<0.001)…” (ll.221-223). It is unclear why the value showing the lowest absolute beta-coefficient is deemed as the one with the strongest association. Could the authors verify and clarify?

2) Another interesting aspect raising from the Poisson regression is the different direction of the association of the SCORE risk with NC (beta= +0.15) and BMI (beta=-0.18). This result is interesting, as BMI (and, thus, obesity) is usually associated with higher cardiovascular risk, while these results apparently suggest that lower BMI are associated with higher SCORE risk categories. Could the authors comment on these aspects?

3) The data on MHO and Metabolic Syndrome are not presented in the manuscript and the presented tables are apparently univariate associations. The authors could consider perform a multivariate regression (as in table 3) for the subgroups and perhaps include a short description in the manuscript. These data may be interesting for the readers

Reviewer #2: The authors have made some important changes to this manuscript in response to comments, the most notable of which was incorporation of a the previously-validated Systemic Coronary Risk Evaluation score, which adds another interesting (and novel) element to their work. In the original version, BMI and waist circumference were concluded to be the key anthropometric parameters for use in clinical practice, whereas the revised conclusion promotes neck circumference "in combination" with the other three parameters. Whilst the conclusion is modest, the analyses are carefully done and add usefully to the existing body of work seeking to identify robust anthropometric predictors of cardiovascular outcome.

All comments have been appropriately addressed, in particular the provision of useful information and summary data about the study population.

**********

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Reviewer #1: No

Reviewer #2: No

PLoS One. 2020 Nov 5;15(11):e0241841. doi: 10.1371/journal.pone.0241841.r004

Author response to Decision Letter 1

16 Oct 2020

Firstly, we would like to thank the reviewers for your suggestions and for the precious time spent reviewing our manuscript. We believe that we have improved our paper thanks to your indications.

In particular, here are the responses to the comments of the first reviewer.

The reviewer is right, we made a mistake in defining the NC as the one with the strongest association. Accordingly, we modified the relative parts of the text in the abstract, results and discussions sections where the concept was expressed.

2) Another interesting aspect arising from the Poisson regression is the different direction of the association of the SCORE risk with NC (beta= +0.15) and BMI (beta=-0.18). This result is interesting, as BMI (and, thus, obesity) is usually associated with higher cardiovascular risk, while these results apparently suggest that lower BMI are associated with higher SCORE risk categories. Could the authors comment on these aspects?

We have expanded the discussion about this finding. In fact, on page 13, lines 241-256 we stated “Moreover, after a stepwise approach, also NC, followed by BMI and WHtR (or WC in people with Metabolic Syndrome) were associated to the SCORE, the official European cardiovascular disease risk assessment model. In particular, in our population, BMI was inversely associated with SCORE. This inverse association was confirmed also in MHO and Metabolic Syndrome subgroups. This seemingly inconsistent result could be due to the wide age range of our population (with a high proportion of subjects <40 years) and the lack of obesity indices among the parameters considered in the SCORE assessment (gender, age, total cholesterol, systolic blood pressure and smoking habit). Moreover, it is well known that BMI is not a good predictor of BFD (7), which is more important than BMI in defining the CVD risk (7). Furthermore, even though the so-called obesity paradox in CVD has been well described, where CVD patients with obesity have a greater prognosis than their normal weight counterparts do (27) , this paradox has not been confirmed when estimating the risk of having CVD(28).In fact, maintaining a healthy weight is one of the main indications in all guidelines for the prevention of CVDs, including those drawn up by the European Society of Cardiology. (20) For this reason, further studies, including an assessment of body composition, are needed to investigate this inverse association found in our population”.

3) The data on MHO and Metabolic Syndrome are not presented in the manuscript and the presented tables are apparently univariate associations. The authors could consider performing a multivariate regression (as in table 3) for the subgroups and perhaps include a short description in the manuscript. These data may be interesting for the readers

As suggested by the reviewer, we added the required information and analysis in the new text. Furthermore we introduced the concept of MHO in the introduction (page 2, lines 48-53). “Several studies showed that a subgroup of subjects with obesity may be at significantly lower risk than usually estimated from obesity-related CVDs (6). This subset has been described as Metabolically healthy obesity (MHO) (6). Compared to patients with metabolically unhealthy obesity, individuals with MHO are distinguished by lower liver and visceral fat but higher subcutaneous leg fat content, higher cardiorespiratory fitness and physical activity, insulin sensitivity, lower levels of inflammatory markers, and normal adipose tissue function (6).”

PLoS One. doi: 10.1371/journal.pone.0241841.r005

Decision Letter 2

Michele Vacca

22 Oct 2020

Cross-Sectional Relationship Among Different Anthropometric Parameters and Cardio-metabolic Risk Factors in a Cohort of Patients with Overweight or Obesity

PONE-D-20-14485R2

Dear Prof. De Pergola, Dear Giovanni,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Well Done!

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Michele Vacca, M.D., Ph.D.

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: (No Response)

**********

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

Reviewer #1: Yes

**********

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

Reviewer #1: Yes

**********

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

Reviewer #1: (No Response)

**********

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

Reviewer #1: (No Response)

**********

6. Review Comments to the Author

Reviewer #1: The authors have satisfactorily addressed my comments. The revised manuscript is sensibly improved and better conveys the findings of the study.

**********

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

PLoS One. doi: 10.1371/journal.pone.0241841.r006

Acceptance letter

Michele Vacca

26 Oct 2020

PONE-D-20-14485R2

Cross-Sectional Relationship Among Different Anthropometric Parameters and Cardio-metabolic Risk Factors in a Cohort of Patients with Overweight or Obesity

Dear Dr. De Pergola:

I'm 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.

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Thank you for submitting your work to PLOS ONE and supporting open access.

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on behalf of

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Academic Editor

PLOS ONE

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Data Availability Statement

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PERMALINK

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

Luisa Lampignano

Roberta Zupo

Rossella Donghia

Vito Guerra

Fabio Castellana

Isanna Murro

Carmen Di Noia

Rodolfo Sardone

Gianluigi Giannelli

Giovanni De Pergola

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Materials and methods

Study population and design

Clinical, anthropometric and biochemical parameters assessment

Statistics

Results and discussion

Table 1. Characteristics of the study population (n = 1214).

Table 2. Multivariate regression models # between continuous § and categorical ψ variables and anthropometric parameters.

Table 3. Multivariate Poisson linear regression models of SCORE and all anthropometric parameters included in the model (A) Final model applying a backward stepwise method for SCORE on all variables included together in the model (B)-).

Table 4. Multivariate Poisson linear regression models of SCORE and all anthropometric parameters included in the model (A). Final model in stepwise method in backward of SCORE on all variables included together in the model (B) in subjects with Metabolic Syndrome (36.9%).

Table 5. Multivariate Poisson linear regression models of SCORE and all anthropometric parameters included in the model (A). Final model in stepwise method in backward of SCORE on all variables included together in the model (B) in subjects with MHO (48.3%).

Conclusion

Data Availability

Funding Statement

References

Decision Letter 0

Michele Vacca

Roles

Author response to Decision Letter 0

Decision Letter 1

Michele Vacca

Roles

Author response to Decision Letter 1

Decision Letter 2

Michele Vacca

Roles

Acceptance letter

Michele Vacca

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Cited by other articles

Links to NCBI Databases

Table 2. Multivariate regression models ^# between continuous ^§ and categorical ^ψ variables and anthropometric parameters.