Factor analysis for the clustering of cardiometabolic risk factors and sedentary behavior, a cross-sectional study.

BACKGROUND: Few studies have reported on the clustering pattern of CVD risk factors, including sedentary behavior, systemic inflammation, and cadiometabolic components in the general population.
OBJECTIVE: We aimed to explore the clustering pattern of CVD risk factors using exploratory factor analysis to investigate the underlying relationships between various CVD risk factors.
METHODS: A total of 5606 subjects (3157 male, 51.5±11.7 y/o) were enrolled, and 14 cardiovascular risk factors were analyzed in an exploratory group (n = 3926) and a validation group (n = 1676), including sedentary behaviors.
RESULTS: Five factor clusters were identified to explain 69.4% of the total variance, including adiposity (BMI, TG, HDL, UA, and HsCRP; 21.3%), lipids (total cholesterol and LDL-cholesterol; 14.0%), blood pressure (SBP and DBP; 13.3%), glucose (HbA1C, fasting glucose; 12.9%), and sedentary behavior (MET and sitting time; 8.0%). The inflammation biomarker HsCRP was clustered with only adiposity factors and not with other cardiometabolic risk factors, and the clustering pattern was verified in the validation group.
CONCLUSION: This study confirmed the clustering structure of cardiometabolic risk factors in the general population, including sedentary behavior. HsCRP was clustered with adiposity factors, while physical inactivity and sedentary behavior were clustered with each other.

Background

Despite significant advances over the past decades, atherosclerotic cardiovascular diseases (CVD) remain the most significant cause of mortality worldwide [1]. CVD is a complex disease, and decades of research has recognized that CVD risk factors are clustered in specific patterns imply common underlying disease processes [2]. Metabolic abnormalities such as central obesity, insulin resistance, dyslipidemia, and high blood pressure are highly involved in the pathogenesis of CVD and are recognized as metabolic syndrome. Metabolic syndrome has variable presentation and is considered as a high-risk factor for CVD [3]. However, with the evolving understanding of the pathophysiology of atherosclerosis, risk factors are being added to the ever-expanding battery of CV risk factors.

Sedentary behavior (SB) and physical activity (PA) has been established as an independent risk factor for CVD on its own [4-7]. The current professional guidelines recommend avoiding SB and maintaining adequate PA, which are considered major goals in public health policy [8]. SB has also been linked to reduced triglyceride metabolism, insufficient antioxidant production, and glucose intolerance in several animal studies [9]. The underlying pathophysiologic link between SB and other CVD risk factors are deeply intertwined. There has been a great number of studies demonstrating the association between SB and tradition CVD risk factors. For example, in the landmark NHANES 2003–2006 study. total sedentary time was detrimentally associated with several biomarkers including waist circumference, HDL-cholesterol, C-reactive protein, triglycerides, insulin, and insulin resistance. However, while this study demonstrated the close relationship between SB and inflammation, the information of other important risk factors including total cholesterol, UA, HbA1C, were missing [10]. In a later meta-analysis, Edwardson et al. demonstrated that patients with longer sedentary time have greater odds of having metabolic syndrome [11]. However, it is impossible to delineate the association between SB and a particular component of metabolic syndrome. Hence, although the importance of SB is undeniable, there have only been a few studies have investigated the clustering relationship between SB and other risk factors, and there is insufficient information to explain the variances of metabolic abnormalities observed [10-14]. Furthermore, the definition of SB has not been well established in previous studies [15].

Exploratory factor analysis is a statistical method of data reduction that allows investigators to overcome the analytical difficulty posed by the vast number of risk variables by demonstrating the underlying relationships between different risk factors [16]. Factor analysis has been performed on many different populations but PA and SB have not been included in most major studies [17-19]. In addition, CVD has long been considered a systemic inflammatory disease. Inflammatory biomarkers such as high-sensitive C-reactive protein (HsCRP) are used to reflect disease severity and guide treatment strategies [20, 21]. Therefore, the current study investigates the clustering relationship of SB, cardiometabolic components, and inflammatory biomarkers in the general population of Taiwan.

Methods

Study population

We derived our data from the VGH-HEALTHCARE study, which is a prospective cohort study to evaluate the impact of PA and SB on long-term outcomes [22]. Adult subjects who received a comprehensive health examination at the Healthcare Center of Taipei Veterans General Hospital from February 2015 to July 2019 were invited to join this study. In brief, the healthcare center of Taipei Veterans general hospital provides elective, self-paid health examination services to all individuals who wish to receive health examination to identify undetected conditions for primary prevention purposes. We included patients without significant symptoms or illness and excluded those who refused to participate, whose exam revealed an acute illness, or had a chronic condition that require regular follow up such as active cancer, heart failure, coronary artery disease or stroke. We believe that data from our study population could provide vital information on primary prevention from the general population perspective. Demographic data, biochemical blood tests, and information on PA and SB were collected.

VGH-HEALTHCARE is an ongoing prospective study, so information on long-term outcomes is currently not available. The present study serves as a cross-section analysis to investigate the relationship between baseline cardiometabolic factors, SB, and inflammation biomarkers. The enrolled subjects were divided into two groups with similar baseline characteristics, with 70% in the exploratory group and 30% in the validation group. All participants provided a written consent. This study was conducted in concordance with the Declaration of Helsinki and was approved by the Internal Research Board of Taipei Veterans General Hospital. All information was obtained after receiving informed consent from the study participants.

Clinical assessments and biochemical parameters

The baseline information included age, sex, body height, body weight, body mass index (BMI), waist circumference, monthly income, education level, alcohol drinking behavior, and smoking status. The collected medical history included hypertension, type 2 diabetes, and hyperlipidemia. After an overnight fast, a TBA-c16000 automatic analyzer (Toshiba Medical Systems, Tochigi, Japan) was used to measure biochemical parameters, including fasting glucose, hemoglobin A1C (HbA1C), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin, uric acid (UA), and high-sensitivity C-reactive protein (hsCRP) [23, 24].

Physical activity and sedentary behavior

PA levels and sedentary status were assessed using the validated Chinese version of the International Physical Activity Questionnaire–Short Form (IPAQ-SF) [25, 26]. All of the included patients filled out the questionnaires within one hour at noon with the assistance of specially trained nurses. The IPAQ-SF includes the number of days and the duration of vigorous, moderate, and walking activities during the previous week [27, 28]. The IPAQ-SF enables the calculation of metabolic equivalents (MET minutes per week), which are derived by assigning standardized MET values of 3.3, 4, and 8 for walking, moderate-intensity activity, and vigorous-intensity activity, respectively. These data were quantified, and an estimated metabolic equivalent of a task for each individual was classified as high, moderate, or low PA according to the IPAQ-SF score. The total daily sitting time was also collected for all participants.

Statistical analysis

The data are expressed as the mean ± standard deviation for normally distributed continuous variables and as number(percentage) for categorical variables. Demographic characteristics and biochemical variables were compared using student’s t-tests and the Mann–Whitney U test for the comparison of continuous variables, while chi-squared tests were used for categorical variables. Statistical significance was considered as P < 0.05. All statistical analyses were carried out using SPSS 20.0 software (IBM, Inc. Chicago, USA).

We performed an exploratory factor analysis to determine the clustering of cardiovascular risk factors, PA, and SB. The detailed statistical method has been described previously [29]. In brief, exploratory factor analysis is a statistical method designed to reveal the inter-correlations between the analyzed variables by reducing the collected variables into summary factors while retaining as much of the variance in the original variables as possible. There are three main steps of factor analysis: (1) extraction of factors (principal component analysis (PCA)); (2) rotation of factors to obtain a simple structure that can be easily interpreted; and (3) naming and interpreting each factor based on estimated values for factor loadings.

We used PCA to identify the principal components that reflect a group of variables that act together on a common pathophysiological process. We used an eigenvalue >1 as the extraction threshold, which was calculated as the sum of the squared factor loadings, which is a measurement of the amount of variation in the total sample accounted for by each factor. We then used orthogonal rotation (varimax rotation) to obtain factor loadings. We used an absolute loading value of >0.4 to interpret the factor pattern, which has been used by previous major factor analysis studies [30, 31].

In the primary analysis, factors were derived from 14 potential CVD risk factors: BMI, waist circumference, systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose, HbA1C, TC, HDL-C, LDL-C, TG, UA, HsCRP, MET, and daily sitting time. We first tested pair-wise correlations. We defined a KMO value greater than 0.6 and a significant Bartlett’s test of sphericity (P<0.001) as an indication for sampling adequacy and a lack of an identity matrix. Because it is difficult to find another population with the same low risk who had also completed the questionnaires as well as a biochemical study including CRP, we consider that it was reasonable to divide the whole population into two groups, one for training and one for validation to avoid overfitting the developed model. We randomly assigned 70% of the total population to the training group while the remaining 30% were assigned to the validation group. In statistics for data mining, it is a common method that training data is often a subset of the total data set, and the test set is a subset of the test-trained model. Similar analyses have been reported previously. For example, in Hravnak’s machine learning algorithms data were divided into Block 1 for the ML training/cross-validation set and Block 2 for the test set [32]. Furthermore, Goodman et al. used factor analysis for the cardiovascular clustering risk, and they selected 20% of the cases as an exploratory sample and the remaining 80% cases were used as a validation sample [30]. Because our current study aimed to investigate the cluster risk including inflammation makers and SB information, it is reasonable to test and validate sample using the same number of variables.

Results

A total of 5606 subjects (3157 male, age 51.5±11.7) were enrolled in this study. The enrollees were divided into a training group (N = 3926) and a validation group (N = 1676). The participants’ baseline characteristics, biochemical data, PA, and SB are shown in Table 1. The enrolled population consists of healthy middle age Taiwanese people. There was no significant difference between the demographic and biochemical data of the two groups.

Table 1

Baseline characteristics, comorbidities, and physical activity of the two groups.

	Training group (n = 3926)	Validation group (n = 1676)	p value
Male, n (%)	947.0 (56.7)	2,201.0 (56.5)	0.860
Age, years	51.58 ± 11.65	51.54 ± 11.81	0.902
Height, cm	165.47 ± 8.33	165.64 ± 8.41	0.491
Weight, kg	66.25 ± 12.88	66.23 ± 12.94	0.971
BMI, kg/m2	24.07 ± 3.58	24.02 ± 3.64	0.626
Waist circumference, cm	84.47 ± 10.04	84.46 ± 10.11	0.959
Smoking, n (%)	343.0 (21.1)	737.0 (19.4)	0.130
Drinking, n (%)	552.0 (34.1)	1,271.0 (33.4)	0.652
Dyslipidemia, n (%)	205.0 (12.7)	514.0 (13.6)	0.411
Diabetes, n (%)	122.0 (7.6)	250.0 (6.6)	0.193
Hypertension, n (%)	284.0 (17.6)	671.0 (17.7)	0.953
SBP, mmHg	120.39 ± 16.89	120.22 ± 17.06	0.736
DBP, mmHg	76.80 ± 10.66	76.65 ± 10.73	0.657
Cholesterol, mg/dL	202.34 ± 37.61	203.48 ± 38.10	0.315
Triglyceride, mg/dL	120.08 ± 73.10	119.50 ± 72.79	0.792
Uric acid, mg/dL	6.24 ± 1.52	6.28 ± 1.55	0.487
HDL, mg/dL	50.68 ± 13.89	50.24 ± 13.54	0.286
LDL, mg/dL	127.27 ± 34.15	128.56 ± 34.41	0.209
HbA1c, %	5.65 ± 0.74	5.65 ± 0.75	0.997
GLU, mg/dL	93.54 ± 21.84	93.31 ± 21.80	0.719
AST, U/L	24.22 ± 10.97	24.61 ± 13.73	0.311
ALT, U/L	27.20 ± 19.60	27.89 ± 25.60	0.336
Total Bilirubin, mg/dL	1.11 ± 0.55	1.12 ± 0.50	0.931
Creatinine, mg/dL	0.87 ± 0.37	0.87 ± 0.23	0.936
hsCRP, mg/dL	0.19 ± 0.36	0.19 ± 0.34	0.924
sitting time, min per day	388.63 ± 186.27	393.26 ± 190.37	0.402
METs, per week	1,524.33 ± 1,837.33	1,554.67 ± 1,902.77	0.581

ALT = alanine aminotransferase; AST = aspartate aminotransferase; BMI = body mass index; GLU = serum glucose; hsCRP = high sensitive C-reactive protein; HDL = high density lipoprotein; LDL = low density lipoprotein; SBP = systolic blood pressure; DBP = diastolic blood pressure; MET = metabolic equivalent; HbA1C = hemoglobin A1C.

The correlation among the 14 variables among all subjects is shown in Table 2. Sitting time demonstrated a negative correlation with MET (Pearson correlation coefficient (r) = -0.165, P < 0.001). MET value was significantly negatively correlated with waist circumference, blood pressure, cholesterol, triglyceride, LDL and sitting time, but positively correlate with HDL. We have further categories subjects into low, moderate, and high PA group and we found that subjects with low activity have unfavorable lipid profiles and higher baseline inflammatory makers, supporting the connection between higher cardiovascular risk to low PA (Table 3). However, although sitting time is negatively correlated with MET values, the correlation between lipid profiles only existed in HDL. It is not surprising because sitting time is only one factor among the definition of SB and total MET activity estimation was generated from more activity’s information. To explore the effect of gender and age of factor clustering pattern, we re-analyzed the correlation, taking into consideration gender and age which showed similar factor clustering in all subgroups (S1–S4 Tables).

Table 2

Correlation of cardiovascular risk factors.

	Age	BMI	WaistC	SBP	DBP	Cholesterol	TG	UA	HDL	LDL	A1c	GLU	sitting time	METs
Age	1	.107†	.170†	.271†	.122†	.087†	.051†	.034*	-.035*	.060†	.289†	.231†	-.180†	.063†
BMI	--	.1	.861†	.340†	.321†	-.001	.350†	.387†	-.444†	.091†	.260†	.285†	.022	-.003
Waist Circum	--	--	1	.334†	.316†	-.008	.353†	.414†	-.463†	.082†	.272†	.302†	.001	-.028*
SBP	--	--	--	1	.729†	.040*	.177†	.216†	-.159†	.056†	.180†	.201†	-.065†	.073†
DBP	--	--	--	--	1	.061†	.216†	.246†	-.186†	.088†	.126†	.158†	-.014	.033*
Cholesterol	--	--	--	--	--	1	.191†	.103†	.226†	.894†	.023	-.022	-.035*	-.031*
Triglyceride	--	--	--	--	--	--	1	.297†	-.447†	.052†	.252†	.311†	.014	-.059†
UricAcid	--	--	--	--	--	--	--	1	-.356†	.167†	.081†	.064†	.018	.030*
HDL	--	--	--	--	--	--	--	--	1	-.046†	-.192†	-.226†	-.046†	.032*
LDL	--	--	--	--	--	--	--	--	--	1	.020	-.037*	-.005	-.039*
HbA1c	--	--	--	--	--	--	--	--	--	--	1	.778†	-.049†	-.007
GLU	--	--	--	--	--	--	--	--	--	--	--	1	-.024	.001
sitting time	--	--	--	--	--	--	--	--	--	--	--	--	1	-.165†
METs	--	--	--	--	--	--	--	--	--	--	--	--	--	1

*0.05≧P≧0.01,

† 0.01≧P;

BMI = body mass index; GLU = serum glucose; HDL = high density lipoprotein; LDL = low density lipoprotein; SBP = systolic blood pressure; DBP = diastolic blood pressure; MET = metabolic equivalent; HbA1C = hemoglobin A1C.

Table 3

Baseline characteristics, comorbidities, and physical activity of different physical activity groups.

	"Low (n = 2398)"	"Moderate (n = 2154)"	"High (n = 844)"	p value
Age(y/o)	50.18 ± 11.42	52.60 ± 11.89	52.75 ± 12.01	< .001
Height	165.29 ± 8.29	165.67 ± 8.56	166.28 ± 8.19	0.010
Weight	66.23 ± 13.38	66.08 ± 12.73	66.63 ± 12.01	0.568
BMI	24.12 ± 3.81	23.95 ± 3.51	24.00 ± 3.34	0.283
Waist circumference	84.83 ± 10.49	84.32 ± 9.90	83.81 ± 9.34	0.037
Smoking (n, %)	533 (22.0)	386 (17.8)	159 (18.9)	0.001
Drinking (n, %)	847 (35.1)	682 (31.6)	290 (34.5)	0.036
Dyslipidemia (n, %)	307 (12.8)	303 (14.1)	109 (12.9)	0.424
Diabetes (n, %)	160 (6.7)	152 (7.1)	59 (7.0)	0.868
Hypertension (n, %)	396 (16.5)	415 (19.3)	143 (16.9)	0.043
SBP	119.15 ± 16.55	120.72 ± 17.36	122.32 ± 17.11	< .001
DBP	76.48 ± 10.69	76.71 ± 10.78	77.32 ± 10.56	0.143
Cholesterol	204.33 ± 38.48	202.47 ± 37.93	201.58 ± 36.37	0.111
Triglyceride	124.99 ± 75.70	117.90 ± 73.99	109.00 ± 59.39	< .001
UricAcid	6.27 ± 1.57	6.26 ± 1.50	6.29 ± 1.54	0.883
HDL	49.87 ± 13.40	50.48 ± 13.86	51.57 ± 13.69	0.008
LDL	129.45 ± 34.75	127.43 ± 34.40	126.51 ± 32.82	0.046
A1c	5.64 ± 0.76	5.67 ± 0.74	5.63 ± 0.74	0.360
GLU	93.36 ± 22.00	93.49 ± 21.69	93.17 ± 21.44	0.935
AST	24.35 ± 13.48	24.41 ± 12.99	25.07 ± 11.15	0.361
ALT	28.25 ± 22.86	27.31 ± 26.94	26.89 ± 17.47	0.251
Total Bilirubin	1.12 ± 0.54	1.12 ± 0.49	1.11 ± 0.49	0.900
Creatinine	0.86 ± 0.32	0.88 ± 0.27	0.88 ± 0.18	0.014
hsCRP	0.22 ± 0.39	0.18 ± 0.32	0.15 ± 0.29	0.004
sitting time (min per day)	421.64 ± 197.75	379.51 ± 178.33	339.07 ± 163.14	< .001
METs (per week)	374.97 ± 488.09	1,494.85 ± 663.30	4,901.42 ± 2,318.10	< .001

ALT = alanine aminotransferase; AST = aspartate aminotransferase; BMI = body mass index; GLU = serum glucose; hsCRP = high sensitive C-reactive protein; HDL = high density lipoprotein; LDL = low density lipoprotein; SBP = systolic blood pressure; DBP = diastolic blood pressure; MET = metabolic equivalent; HbA1C = hemoglobin A1C.

Tables 3 and 4 show the factor analysis results of the training group and the validation group. In the training group (Table 3), PCA identified five factors with an eigenvalue >1. The combined factors explained 69.43% of the variance among the original 14 factors. BMI, WC, HsCRP, TG, UA, and HDL-C were grouped together in the first common factor, the adiposity factor, which is similar to the metabolic syndrome criteria put forth by the NCEP/ATP III guidelines [33]. The inflammation biomarker hsCRP, which represents the underlying inflammation status of a subject, was shown within the adiposity group. This factor explained 21.30% of the total variance. The second common factor was the lipid factor, which contained LDL-C and total cholesterol and explained approximately 13.97% of the total variance.

Table 4

Factor analysis of the training group.

	Component
	1	2	3	4	5
Waist Circumference	.794
BMI	.788
HDL	-.719
Uric acid	.686
Triglyceride	.610
HsCRP	.492
Cholesterol		.986
LDL		.942
SBP			.918
DBP			.902
GLU				.910
A1c				.901
METs (per week)					.757
sitting time (per day)					-.713
Eigenvalues	3.733	1.958	1.523	1.424	1.083
Rotation Sums of Squared Loadings (% of Variance)	21.297	13.965	13.303	12.910	7.957
Rotation Sums of Squared Loadings (Cumulative %)	21.297	35.263	48.566	61.476	69.433

The third common factor, the blood pressure factor, consisted of SBP and DBP and accounted for 13.03% of the variance. The fourth common factor, the glucose factor, included fasting blood glucose and HbA1C and accounted for 12.91% of the variance. The final common factor, the SB factor, consisted of both the MET and the daily sitting time, which explained 7.96% of the variance.

For the results of the validation samples (Table 5), the PCA also identified five factors. The combined factors explained 69.48% of the variance in the original 14 factors. The adiposity factor, lipid factor, blood pressure factor, glucose factor, and activity factor explained 20.26%, 13.96%, 13.81%, 13.03, and 8.43% of the variance, respectively. Figs 1 and 2 show the component plots with factor diagrams from the PCA with varimax rotation. Both groups demonstrated a consistent clustering of risk factors.

Table 5

Factor analysis of the validation group.

	Component
	1	2	3	4	5
Waist Circumference	.808
BMI	.797
HDL	-.740
Uric acid	.660
Triglyceride	.504
HsCRP	.431
Cholesterol		.985
LDL		.946
GLU			.914
A1c			.908
SBP				.909
DBP				.890
METs (per week)					.803
sitting time (per day)					-.677
Eigenvalues	3.762	1.962	1.483	1.403	1.118
Rotation Sums of Squared Loadings (% of Variance)	20.262	13.955	13.814	13.026	8.426
Rotation Sums of Squared Loadings (Cumulative %)	20.262	34.217	48.030	61.057	69.483

BMI = body mass index; GLU = serum glucose; HDL = high density lipoprotein; LDL = low density lipoprotein; SBP = systolic blood pressure; DBP = diastolic blood pressure; MET = metabolic equivalent; HbA1C = hemoglobin A1C.

Fig 1

Component plots of the training group with factor diagrams from principle component analysis with varimax rotation.

BMI = body mass index; GLU = serum glucose; HDL = high density lipoprotein; LDL = low density lipoprotein; SBP = systolic blood pressure; DBP = diastolic blood pressure; MET = metabolic equivalent; HbA1C = hemoglobin A1C.

Fig 2

Component plots of the validation group with factor diagrams from principle component analysis with varimax rotation.

BMI = body mass index; GLU = serum glucose; HDL = high density lipoprotein; LDL = low density lipoprotein; SBP = systolic blood pressure; DBP = diastolic blood pressure; MET = metabolic equivalent; HbA1C = hemoglobin A1C.

To explore the composition of factor clustering in different gender and age, we performed age and sex-stratified factor analysis. The results showed a similar clustering pattern of risk factors in both gender and age groups (S1–S4 Tables).

Discussion

This single-center cross-sectional analysis examined 5606 healthy Asian adults and demonstrated that complex clustering cardiometabolic factors can be divided into five factor clusters: the adiposity factor (waist circumference, BMI, TG, HDL, and UA), the blood pressure factor (SBP and DBP), the lipid factor (TC and LDL), the glucose factor (fasting glucose and HbA1C), and the PA factor. These factors explained 21.97%, 13.97%, 13.30%, 12.91%, and 7.96% of the total variance, respectively. Systemic inflammation was linked to the adiposity factor, while SB and PA were clustered together and formed an independent CVD risk factor. Multiple cardiometabolic factors were involved in the development and progression of atherosclerotic cardiovascular disease.

This study is the first to investigate the relationship of cardiometabolic factors, systemic inflammation, and sedentary information simultaneously in the general population of Taiwan. We also demonstrated that the PA factors do not cluster with traditional CV risk factors. Our results suggest that physical inactivity may exert its effect on cardiovascular disease in an independent and unique way. This result may prompt future researchers to explore the possible pathophysiologic mechanism behind the independent effect of the PA level. Our findings could also provide important evidence that adiposity is linked to baseline inflammation, which explained nearly 20% of the variance in subjects without CVD.

Systemic inflammation and cardiovascular risk factors

Increased baseline inflammation is believed to play a crucial role in all stages of the artherothrombotic disease process, and treatment strategies to reduce inflammation have been demonstrated to lower the residual risk of CVD in addition to lipid-lowering medications [20, 21, 34]. We demonstrated that HsCRP was a part of the adiposity factor but was independent of the lipid factor (TC and LDL). The correlation between baseline CRP and LDL has been a controversial topic in previous observations [35]. The JUPITER trial demonstrated that risk reduction with statin therapy is related to the level of CRP, but no such relationship was observed for LDL-C. This suggests that statins reduced hsCRP independently of LDL and that LDL was not related to hsCRP [36, 37].

The current study extended the understanding that inflammation is linked to the adiposity factor and contributes to cardiometabolic clustering variance in the general population of Taiwan. Nicklas et al. showed that the change in hsCRP after weight loss is significantly correlated with changes in total body fat, abdominal adiposity, visceral adiposity, and lipid profiles, especially TG and HDL, but not LDL. This supports the close relationship between baseline inflammation and adiposity [38]. It is also worth mentioning that uric acid was also linked with adiposity in our study. This result is consistent with a previous factor analysis study of 2945 adults from the FIBER study [39]. Uric acid has been recognized as a maker for inflammation and oxidative stress, which underlie the disease processes of gout and CVD [40]. The inclusion of uric acid further supports our finding that insulin resistance and inflammation are features of adiposity.

Clustering of sedentary behavior and physical inactivity

SB and PA can explain some of the variance in our study, but the proportion of explained variance was smaller than that of other factors. We also demonstrated that daily sitting time has a negative correlation with weekly PA, suggesting a close relationship between them. The correlation between sitting time and PA has not been consistent in previous studies, with some demonstrating that PA and SB are interdependent, while others show an independent relationship [7, 41–43]. This discrepancy can be explained by the fact that a person can be both sedentary and physically active (the Active Couch Potato phenomenon, describes someone who meets the recommendations for physical activity but still sits around for long periods of the day) [44]. Another explanation is the inconsistent definition of SB across previous studies. Many studies used the lowest MET as a definition of SB, which does not require the subject to be actually sitting [15]. In this study, we used the IPAQ questionnaire, which is a widely validated questionnaire that is recommended by the WHO, to evaluate the association between sedentary behavior and weekly physical activity [45, 46]. Our result is compatible with a recent large study with robust methodology by Stamatakis et al., who showed that the daily sitting time and PA of 149,077 middle age adults were well correlated [42].

However, MET and daily sitting time were not significantly correlated with other cardiometabolic risk factors in our study. To evaluate whether PA and SB are truly independent of other risk factors, we divided our subjects into low, mediate, and high-intensity PA groups according to the definition of the IPAQ questionnaire. Table 3 shows the baseline characteristics of all subjects according to PA. Subjects with low PA have longer sitting times, larger waist circumferences, unfavorable lipid profiles (higher TG, higher LDL-C, and lower HDL-C) and higher baseline CRP. This indicates that low PA was still associated with risk factors of cardiovascular disease. Indeed, as shown in Table 3, patients with low PA have multiple factors at the same time, making it difficult to evaluate the importance and correlation of individual risk factors. Factor analysis of this complex data has provided more information to dissect the complex clustering of risk factors.

Table 3 have also showed that nearly 43% of our participants belonged to the low PA categories. Although there might be a selection bias that our population may have higher socioeconomic status, but lower daily activity, the high prevalence of low PA is not unique to our population. Both global and Taiwanese survey have showed equally alarming rate of insufficiently physically activity [47, 48]. Moreover, the WHO report have shown that over the past 15 years, the levels of insufficient activity did not improve [49].

Study limitations

There were some limitations to the present study. First, the information on sitting time was self-reported and is thus susceptible to reporting error. However, IPAQ was developed to measure health-related PA in the general population, has been tested extensively, and is now used in many international studies [45]. The sitting time was defined as the time subjects spend sitting while at work, at home, and during leisure time. This may include time spent sitting at a desk, socializing with friends, reading, sitting, or lying down watching television. The screen time spent on individual activities such as TV watching or computer/mobile usage was not recorded individually. Second, all of the participants were recruited from those who received a comprehensive annual or biennial examination at a healthcare center, and all subjects were asymptomatic and had few risk factors. A detailed history of medication was not available, and the possibility of selection bias cannot be excluded. Larger studies are also still needed to investigate whether our findings could be applied to other populations. Third, the study was cross-sectional in nature and provided no outcome data. Because the VGH-HEALTHCARE study is still ongoing, the long-term outcome data will be available when the VGH- HEALTHCARE study completes. Fourth, our study did not include emerging risk factors for CVD such as hematological factors and liver function. However Although these factors are considered to be new cardiovascular markers in some studies, there is little evidence supporting their role in the general population compared with the factors analyzed in the current study. Fifth, we did not apply machine learning method in the analysis of our data. We plan to use machine learning method for the analysis of outcome data after the VGH-HEALTHCARE study is completed. However, we did not consider using machine learning in our current study, for two reasons. First, using machine learning for factor analysis has not been widely accepted for lack of long-term outcome information. Second. To make our results more convincing to the general audience, we chose to investigate the percentage of variance of clustering cardiometabolic risk factors and the association between sedentary behavior and other associated risk factors with a well validated statistical method.

Conclusion

The complex relationship of cardiometabolic factors, inflammation, and sedentary information among the general population of Taiwan can be divided into five factor clusters: the adiposity factor (waist circumference, BMI, TG, HDL, and UA), the blood pressure factor (SBP and DBP), the lipid factor (TC and LDL), the glucose factor (fasting glucose and HbA1C), and the PA factor, which explained 21.97%, 13.97%, 13.30%, 12.91%, and 7.96% of the total variance, respectively. Our results suggest that systemic inflammation shares the same underlying disease process with metabolic syndrome, while the independent role of PA warrants exploration with future studies.

Factor analysis in patients <65 years of age.

(DOCX)

Click here for additional data file.

Factor analysis in patients >65 years of age.

(DOCX)

Click here for additional data file.

Factor analysis in male patients.

(DOCX)

Click here for additional data file.

Factor analysis in female patients.

(DOCX)

Click here for additional data file.

2 Jul 2020

PONE-D-20-13875

Factor analysis for the clustering of cardiometabolic risk factors and sedentary behavior

PLOS ONE

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Reviewer #1: Yes

Reviewer #2: Partly

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Reviewer #1: No

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Reviewer #1: This paper describes results of analysis to explore the clustering pattern of CVD risk factors in a Taiwanese study population. Although the paper is very well written, I have a few concerns which I explain below.

In the introduction the authors state that few studies have investigated the clustering relationship between sedentary behavior and other risk factors, and there is insufficient information to explain the variances of metabolic abnormalities observed. However, the authors did not give any references and I doubt whether this is actually true. I believe that there is a battery of studies showing that sedentary behavior clusters with factors of the metabolic syndrome? The reasons for why you did this study are not clear to me. Please explain and rephrase this part so that limitations of previous studies can be explained in further detail.

Why did respondents receive a comprehensive health examination at the Healthcare Center of Taipei Veterans 96 General Hospital from February 2015 to July 2019? In the results section it says that respondents are healthy, but why did they receive a health examination? Did they visit a specialist in the hospital? If so, they don’t seem to be healthy. What were further inclusion criteria and what were the response rates to this questionnaire?

It is not clear to me why respondents were divided in a training and validation group. Please explain. The numbers seem relatively small anyway, why not studying the group as a whole? Furthermore, to me, it seems more logic to perform another factor analysis on another study population (with different characteristics), rather than a sample of the same study population with similar characteristics.

Have the authors considered the method of machine learning (e.g. random forest)?

The result that there was no correlation between MET or sitting with lipid profiles, but that respondents with low physical activity have unfavorable lipid profiles and higher levels of HsCrp puzzles me. To be able to interpret this correctly, corrections for potential confounders, such as age and sex, etc, should be applied at the least.

The findings that adiposity is linked to inflammation and sedentary behavior is clustered with physical activity are not new to me. The manuscript lacks novelty. Furthermore, no explanation is given for that the explained variance for physical activity is relatively low.

A huge limitation of this study which is currently missing in this section is the cross-sectional data.

Reviewer #2: Leu et al. investigates cluster of CV risk factors in an Asian population. Authors identify five clusters with sedentary behavior explaining the lowest variance. An interesting finding was that inflammation biomarker HsCRP was clustered with only adiposity factors and not with other cardiometabolic risk factors.

Comments

-Authors imply they have looked at clusters of new CV risk factors. However, CRP and physical inactivity are not new CV risk factors.

-Authors mention in the method section they have data on haematological parameters (e.g., haemoglobin) which are emerging as novel risk factors for CVD. Why did authors not include these parameters in their analysis? Same questions applied also to liver enzymes and alcohol intake.

-It is not clear from method section whether participants in the current study were free of chronic diseases, including dyslipidaemia, hypertension and CVD. Were participants taking any medications? If yes, how was that taken into account in the analysis?

-How do authors explain the low correlation between sitting time and total physical activity? Wouldn’t you expect a higher correlation between the two factors? How this correlation observed in the current study contrasts previous findings? Maybe the questionnaires used in this study to capture both physical activity and inactivity were not good enough?

-It would be of interest to explore age and sex-stratified analysis, and see whether the clusters differ by age groups and sex. The paper would bring more novelty if the analysis were since the beginning stratified by sex, considering the sex gap in CVD.

**********

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Reviewer #1: Yes: Gerrie-Cor Herber

Reviewer #2: No

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21 Aug 2020

Response to reviewer 1

We appreciate your detailed comments, which have helped to improve our manuscript. Our responses to your comments are presented below and the passages that were added to the text are underlined in the revised manuscript.

Reply to the comments

1. Regarding the comments: "In the introduction the authors state that few studies have investigated the clustering relationship between sedentary behavior and other risk factors, and there is insufficient information to explain the variances of metabolic abnormalities observed. However, the authors did not give any references and I doubt whether this is actually true. I believe that there is a battery of studies showing that sedentary behavior clusters with factors of the metabolic syndrome? The reasons for why you did this study are not clear to me. Please explain and rephrase this part so that limitations of previous studies can be explained in further detail.".

Reply: Thank you for your comments and we apologize that our manuscript was unclear. We agree that studies have investigated clustering factor components with or without physical activity in metabolic syndrome [1-5]. However, few studies have considered inflammatory biomarkers among the clustering risk factors. In addition, although avoiding sedentary behavior has been suggested by international guidelines, there is no clear consensus on the definition of sedentary behavior [6]. Our current study used the IPAQ questionnaire, which reported physical activity volume (MET) and sitting time information at the same time, providing the opportunity to investigate the impact of sitting time/MET as well as inflammatory biomarkers in the same factor analysis model. The advantage of performing exploratory factor analysis rather than simply performing a linear correlation as that factor analysis can reveal the correlation and the underlying factor structure of a set of factors without imposing a preconceived structure on the outcome. Thus, we believe that our study design is valuable compared to the previous studies. We have revised the introduction of the manuscript to clarify the aim of our study. (page 4, lines70 to page 4, line 73, and page 5, line 77 to page 5, line 81)

Reference

1. HealyGN, MatthewsCE, DunstanDW, WinklerEAH, OwenN. Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 200306. Eur Heart J. 2011;32: 590–597. doi:10.1093/eurheartj/ehq451

2. EdwardsonCL, GorelyT, DaviesMJ, GrayLJ, KhuntiK, WilmotEG, et al. Association of Sedentary Behaviour with Metabolic Syndrome: A Meta-Analysis. O’ConnorKA, editor. PLoS One. 2012;7: e34916. doi:10.1371/journal.pone.0034916

3. SungwachaJN, TylerJ, Longo-MbenzaB, Lasi On’KinJBK, GombetT, ErasmusRT. Assessing clustering of metabolic syndrome components available at primary care for Bantu Africans using factor analysis in the general population. BMC Res Notes. 2013;6: 228. doi:10.1186/1756-0500-6-228

4. EsteghamatiA, ZandiehA, KhalilzadehO, MortezaA, MeysamieA, NakhjavaniM, et al. Clustering of leptin and physical activity with components of metabolic syndrome in Iranian population: An exploratory factor analysis. Endocrine. 2010;38: 206–213. doi:10.1007/s12020-010-9374-9

5. KelishadiR, ArdalanG, AdeliK, MotaghianM, MajdzadehR, Mahmood-ArabiMS, et al. Factor Analysis of Cardiovascular Risk Clustering in Pediatric Metabolic Syndrome: CASPIAN Study. Ann Nutr Metab. 2007;51: 208–215. doi:10.1159/000104139

6. TremblayMS, AubertS, BarnesJD, SaundersTJ, CarsonV, Latimer-CheungAE, et al. Sedentary Behavior Research Network (SBRN) – Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14: 75. doi:10.1186/s12966-017-0525-8

2. Regarding the comments: "Why did respondents receive a comprehensive health examination at the Healthcare Center of Taipei Veterans 96 General Hospital from February 2015 to July 2019? In the results section it says that respondents are healthy, but why did they receive a health examination? Did they visit a specialist in the hospital? If so, they don’t seem to be healthy. What were further inclusion criteria and what were the response rates to this questionnaire?"

Reply: We appreciate this helpful comment and apologize that this information was unclear. Our study derived data from the healthcare center of Taipei Veterans general hospital, which provides elective, self-paid health examination services to all individuals. In Taiwan, the healthcare service is covered by the national health insurance system (NHI), which guarantees nearly full reimbursement should a patient develop any medical illness or discomfort that requires a clinic visit or hospital evaluation [7]. However, for those without significant symptoms or illness, but who would like an examination in advance to identify undetected conditions for primary prevention purposes, the cost of the check-up is not covered by the NHI system, and the patient pays for it. We believe that data from our study population could provide vital information on primary prevention from the general population perspective. As seen in Table 1 in the manuscript, the prevalence of comorbidities in our population was quite low. Thus, their data reflect the healthy general population of Taiwan. For the response rate, there were 15,636 patients who had received check-up services during the study period, and 5606 (35.9%) subjects agreed to participate in our study. All participants completed the questionnaires. To avoid ambiguity, we added a more detailed description in the Methods section (page 6, lines 100 and page 7, line 110).

Reference

7. HsiehC-Y, SuC-C, ShaoS-C, SungS-F, LinS-J, Yang KaoY-H, et al.

Taiwan’s National Health Insurance Research Database: past and future

. Clin Epidemiol. 2019;Volume 11: 349–358. doi:10.2147/CLEP.S196293

3. Regarding the comments: "It is not clear to me why respondents were divided in a training and validation group. Please explain. The numbers seem relatively small anyway, why not studying the group as a whole? Furthermore, to me, it seems more logic to perform another factor analysis on another study population (with different characteristics), rather than a sample of the same study population with similar characteristics”.

Reply: Thank you for your comments and we apologize that the rationale for performing a validation analysis was unclear in our manuscript. Our study subjects were relative healthy without major diseases. They underwent a detailed check-up for primary prevention purposes. We designed our study this way so that we could analyze the different variable components of variance that were not affected by underlying disease or medications. Because it is difficult to find another population with the same low risk who had also completed the questionnaires as well as a biochemical study including CRP, we consider that it was reasonable to divide the whole population into two groups, one for training and one for validation to avoid overfitting the developed model. In statistics for data mining, it is a common method that training data is often a subset of the data set, and the test set is a subset of the test-trained model. Similar analyses have been reported previously. For example, in Hravnak’s machine learning algorithms data were divided into Block 1 for the ML training/cross-validation set and Block 2 for the test set [8]. Furthermore, Goodman et al. used factor analysis for the cardiovascular clustering risk, and they selected 20% of the cases as an exploratory sample and the remaining 80% cases were used as a validation sample [9]. Because our current study aimed to investigate the cluster risk including inflammation makers and sedentary behavior information, it is reasonable to test and validate sample using the same number of variables. We have modified the Methods section of the manuscript to describe our design. (page 11, lines 180 to page 120, line 195).

Reference

8. HravnakM, ChenL, DubrawskiA, BoseE, ClermontG, PinskyMR. Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data. J Clin Monit Comput. 2016;30: 875–888. doi:10.1007/s10877-015-9788-2

9. GoodmanE, DolanLM, MorrisonJA, DanielsSR. Factor analysis of clustered cardiovascular risks in adolescence: obesity is the predominant correlate of risk among youth. Circulation. 2005;111: 1970–7. doi:10.1161/01.CIR.0000161957.34198.2B

4. Regarding the comments: "Have the authors considered the method of machine learning (e.g. random forest)?”

Reply: We appreciate your instructive comment about using machine learning on our data. The current study is one of the initial studies that was performed using from the VGH-HEATHCARE database, and it aimed to dissect the complexity of clustering risk factors in the general population. The machine learning method, such as the random forest technique, is a powerful tool for building risk prediction models in both cross-sectional and cohort studies [10,11]. Because the VGH-HEATHCARE study is a prospective cohort study that still ongoing, machine learning studies will be conducted in the future to continue building the risk prediction model after a long-term outcome is available in the future. Thank you for your invaluable suggestion.

Reference

10. KanervaN, KonttoJ, ErkkolaM, NevalainenJ, MännistöS. Suitability of random forest analysis for epidemiological research: Exploring sociodemographic and lifestyle-related risk factors of overweight in a cross-sectional design. Scand J Public Health. 2018;46: 557–564. doi:10.1177/1403494817736944

11. WengSF, RepsJ, KaiJ, GaribaldiJM, QureshiN. Can machine-learning improve cardiovascular risk prediction using routine clinical data? LiuB, editor. PLoS One. 2017;12: e0174944. doi:10.1371/journal.pone.0174944

5. Regarding the comments: "The result that there was no correlation between MET or sitting with lipid profiles, but that respondents with low physical activity have unfavorable lipid profiles and higher levels of HsCrp puzzles me. To be able to interpret this correctly, corrections for potential confounders, such as age and sex, etc, should be applied at the least”.

Reply: We appreciate your insightful comment, and we apologize that our results were unclear. As shown in Table 2 of the manuscript, the MET value was significantly negatively correlated with waist circumference, blood pressure, cholesterol, triglyceride, LDL, and sitting time, but positively correlated with HDL. We have further categorized subjects into low, moderate, and high physical activity group, and we found that subjects with low activity have an unfavorable lipid profile and higher baseline inflammatory marker levels, supporting the connection between higher cardiovascular risk and low physical activity. Although sitting time is negatively correlated with MET values, the correlation between lipid profiles only existed for HDL. This is not surprising because sitting time is only one factor among the definition of sedentary behavior and total MET activity estimation was generated using other activity information. We re-analyzed the correlation, taking into consideration gender and age according to your suggestion. (supplemental Table 1, 2, 3, and 4) Thank you again for your suggestion

We have modified the Results of the manuscript. (page 13, lines 206 to page 14, line 219).

6. Regarding the comments:” The findings that adiposity is linked to inflammation and sedentary behavior is clustered with physical activity are not new to me. The manuscript lacks novelty. Furthermore, no explanation is given for that the explained variance for physical activity is relatively low.”

Reply: We appreciate your comment that pointed out the limitation of our study. The findings that adiposity is linked with inflammation and sedentary behavior is clustered with physical activity are not new. The novelty of our study is as follows: (1) we are the first to explore the clustering structure of CV risk factors and physical activity together; and (2) we demonstrated that the physical activity factors do not cluster with traditional CV risk factors. Our result suggests that physical inactivity may exert its effect on cardiovascular disease in an independent and unique way. This result may prompt future researchers to explore the possible pathophysiologic mechanism behind the independent effect of the physical activity level. The low variance of physical activity reflects the low physical activity in our healthy subjects. Table 3 in our manuscript shows that nearly 43% of our participants belong to the low activity categories, indicating a relatively inadequate physical activity level. Because our study population was enrolled from a self-pay check-up population, there might be a selection bias that this population may have higher socioeconomic status, but lower daily activity. However, the high prevalence of low physical activity is not unique to our population because other studies have demonstrated similar low physical activity levels. A recent global heath observatory survey showed that 23% of men and 32% of women ≥18 years of age were insufficiently physically active. Over the past 15 years, the levels of insufficient activity did not improve (28.5% in 2001; 27.5% in 2016). The WHO regions of the Americas (39%) and the Eastern Mediterranean Region (35%) had the highest prevalence of insufficient physical activity [12,13].

Reference

12. WHO | Prevalence of insufficient physical activity. WHO. 2018.

13. CDC maps show variance in adult physical activity levels by state, territory | AHA News. [cited 11 Aug 2020]. Available: https://www.healthycommunities.org/news/headline/2020-01-22-cdc-maps-show-variance-adult-physical-activity-levels-state-territory

7. Regarding the comments:”A huge limitation of this study which is currently missing in this section is the cross-sectional data”

Reply: Thank you and we agree with your opinion that our study is a cross-sectional study that lacks long-term outcome information. Because the VGH-HEALTHCARE study is still ongoing, we can only wait for the long-term outcome data in the future. We have mentioned this limitation of cross-sectional data with the study limitations, and we also revised the title, adding “a cross-sectional study”. Thank you for your suggestion. (page 21, lines 336 to page 21 line 338).

Responses to reviewer 2

We appreciate your helpful comments. Our responses are presented below, and the relevant passages have been incorporated into the revised manuscript.

1. Regarding the comments: " Authors imply they have looked at clusters of new CV risk factors. However, CRP and physical inactivity are not new CV risk factors. “

Reply: We appreciate your opinion that CRP is not a new marker. Our paper aimed to dissect various cardiometabolic risk factors that are easy to obtain simultaneously in the general population. We apologize that this was unclear in our manuscript, and it has been revised accordingly.

2. Regarding the comments: “Authors mention in the method section they have data on haematological parameters (e.g., haemoglobin) which are emerging as novel risk factors for CVD. Why did authors not include these parameters in their analysis? Same questions applied also to liver enzymes and alcohol intake."

Reply: Thank you for this insightful comment. Our current study enrolled subjects who underwent an annual physical check-up at VGH. Although the hemoglobin value and liver function are considered to be new cardiovascular markers, there is little evidence supporting their role in the general population compared with the factors in our studies. Therefore, we only enrolled the most important markers. In addition, the information about alcohol intake in our study was incomplete. We do not have information on the type of alcohol (e.g. wine, beer, or whisky) or the amount of consumption. Therefore, these factors were not included in the final analysis. We have added this as a study limitation. Thank you for your comments. (page 21, lines 339 to page 21 line 342).

3. Regarding the comments: " It is not clear from method section whether participants in the current study were free of chronic diseases, including dyslipidaemia, hypertension and CVD. Were participants taking any medications? If yes, how was that taken into account in the analysis?"

Reply: We appreciate this helpful comment and apologize for the lack of clarity in presenting our results. Our study derived data from the Taipei Veterans general hospital healthcare center that provides a health examination service to all individuals. However, for most of the population and most aspects of healthcare that is covered by national health insurance, patients with a particular complaint would usually have no difficulty finding a specialist appointment [7]. Our health examination services were almost exclusively used by health-conscious individuals who simply wished to receive comprehensive check-ups or who were concerned about having an undetected health issue. As seen in our data, the number of comorbidities in our population was quite low. Thus, they fit our initial description of healthy community-dwelling individuals. To avoid ambiguity, we have added a more detailed description to the Methods section. (page 6, lines 100 and page 7, line 110).

Reference

7. HsiehC-Y, SuC-C, ShaoS-C, SungS-F, LinS-J, Yang KaoY-H, et al.

Taiwan’s National Health Insurance Research Database: past and future

. Clin Epidemiol. 2019;Volume 11: 349–358. doi:10.2147/CLEP.S196293

Regarding the comments: "How do authors explain the low correlation between sitting time and total physical activity? Wouldn’t you expect a higher correlation between the two factors? How this correlation observed in the current study contrasts previous findings? Maybe the questionnaires used in this study to capture both physical activity and inactivity were not good enough?”

Reply: We appreciate this comment. Traditionally, sedentary behavior and total physical activity are believed to be independent risk factors for cardiovascular events [14,15]. However, the association between physical activity and sedentary behavior is not consistent. Some studies reported no significant association between the two while others showed that there was some association [16,17]. This discrepancy can be explained by the fact that a person can be both sedentary and physically active (the Active Couch Potato phenomenon, for example, would be an office worker who jogs or bikes to and from work, but who then sits all day at a desk and spends several hours watching TV in the evening) [18]. In this study, we used the IPAQ questionnaire, which is a widely validated questionnaire that is recommended by the WHO, to evaluate the association between sedentary behavior and weekly physical activity [19,20]. We believe that the low but still significant correlation between sitting time is consistent with previous studies, and it can be explained by the fact that non-sedentary physical activity in the general population is quite variable.

Reference

14. PandeyA, SalahuddinU, GargS, AyersC, KulinskiJ, AnandV, et al. Continuous dose-response association between sedentary time and risk for cardiovascular disease a meta-analysis. JAMA Cardiol. 2016;1: 575–583. doi:10.1001/jamacardio.2016.1567

15. EkelundU, BaumanA, LeeIM. Effects of early physical exercise on later health – Authors’ reply. The Lancet. Lancet Publishing Group; 2017. p. 801. doi:10.1016/S0140-6736(17)30506-8

16. SilfeeV, LemonS, LoraV, RosalM. Sedentary behavior and cardiovascular disease risk factors among latino adults. J Health Care Poor Underserved. 2017;28: 798–811. doi:10.1353/hpu.2017.0075

17. ChomistekAK, MansonJE, StefanickML, LuB, Sands-LincolnM, GoingSB, et al. Relationship of sedentary behavior and physical activity to incident cardiovascular disease: Results from the women’s health initiative. J Am Coll Cardiol. 2013;61: 2346–2354. doi:10.1016/j.jacc.2013.03.031

18. OwenN, HealyGN, MatthewsCE, DunstanDW. Too much sitting: The population health science of sedentary behavior. Exerc Sport Sci Rev. 2010;38: 105–113. doi:10.1097/JES.0b013e3181e373a2

19. CraigCL, MarshallAL, SjöströmM, BaumanAE, BoothML, AinsworthBE, et al. International physical activity questionnaire: 12-Country reliability and validity. Med Sci Sports Exerc. 2003;35: 1381–1395. doi:10.1249/01.MSS.0000078924.61453.FB

20. BoonRM, HamlinMJ, SteelGD, RossJJ. Validation of the New Zealand physical activity questionnaire (NZPAQ-LF) and the international physical activity questionnaire (IPAQ-LF) with accelerometry. Br J Sports Med. 2010;44: 741–746. doi:10.1136/bjsm.2008.052167

4. Regarding the comments: "It would be of interest to explore age and sex-stratified analysis, and see whether the clusters differ by age groups and sex. The paper would bring more novelty if the analysis were since the beginning stratified by sex, considering the sex gap in CVD”.

Reply: We appreciate this important comment. We had not considered age and sex-stratified analysis, but this is an important direction to explore. Thus, we performed an age and sex-stratified analysis as you suggested. The results showed a similar clustering pattern of risk factors in both gender and age groups (Supplemental Tables 1, 2, 3, and 4). This recommendation is greatly appreciated because the addition of sex and age-stratified results strengthen our study. We have mentioned the results of this analysis in our revised manuscript. (page 16, line 244 to page 16, line 247)

Submitted filename: 20200810 Responses_to_the_reviewerPlosOneV2(Final).docx

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28 Sep 2020

PONE-D-20-13875R1

Factor analysis for the clustering of cardiometabolic risk factors and sedentary behavior, a cross-sectional study.

PLOS ONE

Dear Dr. Leu,

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PLOS ONE

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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 #2: All comments have been addressed

Reviewer #3: (No Response)

Reviewer #4: All comments have been addressed

**********

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Partly

Reviewer #4: Yes

**********

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

Reviewer #2: Yes

Reviewer #3: (No Response)

Reviewer #4: Yes

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: (No Response)

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: Authors have successfully addressed my suggestions; the analysis on sex differences is of interest, despite no differences between men and women were found. I do not have further comments

Reviewer #3: More work is needed from the author, particularly in the method section, to make the manuscript scientifically suitable for publication.

Reviewer #4: Dear editor in chief

Thank you for inviting me to review the above-referenced paper. This research paper by Tsung-Ying Tsaiet al., aims to investigate the clustering relationship of sedentary behavior, cardiometabolic components, and inflammatory biomarkers among 5606 adults in Taiwan. This was investigated using an exploratory factor analysis. They found five cardiometabolic risk factors clusters: the adiposity factor (waist circumference, BMI, TG, HDL, and UA), the blood pressure factor (SBP and DBP), the lipid factor (TC and LDL), the glucose factor (fasting glucose and HbA1C), and the physical activity factor. Inflammation biomarker was clustered with adiposity factors, while physical inactivity and sedentary behavior were clustered with other factors.

I think the manuscript can be accepted for publication after a minor revision:

1. In "Statistical analysis" section, it's written" categorical variables were expressed as the mean ± 95% confidence interval but it was expressed as number and percentage.

2. In the discussion section, It's written "Table 3 shows the baseline characteristics of all subjects according to physical activity". and "Indeed, as shown in Table 3, patients with low physical activity have multiple factors at the same time, making it difficult to

evaluate the importance and correlation of individual risk factors". Here there are two remarks: first, the description of the results of the table are described in the result section and not the discussion section; Second, table 3 shows the Factor analysis of the training group and Table 5 shows the baseline characteristics of all subjects according to physical activity.

**********

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Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

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Submitted filename: Response PONE-D-20-13875 R1 reviewer.docx

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19 Oct 2020

Point to point response to reviewers

Response to reviewer 4

Regarding the comments: "In "Statistical analysis" section, it's written" categorical variables were expressed as the mean ± 95% confidence interval but it was expressed as number and percentage”.

Reply: Thank you for your comments, we apologize for this incorrect statement. We have revised the statement in the method section to fit the presentation in the results section. (page 10 lines 10)

Regarding the comments: "In the discussion section, It's written "Table 3 shows the baseline characteristics of all subjects according to physical activity". and "Indeed, as shown in Table 3, patients with low physical activity have multiple factors at the same time, making it difficult to evaluate the importance and correlation of individual risk factors". Here there are two remarks: first, the description of the results of the table are described in the result section and not the discussion section; Second, table 3 shows the Factor analysis of the training group and Table 5 shows the baseline characteristics of all subjects according to physical activity.”

Reply: Thank you for your comments, we apologize for the mislabeling. We have moved the statement regarding table 3 to the discussion section and labeled the statements regarding the tables correctly in the revised manuscript.

(page 21 lines 4 to 6; page 34)

Response to reviewer 1

1. Reviewer 1 wondered that there is a battery of studies showing that sedentary behavior clusters with factors of the metabolic syndrome, and claimed that knowledge gaps were not clearly explained.

a. I agree with the reviewer.

b. Authors’ reply to this comment was not exactly relevant to the question.

c. In Response to this comment, the authors failed to appropriately revising the manuscript. They mentioned that “There has been is a battery of studies demonstrating the association between sedentary and tradition CVD risk factors”; however, the authors did not explain that association, and they did not give an example from a study examined such association to support the introductory statement (i.e., Ln 68-70). The authors were not clear if the phathophysiology of the association between sedentary behavior and CVD was previously explained or not. In addition, the authors in Ln 75-78 contrary the introductory statement where they stated “few studies”. Finally, the authors failed to state knowledge gaps, and then justify their study.

Reply: We appreciate this comment from reviewer 1 and apologize that our manuscript was not fully respond to the reviewer’s requirement. There are many studies reporting the association between sedentary behavior and various cardiovascular risk factors. For example, in the landmark NHANES 2003-2006 study, total sedentary time was detrimentally associated with several biomarkers including waist circumference, HDL-cholesterol, C-reactive protein, triglycerides, insulin, and insulin resistance. Breaks from long sitting period, independent of sedentary time, were beneficially associated with waist circumference, C-reactive protein, and fasting plasma glucose. However, while this study only demonstrated the close relationship between sedentary behavior and inflammation, other important risk factors such as total cholesterol, UA, HbA1C, were not evaluated in that study. Moreover, only a part of the study population has serum glucose measurement in this study. [8] In a later meta-analysis of more than 20000 subjects, Edwardson et al. demonstrated that patients with longer sedentary time have greater odds of having metabolic syndrome. However, metabolic syndrome represented a clinical syndrome with heterogenous characteristics and it is impossible to delineate the association between sedentary behavior and a particular component of metabolic syndrome from these evidences.[9] Thus, although sedentary behavior has been shown to be associated with many cardiovascular risk factors, the more detailed clustering relationship was unknown. Factor analysis has a unique advantage revealing the clustering structure of various risk factors. However, previous sedentary behavior rarely included sedentary behavior while those that did do not include major cardiovascular risk factors. Hence our study has the advantage to analyze the clustering structure of most used cardiovascular risk factors and sedentary behavior. We have revised our manuscript and added above descriptions in the section of Introduction. In addition, we have also added a statement about the pathophysiologic implication of sedentary behavior that sedentary behavior linked to reduced triglyceride metabolism, insufficient antioxidant production, and glucose intolerance in several animal studies. (page 5, lines 13 to page 6 line 13).

References

8. HealyGN, MatthewsCE, DunstanDW, WinklerEAH, OwenN. Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 200306. Eur Heart J. 2011;32: 590–597. doi:10.1093/eurheartj/ehq451

9. EdwardsonCL, GorelyT, DaviesMJ, GrayLJ, KhuntiK, WilmotEG, et al. Association of Sedentary Behaviour with Metabolic Syndrome: A Meta-Analysis. O’ConnorKA, editor. PLoS One. 2012;7: e34916. doi:10.1371/journal.pone.0034916

2. Comment 2 of the reviewer 1, as well as comments 2 and 3 of the reviewer 2 was mainly about inclusion and exclusion criteria.

a. Authors’ reply to this comment was not exactly relevant to the question. In general, the authors do not need to emphasize the self-paid issue. Instead, they need to clearly define the study population and study patients. In addition, they must clearly state inclusion and exclusion criteria, which were overlooked in the revised manuscript.

b. The revised manuscript (i.e., Ln 98-108) included not needed information, while overlooked important information such as the reason for selecting this healthcare center. Taking into account that self-paid reason for selecting this center is not relevant, unless this issue is matter in the analysis.

Reply: We appreciate this comment from reviewer 1. We have removed some redundant description about the self-paid reason and added the following statement to describe our inclusion and exclusion criteria. “We included patients without significant symptoms or illness and excluded those who refused to participate, whose exam revealed an acute illness, or had a chronic condition that require regular follow up such as active cancer, heart failure, coronary artery disease or stroke. “(page 8, lines 6 to line 10 )

3. Reviewer 1 asked the authors to explain the training and validation groups, and questioned the study population and study patients.

a. I agree with the reviewer.

b. Authors’ response is not exactly relevant.

c. The authors need to define both training and validation groups in the current study, and the criteria used for selecting the subjects to such group.

d. Again, the authors need to clearly state inclusion and exclusion criteria.

Reply: We appreciate this instructive comment from reviewer 1. The training group and the validation group were randomly selected from the total study population. We have described the allocation process in the revised manuscript.

(page 13, lines 2 to line 3)

4. Reviewer 1 asked if the authors considered the method of machine learning in their methodology.

a. Authors’ response is inconvenience.

b. In the revised manuscript, Ln 181-185 was not clear.

c. In the revised manuscript, authors explained some methodology for machine learning, whereas the current study considered factor analysis. Therefore, Ln 186-187 is not relevant to the current study method.

d. In the revised manuscript, Ln 188-193 neither relevant nor clear. The authors should justify classifying such study subjects as training group and others as validation group.

Reply: We appreciate this comment from reviewer. We have added a short description about not considering machine learning methods in our study limitation. We will use machine learning method for the analysis of outcome data after the follow-ups of the VGH-HEALTHCARE study is completed. For the current study, we did not consider using machine learning in our methodology for two reasons. First, using machine learning technology for factor analysis has not been widely accepted because of lack of long-term outcome information. Second, our study purpose would like to investigate the percentage of variance of clustering cardiometabolic risk factors and the association between sedentary behavior and other associated risk factors.

(Page 23, line 5 to line 13)

5. In the comment 5 of the reviewer 1 and comment 4 of the reviewer 2, the analysis should consider the potential confounding effect of the age and sex.

• The authors re-analyzed that correlation and provided supplemental tables.

Reply: We appreciate this comment from the reviewer.

6. Reviewer 1 in the comment 6 claimed that the current manuscript lacks the novelty and explanation for relatively low variance for physical activity.

a. The authors explained in their reply this issue; however, it was not clear in the revised manuscript. They need to clarify and state it in the revised manuscript.

b. In the reply, the authors mentioned that “A recent global heath observatory survey showed that 23% of men and 32% of women ≥18 years of age were insufficiently physically active. Over the past 15 years, the levels of insufficient activity did not improve (28.5% in 2001; 27.5% in 2016).” Does this apply to your country?

Add novelty in revised manuscript

Reply: Thank you for your comment. As stated in our previous response, this study is the first to investigate the clustering relationship of a comprehensive array of cardiometabolic factors, including systemic inflammation, and sedentary information simultaneously in the general population of Taiwan. We also demonstrated that the physical activity factors do not cluster with traditional CV risk factors. Our results suggest that physical inactivity may exert its effect on cardiovascular disease in an independent and unique way. This result may prompt future researchers to explore the possible pathophysiologic mechanism behind the independent effect of the physical activity level. We have added the novelty of this study in the revised manuscript. In response to your second comment, inadequate physical activity was observed in the Taiwanese population as well.[42] Although our data is not a good representative of all Taiwanese people, the percentage is reasonable for the Taiwanese general population. There may be a selection bias, that we cannot avoid selecting subjects with better higher social economic status whose sedentary behavior may be different from other population. (page 17, lines 11 to line 17)

References:

42. WuX, TsaiSP, TsaoCK, ChiuML, TsaiMK, LuPJ, et al. Cohort Profile: The Taiwan MJ Cohort: Half a million Chinese with repeated health surveillance data. Int J Epidemiol. 2017;46: 1744-1744g. doi:10.1093/ije/dyw282

7. Reviewer 2 wondered "How do authors explain the low correlation between sitting time and total physical activity?”

a. I agree with the reviewer.

b. The authors need to make the revised as clear as their reply to this comment.

Reply: In our previous response to reviewers, we have addressed this issue by stating that traditionally, sedentary behavior and total physical activity are believed to be independent risk factors for cardiovascular events [6,7]. However, the association between physical activity and sedentary behavior is not consistent. Some studies reported no significant association between the two while others showed that there was some association [43]. This discrepancy can be explained by the fact that a person can be both sedentary and physically active (the Active Couch Potato phenomenon, describes someone who meets the recommendations for physical activity but still sits around for long periods of the day.) [44]. In this study, we used the IPAQ questionnaire, which is a widely validated questionnaire that is recommended by the WHO, to evaluate the association between sedentary behavior and weekly physical activity [45,46]. We believe that the low but still significant correlation between sitting time is consistent with previous studies, and it can be explained by the fact that non-sedentary physical activity in the general population is quite variable. We believe our original description should be sufficient.

References:

6. PandeyA, SalahuddinU, GargS, AyersC, KulinskiJ, AnandV, et al. Continuous dose-response association between sedentary time and risk for cardiovascular disease a meta-analysis. JAMA Cardiol. 2016;1: 575–583. doi:10.1001/jamacardio.2016.1567

7. EkelundU, BaumanA, LeeIM. Effects of early physical exercise on later health – Authors’ reply. The Lancet. Lancet Publishing Group; 2017. p. 801. doi:10.1016/S0140-6736(17)30506-8

43. SilfeeV, LemonS, LoraV, RosalM. Sedentary behavior and cardiovascular disease risk factors among latino adults. J Health Care Poor Underserved. 2017;28: 798–811. doi:10.1353/hpu.2017.0075

44. OwenN, HealyGN, MatthewsCE, DunstanDW. Too much sitting: The population health science of sedentary behavior. Exerc Sport Sci Rev. 2010;38: 105–113. doi:10.1097/JES.0b013e3181e373a2

45. CraigCL, MarshallAL, SjöströmM, BaumanAE, BoothML, AinsworthBE, et al. International physical activity questionnaire: 12-Country reliability and validity. Med Sci Sports Exerc. 2003;35: 1381–1395. doi:10.1249/01.MSS.0000078924.61453.FB

46. BoonRM, HamlinMJ, SteelGD, RossJJ. Validation of the New Zealand physical activity questionnaire (NZPAQ-LF) and the international physical activity questionnaire (IPAQ-LF) with accelerometry. Br J Sports Med. 2010;44: 741–746. doi:10.1136/bjsm.2008.052167

8. Reviewer 1 claimed a huge limitation in this study.

• The authors revised the manuscript, and added a section for study limitations.

Reply: We appreciate your comment.

Submitted filename: Response to the reviwerPlosOneV4R.docx

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2 Nov 2020

Factor analysis for the clustering of cardiometabolic risk factors and sedentary behavior, a cross-sectional study.

PONE-D-20-13875R2

Dear Dr. Leu,

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.

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Kind regards,

Mauro Lombardo

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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 #3: All comments have been addressed

**********

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: (No Response)

**********

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

Reviewer #3: (No Response)

**********

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

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: (No Response)

**********

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: (No Response)

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: The revised manuscript looks okay to me, and I have no additional comments or suggestions to the authors.

**********

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Reviewer #3: No

6 Nov 2020

PONE-D-20-13875R2

Factor analysis for the clustering of cardiometabolic risk factors and sedentary behavior, a cross-sectional study.

Dear Dr. Leu:

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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