Relationship Between Occupational Physical Activity and Subclinical Vascular Damage in Moderate-Altitude Dwellers

Abstract

Ujka, Kristian, Rosa Maria Bruno, Luca Bastiani, Eva Bernardi, Paolo Sdringola, Nenad Dikic, Bikash Basyal, Sanjeeb Sundarshan Bhandari, Buddha Basnyat, Annalisa Cogo, and Lorenza Pratali. Relationship between occupational physical activity and subclinical vascular damage in moderate-altitude dwellers. High Alt Med Biol. 18:249–257, 2017.

Background:

Occupational physical activity (OPA) has been associated with increased cardiovascular (CV) events. The aim of this study was to investigate the association between OPA and markers of subclinical vascular damage among a moderate-altitude population living in the rural village of Chaurikharka (Nepal; 2600 m sea level).

Methods:

Seventy-two individuals (age 42 ± 15 years, ranges 15–85 years, 23 men) were enrolled. Physical activity (PA) was evaluated using the International Physical Activity Questionnaire (IPAQ). Carotid–femoral pulse wave velocity (PWV), carotid ultrasound assessment, and flow-mediated dilation (FMD) were performed.

Results:

OPA was 9860 ± 5385 Metabolic Equivalent of Task (MET)-minutes/week, representing 77% of total energy expenditure, with 97% of the population performing high-intensity PA. In the univariate analysis, OPA was significantly associated with PWV (β = 0.474, p = 0.001) and carotid stiffness (CS) (β = 0.29, p = 0.019). In the multivariate analysis, including age, sex, oxygen saturation, mean blood pressure, low-density lipoprotein (LDL), and OPA, OPA remained an independent predictor of PWV (β = 0.403, p = 0.001) but not of CS (β = 0.028, p = 0.8). OPA remained an independent predictor of PWV independently from the Framingham risk score (FRS).

Conclusion:

High-intensity OPA shows a positive, independent association with aortic stiffness in Himalayan moderate-altitude dwellers. This study suggests how vigorous OPA performed in moderate altitude may be a CV risk factor.

Introduction

Cardiovascular (CV) diseases are the leading cause of mortality in the world (Go et al., 2014), and many preventive strategies have been suggested to reduce CV mortality (Daniels, 2014; Lavie et al., 2015). Leisure-time physical activity (LTPA) improves overall health and reduces the risk of CV events (Held et al., 2012; Lee et al., 2014; Lavie et al., 2015), whereas a sedentary lifestyle worsens CV prognosis (Leon-Latre et al., 2014; Mac Ananey et al., 2015). Evidences regarding the association between occupational physical activity (OPA) and CV risks or benefits are contradictory (Salonen et al., 1988; Wang et al., 2010; Stender et al., 1993; Kristal-Boneh et al., 2000; Krause et al., 2007, 2015; Holtermann et al., 2012; Clays et al., 2014; Wang et al., 2016). These inconclusive findings are mainly due to studies that do not consider separately OPA from LTPA (Krause, 2010). Thus, there is increasing evidence that LTPA and OPA have a complex contrasting effect on CV health with LTPA being protective and OPA being harmful (Krause, 2010; Clays et al., 2012, 2014).

The residents of the Himalayan valleys belonging to the Sherpa ethnicity represent a unique example of vascular remodeling to moderate and high altitude (Beall, 2007; Bruno et al., 2016). In a previous article published from our group, conducted in the same ethnic group, we found a unique vascular phenotype compared to Caucasian volunteers. Sherpas showed a microcirculatory dysfunction characterized by reduced endothelial-dependent dilation and a large artery remodeling characterized by a larger carotid artery diameter and reduced intima-media thickness (Bruno et al., 2014). However, life according to a traditional lifestyle in a hostile environment, far away from city centers and without road infrastructures or motorized vehicles, implicates high-energy expenditure for movement and transportation. Nevertheless, OPA based principally on agriculture and yard activity, performed by traditional means, exposes the individuals to a heavy workload. For these reasons, the populations living in these environments are chronically exposed to high-intensity OPA and thus represent a unique example for studying the potential relationship between OPA and subclinical or clinical vascular damage.

Biomarkers of vascular subclinical damage are potentially useful tools for assessment of CV risk beyond to classical risk factors. Intima-media thickness (IMT) of the common carotid artery, carotid–femoral pulse wave velocity (PWV), and flow-mediated dilation (FMD) are well-known biomarkers of atherosclerosis associated with the development of CV diseases commonly used as surrogate endpoints in various clinical trials (Chambless et al., 2000; Laurent et al., 2006; Vlachopoulos et al., 2015). It is known that regular LTPA is associated with increased FMD and reduced IMT and PWV (Seals et al., 2009; Gando et al., 2010; Pahkala et al., 2011). However, less is known regarding the association between OPA and these markers, as matter of fact most studies do not distinguish between OPA and LTPA, including them in the same group (Krause, 2010). To our knowledge, only Krause et al. (2007) found an association between high-intensity OPA and an accelerated progression of carotid atherosclerosis measured as an increase of IMT.

The aim of this study was to investigate the association between OPA and subclinical vascular damage in a moderate-altitude population chronically exposed to high levels of OPA, using a panel of markers of subclinical atherosclerosis and vascular dysfunction: FMD, carotid and aortic stiffness, and carotid IMT.

Methods

Study population

Seventy-two moderate-altitude individuals belonging to the Sherpa ethnic community, currently living in the rural village of Chaurikharka, Nepal (2600 m sea level), were enrolled. The inclusion criteria were as follows: age between 15 and 85 years, residential status in the village, apparent good health status, and written informed consent. The exclusion criteria were as follows: excessive alcohol use, active neoplasm, active infective diseases, and pregnancy. All the enrolled subjects were aware of the purposes of the study and gave written informed consent. The study was approved by the Nepal Health Research Council and the Nepal Academy of Science and Technology, Kathmandu, Nepal, and registered (Clinical Trials Gov Registration No. NCT01329159).

Experimental protocol

All measurements were performed in the morning, in a quiet room in a fully equipped laboratory we set up in the village. Nepalese physicians collected the medical histories with particular attention to the assessment of CV risk factors. Brachial BP was measured using an automated oscillometric device (OMRON-705IT; Omron, Kyoto, Japan) in supine position after at least 10 minutes of rest. Three measurements were taken with a 2-minute interval and the average of the last two was calculated. Finger O₂ saturation as measured by pulse oximetry (SpO₂) (Oximetry—Ohmeda TuffSat; GE Healthcare, Helsinki, Finland), weight, height, and waist circumference were also taken, and body mass index (BMI) was calculated. Fasting blood samples were taken from 55 patients for laboratory tests. Lipid profile, electrolytes, blood glucose, hemoglobin, and renal function were all determined according to standard laboratory procedures. The presence of hypercholesterolemia was defined as low-density lipoprotein (LDL) >130 mg/dL and low high-density lipoprotein (HDL) <40 mg/dL, according to the National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP)-III (Grundy et al., 2004). The 10-year Framingham risk score (FRS) for CV events (coronary death, myocardial infarction, coronary insufficiency, angina, stroke, transient ischemic attack, peripheral artery diseases, heart failure) was calculated using age, sex, smoking status, diabetes, total cholesterol, HDL, systolic blood pressure, and antihypertensive treatment according to guidelines (D'Agostino et al., 2008).

Assessment of physical activity

Physical activity (PA) levels were assessed using the International Physical Activity Questionnaire (IPAQ). IPAQ is a standardized and validated questionnaire used as an international measure for PA (Craig et al., 2003). The questionnaire used in the present study was the long, self-administered version that gathers information on four different types of PA: household and yard activity (six items), work activity (seven items), transportation physical activity (TPA; six items), and LTPA (six items). Two further questions assess the time spent sitting to indicate sedentary behavior. Nepalese-speaking physicians administered the questionnaire. The data collected were reported as a continuous measure in Metabolic Equivalent of Task (MET)-minutes/week. MET values were calculated for the abovementioned types of PA, and the total physical activity score (TPAS) was extracted according to the guidelines for data processing and analyzing of IPAQ. The PA levels were then classified into three levels: low, moderate, and high intensity according to guidelines (IPAQ Research Committee, 2005): Low—subjects who did not meet criteria for the categories “Moderate” and “Intense”; Moderate—the level of PA was classified as “Moderate” if it meets one of following criteria: 3 or more days of vigorous-intensity activity of at least 20 minutes per day or 5 or more days of moderate-intensity activity and/or walking of at least 30 minutes per day or 5 or more days of any combination of walking, moderate-intensity or vigorous-intensity activities achieving a minimum total PA of at least 600 MET-minutes/week; High—PA level was classified as “High” if one of the following criteria was met: vigorous-intensity activity on at least 3 days achieving a minimum total PA of at least 1500 MET-minutes/week or 7 days of any combination of walking, moderate-intensity, or vigorous-intensity activities achieving a minimum total PA of at least 3000 MET-minutes/week.

Carotid–femoral PWV

Carotid–femoral PWV was measured by applanation tonometry (reference values [r.v.] for age-matched population: 7.2 ± 1.3 m/s. PWV >10 m/s is indicative of subclinical vascular damage) (SphygmoCor CPVH; AtCor Medical) according to the expert consensus of the European Network for Noninvasive Investigation of Large Arteries (Laurent et al., 2006). Waveforms were recorded sequentially at the right common carotid and common femoral arteries. The distance was calculated as the difference between the two recordings sites ([femoral-suprasternal distance] − [carotid-suprasternal distance]) and the transit time was estimated using the foot-to-foot method. PWV was then calculated using the formula PWV = distance/transit time (Bruno et al., 2014). Two measures were taken and then averaged. Central systolic blood pressure (r.v. median, interquartile range: 101, 96–107 mmHg), central pulse pressure (PP), and central augmentation pressure (r.v. median, interquartile range: 9, 5–15 mmHg) were obtained from carotid pulse wave analysis using brachial blood pressure for calibration. Central augmentation index was calculated as the ratio between central augmentation pressure and central PP.

Carotid ultrasound assessment

Carotid ultrasound assessment was obtained by the same expert operator (R.M.B.) using a high-resolution ultrasound machine (MyLab25; ESAOTE, Florence, Italy) attached with a 10 MHz linear probe. Two 10-second recordings were taken in a segment free of atherosclerotic plaques from each common carotid artery 1 cm proximal to the carotid bulb in the anterolateral and posterior-lateral planes. Clips were then analyzed offline by Carotid Studio (Cardiovascular Suite; Quipu srl, Pisa, Italy), a software for the automatic evaluation of carotid diameter, IMT (r.v.: 6.5 ± 1.5 mm), and stiffness (Bruno et al., 2014). The following parameters were calculated: carotid distension, which is the change in diameter of the carotid artery (systolic diameter/diastolic diameter); carotid distensibility coefficient (DC), (DC = ΔA/[A × carotid PP]), where A is the carotid diastolic lumen area calculated from the diameter assuming the cross section of the artery as circular, and ΔA is the lumen area stroke change; carotid cross-sectional compliance coefficient (CC), which represents the change in lumen area during systole for pressure change (CC = ΔA/carotid PP); and carotid stiffness (CS), calculated using the Moens-Korteweg equation: (CS = [ρ × DC]^−1/2), where ρ is the blood density. The mean of the left and right carotid values was calculated (r.v. have not been established yet for carotid distension, CC, DC, and CS. Lower distension indexes and higher CS values indicate vascular damage).

Endothelium-dependent dilation of the brachial artery

Measurements were taken by the same operator (R.M.B.) using the same equipment and technique. All scans were taken using a high-resolution ultrasound (MyLab25; ESAOTE) with a 10 MHz linear probe and analyzed by a real-time computerized detection system with continuous real-time measurement of both brachial artery diameter and blood flow velocity (Cardiovascular Suite; Quipu srl). All subjects were studied at rest for >10 minutes, in a temperature-controlled room. Endothelium-dependent function was assessed by FMD as the dilation of the brachial artery in response to increased blood flow. A B-mode, longitudinal scan of the brachial artery of the dominant arm was taken 5–10 cm above the elbow with the probe being fixed by a stereotactic clamp. After a 1-minute baseline recording, a sphygmomanometer blood pressure cuff was inflated for 5 minutes at 300 mmHg around the forearm and then deflated to induce reactive hyperemia (Charakida et al., 2010; Bruno et al., 2014). Endothelium-independent dilation was assessed as the dilation induced by sublingual administration of 25 μg of glyceryl trinitrate (GTN). FMD and response to GTN were calculated as the percentage increase in brachial artery diameter above baseline. Arterial blood flow velocity was determined by pulsed Doppler with the signal at 70° and acquired by the same detection system. Resting, hyperemic shear rates (SR), and SR area under the curve were calculated according to the following equation: SR = 8 × blood viscosity × mean flow velocity/brachial artery diameter assuming that blood viscosity is 0.0035 Pa × s (Bruno et al., 2012) (r.v. have not been established for FMD, GTN, and SR indexes. Lower values indicate vascular dysfunction and are associated with worse CV prognosis).

Statistical analysis

SPSS version 21.0 (IBM Corp., Armonk, NY) software was used for statistical analysis. Clinical, vascular, and PA characteristics of the sample were assessed using descriptive statistics: continuous variables were expressed as median and interquartile range for not-normally distributed variables and mean and standard deviation for normally distributed variables; categorical variables as counts and percentages. Univariate regression models were made to compare markers of vascular damage and different types of PA. Multiple regression models were performed to evaluate the relationship between the vascular parameters (as dependent variables) and age, sex, oxygen saturation, mean blood pressure, LDL, and OPA score (as independent variables). Before the multiple regression model analysis, covariates were individually tested with a univariate linear regression model and only significant covariates were included. However, LDL concentration was included in the final model, although was not associated with the markers of vascular stiffness. With the aim to show the independent effect of OPA on aortic stiffness, independently from traditional CV risk factors, we performed a multiple regression model PWV (as dependent variable) and FRS and OPA (as independent variables).

Results

Clinical characteristics of the study population are shown in Table 1. It was a relatively young population, mean age 45 ± 15 years, ranges 15–85 years, and men 23 (31.9%). The smoking habit was very low 2.9%. As expected, SpO₂ and mean hemoglobin levels were compatible with a chronic adaptation to altitude, which is typical of this population. The prevalence of hypertension was 33.3%, diabetes 2.8%, obesity 9.7%, and moreover, hypercholesterolemia (LDL value >130 mg/dL) and low HDL (<40 mg/dL) were present in 56.3% and 21.8%, respectively. The presence of chronic kidney disease was 7.3%. FRS was successfully calculated for 42 subjects (58%) mainly due to the young age of the general population enrolled and because of the assessment of hematological data in 55 subjects. Median 10-year FRS was 4.9% (25th interquartile 3.2% to 75th interquartile 8.97%).

Table 1.

Clinical Characteristics of the Study Population (Sherpa Population, 2014; N = 72)

Clinical characteristics	N	Mean ± SD
Age (years)	72	(42 ± 15)
Men (%)	72	(31.9)
Weight (kg)	72	(61.77 ± 10.45)
Height (cm)	72	(158 ± 8)
BMI (kg/m²)	72	(24.6 ± 4.2)
Waist circumference (cm)	72	(86.7 ± 9.8)
Hypertension (%)	72	(33.3)
Diabetes (%)	72	(2.8)
Smoking (%)	72	(6.9)
Obesity (%)	72	(9.7)
Hypercholesterolemia (%) (LDL >130)	55	(44)
Low HDL (%) (HDL <5)	55	(49)
Chronic kidney disease (%)	55	(7.3)
HR (bpm)	72	(68 ± 10)
SBP (mmHg)	72	(129.4 ± 16.5)
DBP (mmHg)	72	(79.2 ± 10.6)
PP (mmHg)	72	(50.2 ± 9.6)
Oxygen saturation (%)	72	(94.1 ± 1.6)
LDL (mg/dL)	55	(138.4 ± 31.2)
Total cholesterol (mg/dL)	55	(207.7 ± 45.6)
HDL (mg/dL)	55	(50.7 ± 13.5)
Triglycerides (mg/dL)	55	(129.4 ± 72.3)
Hemoglobin (mg/dL)	55	(15.0 ± 1.3)
Glucose (mg/dL)	55	(82.7 ± 20.7)
Creatinine (mg/dL)	55	(0.86 ± 0.27)
GFR (mL/min 1.73 mq)	55	(91.6 ± 24.2)
FRS (%)	42	(4.9 [3.2–8.97])^a

Not normally distributed variable expressed as median and interquartile.

BMI, body mass index; DBP, diastolic blood pressure; FRS, Framingham risk score; GFR, glomerular filtration rate; HDL, high-density lipoproteins; LDL, low-density lipoproteins; HR, heart rate; PP, pulse pressure; SBP, systolic blood pressure.

PWV, carotid ultrasound, and FMD were successfully assessed in 63 (87.5%), 69 (95.8%) and 71 (98.6%) subjects, respectively. The mean values are shown in Table 2.

Table 2.

Vascular Characteristics of the Study Population

Vascular characteristics	Mean ± SD
Carotid–femoral PWV (m/s)	7.2 ± 1.6
Central SBP (mmHg)	119 ± 18
Central PP (mmHg)	37.4 ± 11.4
Central augmented pressure (mmHg)	10.2 ± 6.8
Central augmentation index (%)	24 ± 12
Mean carotid diameter (mm)	7.48 ± 0.96
Carotid distension (mm)	0.55 ± 0.15
DC (kPa⁻¹)	28.35 ± 24.72
CC (m² × kPa⁻¹)	0.99 ± 0.33
CS (m/s)	6.65 ± 1.40
Carotid IMT (mm)	0.55 ± 0.14
BA baseline diameter (mm)	4.16 ± 0.62
FMD (%)	4.13 ± 2.34
Baseline SR	167.6 ± 82.5
Hyperemic SR	923.1 ± 446.5
SR AUC	30746.7 ± 18836.6
GTN (%)	7.3 ± 3.0

BA, brachial artery; CC, carotid compliance; CS, carotid stiffness; DC, distensibility coefficient; FMD, flow-mediated dilation; GTN, glyceryl trinitrate-mediated dilation; IMT, intima-media thickness; PWV, pulse wave velocity; SD, standard derivation; SR, shear rate; SR AUC, shear rate area under the curve.

IPAQ was successfully administered to all subjects and PA scores were calculated for all four types of PA (Table 3). Work and yard activity was included under the same category as OPA, while TPA and LTPA were taken into account separately. TPAS ranged between 9702 and 15,120 MET-minutes/week, with 97% of the population performing an extremely high level of PA. OPA was the principal form of PA with MET range 6720–13,440, being responsible for ∼77% of total energy expenditure among the inhabitants. The MET range for LTPA was 0–1440 MET-minutes/week and represented only 6% of TPAS, while TPA was 0–2772 MET-minutes/week (17%).

Table 3.

Physical Activity Levels of the Study Population (International Physical Activity Questionnaire)

	Mean ± SD	Proportion of TPAS (%)	Median (interquartile range)
Transportation PA (MET-minutes/week)	2180 ± 2452	17	1386 (0–2772)
Leisure-time PA (MET-minutes/week)	765 ± 1423	6	0 (0–1440)
Occupational PA (MET-minutes/week)	9860 ± 5385	77	10,080 (6720–13,440)
TPAS (MET-minutes/week)	12,806 ± 5443		12,558 (9702–15,120)
Average sitting time (minutes)	184 ± 91		180 (120–240)

PA, physical activity; TPAS, total physical activity score.

PWV and CS showed a significant positive association with both OPA score and TPAS, while carotid distension and carotid CC presented an inverse association. No significant association was found between these markers and LTPA, TPA, or total sitting time. IMT, carotid diameter, carotid DC, FMD, baseline SR, SR maximum, SR area, SR area to maximum, and GTN were not significantly associated with either OPA score or TPAS. However, SR maximum and SR area under the curve were significantly associated with LTPA. Full results are shown in Table 4.

Table 4.

Univariate Regression Models Between Markers of Vascular Damage and Physical Activity

		OPA	TPA	LTPA	TPAS	Total sitting time
Carotid–femoral PWV, n = 62	βs	0.474	0.133	−0.102	0.503	−0.045
Carotid–femoral PWV, n = 62	p	0.001 ^a	0.302	0.430	0.001 ^a	0.726
Carotid distension, n = 65	βs	−0.320	−0.014	−0.114	−0.346	0.184
Carotid distension, n = 65	p	0.009 ^a	0.915	0.366	0.005 ^a	0.143
DC, n = 65	βs	−0.182	0.259	−0.072	−0.080	0.237
DC, n = 65	p	0.147	0.037 ^a	0.569	0.528	0.058
CC, n = 65	βs	−0.302	−0.056	−0.056	−0.334	0.057
CC, n = 65	p	0.015 ^a	0.660	0.656	0.007 ^a	0.651
CS, n = 65	βs	0.289	−0.083	0.157	0.283	−0.017
CS, n = 65	p	0.019 ^a	0.509	0.213	0.022 ^a	0.890
Carotid IMT, n = 68	βs	0.138	−0.025	0.228	0.179	−0.034
Carotid IMT, n = 68	p	0.261	0.839	0.062	0.145	0.786
Mean carotid diameter, n = 65	βs	−0.041	0.093	0.054	0.013	0.145
Mean carotid diameter, n = 65	p	0.743	0.456	0.667	0.916	0.248
FMD, n = 70	βs	−0.092	−0.089	0.001	−0.131	0.200
FMD, n = 70	p	0.448	0.463	0.998	0.280	0.870
Baseline SR, n = 68	βs	−0.165	−0.064	0.113	−0.163	0.017
Baseline SR, n = 68	p	0.180	0.601	0.359	0.184	0.890
Hyperemic SR, n = 68	βs	−0.097	−0.156	0.284	−0.088	0.091
Hyperemic SR, n = 68	p	0.448	0.205	0.019 ^a	0.474	0.461
SR AUC, n = 67	βs	−0.103	−0.206	0.313	−0.110	0.169
SR AUC, n = 67	p	0.405	0.095	0.010 ^a	0.337	0.171
SR AUC to maximum, n = 67	βs	−0.074	−0.177	0.348	−0.058	0.064
SR AUC to maximum, n = 67	p	0.554	0.152	0.004 ^a	0.639	0.605
GTN	βs	0.010	−0.023	−0.067	−0.016	−0.088
N = 62	p	0.937	0.861	0.603	0.901	0.496

p < 0.05.

CC, compliance coefficient; CS, carotid stiffness; DC, distensibility coefficient; FMD, flow mediated dilation; IMT, intima media thickness; LTPA, leisure time physical activity; OPA, occupational physical activity; PWV, pulse wave velocity; TPA, transportation physical activity; TPAS, total physical activity score; SR, shear rate; SR AUC, shear rate area under the curve; GTN, glyceril trinitrate mediated dilation; βs, β standardized.

The bold values are those that are statistically significant.

At the univariate analysis, PWV showed a significant positive association with OPA score (β = 0.474; p < 0.001) and TPAS, while no association was found either with LTPA or TPA. In the multiple regression analysis, including age, sex, mean blood pressure, SpO₂, blood LDL concentration, and OPA score, only age (β = 0.439; p = 0.002), SpO₂ (β = 0.309; p = 0.014), and OPA (β = 0.403; p = 0.001) remained independent predictors of PWV (full model R² = 0.516). Similar results were found also for CS indexes. At the univariate analysis, carotid distension and CC were found to be inversely correlated with OPA levels (β = −0.32, p = 0.09; β = −0.30, p = 0.015), while CS was found to be positively correlated (β = 0.29, p = 0.019). In the multivariate analysis, including age, sex, mean arterial pressure, SpO₂, blood LDL concentration and OPA, OPA was associated only with the carotid mean diameter (β = −0.319; p = 0.017), while no significant association was found for the other carotid parameters. In the univariate regression model, FRS was associated with PWV (β = 0.375, p = 0.026; full model R² = 0.141). In a multiple regression model, including OPA and FRS as possible predictors of PWV, OPA remained an independent predictor of PWV independently from FRS (β = 0.507, p = 0.002; full model R² = 0.403). Full results are shown in Table 5.

Table 5.

Multivariate Models Exploring Determinants of Arterial Stiffness

Dependent variable	Independent variable	βs	p
Carotid–femoral PWV Full model R2 = 0.516	OPA	0.403	0.001
	Age	0.439	0.002
	Sex§:	0.042	0.70
	Oxygen saturation	0.309	0.014
	Mean blood pressure	0.182	0.11
	LDL	0.100	0.5
Carotid distension Full model R2 = 0.413	OPA	−0.233	0.09
	Age	−0.276	0.073
	Sex§:	0.280	0.027 ^a
	Oxygen saturation	0.272	0.046 ^a
	Mean blood pressure	−0.078	0.56
	LDL	−0.024	0.86
DC Full model R2 = 0.107	OPA	−0.226	0.18
	Age	−0.088	0.64
	Sex§:	0.013	0.93
	Oxygen saturation	0.300	0.85
	Mean blood pressure	0.119	0.48
	LDL	0.198	0.24
CC Full model R2 = 0.349	OPA	−0.205	0.15
	Age	−0.120	0.45
	Sex§:	0.206	0.12
	Oxygen saturation	0.228	0.11
	Mean blood pressure	−0.278	0.057
	LDL	0.043	0.76
CS Full model R2 = 0.573	OPA	0.028	0.80
	Age	0.512	0.0002
	Sex§:	−0.103	0.32
	Oxygen saturation	−0.132	0.24
	Mean blood pressure	0.303	0.01
	LDL	0.001	0.99
IMT Full model R2 = 0.508	OPA	−0.050	0.96
	Age	0.713	<0.001 ^a
	Sex§:	−0.057	0.60
	Oxygen saturation	−0.078	0.52
	Mean blood pressure	−0.142	0.25
	LDL	0.080	0.52
Mean carotid diameter Full model R2 = 0.471	OPA	−0.319	0.017^a
	Age	0.656	<0.001
	Sex§:	0.129	0.27
	Oxygen saturation	0.089	0.48
	Mean blood pressure	0.145	0.26
	LDL	0.079	0.54
FMD Full model R2 = 0.154	OPA	−0.069	0.64
	Age	−0.225	0.19
	Sex§:	−0.301	0.035 ^a
	Oxygen saturation	−0.082	0.59
	Mean blood pressure	0.069	0.66
	LDL	−0.26	0.86
Baseline SR Full model R2 = 0.047	OPA	−0.043	0.79
	Age	−0.206	0.26
	Sex§:	−0.090	0.56
	Oxygen saturation	−0.097	0.56
	Mean blood pressure	0.076	0.65
	LDL	−0.009	0.95
Hyperemic SR Full model R2 = 0.186	OPA	−0.072	0.63
	Age	−0.201	0.23
	Sex§:	−0.260	0.07
	Oxygen saturation	0.047	0.76
	Mean blood pressure	0.025	0.87
	LDL	−0.157	0.31
SR area Full model R2 = 0.181	OPA	−0.122	0.42
	Age	−0.034	0.84
	Sex§:	−0.305	0.036 ^a
	Oxygen saturation	0.071	0.65
	Mean blood pressure	−0.025	0.87
	LDL	−0.180	0.25
SR AUC Full model R2 = 0.156	OPA	−0.084	0.59
	Age	−0.68	0.69
	Sex§:	−0.212	0.15
	Oxygen saturation	0.097	0.54
	Mean blood pressure	−0.009	0.96
	LDL	−0.213	0.18
GTN Full model R2 = 0.174	OPA	0.017	0.92
	Age	−0.269	0.15
	Sex§:	−0.215	0.16
	Oxygen saturation	0.108	0.53
	Mean blood pressure	0.002	0.98
	LDL	−0.067	0.71
Variable PWV Full model R2 = 0.141	FRS	0.375	0.026
Variable PWV Full model R2 = 0.403	FRS	0.240	0.11
Variable PWV Full model R2 = 0.403	OPA	0.507	0.002

CC, compliance coefficient; CS, carotid stiffness; DC, distensibility cefficient; FMD, flow mediated dilation; IMT, intima media thickness; LDL, low density lipoprotein; OPA, occupational physical activity; PWV, pulse wave velocity; SR, shear rate; SR AUC, shear rate area under the curve; GTN, glyceril trinitrate mediated dilation; βs, β standardized; Sex§, β is associated with being male.

The bold values are those that are statistically significant.

Discussion

The aim of this study was to investigate the association between OPA and subclinical vascular damage in a relatively young population born and currently living at moderate altitude.

The main finding of the study is that high-intensity OPA is positively associated with aortic stiffness. OPA, principally based on agriculture and yard activity, represented the principal form of PA responsible for ∼80% of total energy expenditure. OPA was found to be significantly correlated with increased PWV and CS and with reduced carotid compliance and distension, suggesting an increase in both aortic and CS among more active subjects. The association with PWV remained significant even after adjusting for age, sex, SpO₂, mean BP, and LDL concentration, while significance was lost for CS and distension. OPA showed to be significantly correlated with PWV even after adjusting for FRS, remaining an independent determinant of aortic stiffness.

Our study is one of the few studies showing a positive association between high-level OPA and increased aortic stiffness. The population enrolled lived in a rural village of a remote area, far from any road, and with low typical CV risks factors and with high-level physical workload. Recently, Brighenti-Zogg investigated PA in real-life workplaces in healthy Swiss employers (Brighenti-Zogg et al., 2016). The Swiss agricultural workers were considered as a high-intensity group with 8820 MET minutes/week (considering 7 day of working). The Sherpa population showed a higher level of physical workload in comparison with Western countries but they were still in the high PA group according to the International Standard Classification of Occupations 1988 (ISCO 88). Most of the previous studies have reported an inverse association between PA and PWV (Seals et al., 2009; Gando et al., 2010), showing a reduced aortic stiffness among physically active subjects (McDonnell et al., 2013; Endes et al., 2016). However, as widely discussed by Krause et al. most of these studies explored principally the effects of LTPA without differentiating LTPA from OPA or considered them together as “habitual PA” (Krause, 2010). Our population was exposed principally to OPA and performed few or no LTPA. Accordingly, we did not find any significant association between the reported LTPA and PWV. Furthermore, our population was exposed to a high-intensity PA, whose effects on arterial stiffness are less known. Vlachopoulos et al. (2010b), in an experimental study from endurance athletes, found an increased PWV among ultramarathon athletes compared to controls, while a 4-month resistance training was found to have a negative, although reversible, effect on vascular function, worsening large artery compliance (Miyachi et al., 2004). While LTPA has been strongly associated with reduced risk of CV events (Held et al., 2012; Lee et al., 2014), many prospective cohort studies have found an increased risk of CV events (myocardial infarction, CV mortality) among people exposed to high levels of OPA (Stender et al., 1993; Kristal-Boneh et al., 2000; Holtermann et al., 2012; Li et al., 2013; Clays et al., 2014; Krause et al., 2015; Wang et al., 2016). In the present study, we found a significant positive correlation, independently from other CV risk factors, between OPA and PWV. Taking into account that PWV is a well-known biomarker of subclinical vascular damage predictive of CV events, widely used as a surrogate endpoint of atherosclerosis (Laurent et al., 2006; Vlachopoulos et al., 2010a, 2015), our finding suggests an association between high-intensity OPA and overall CV. Our result is partly confirmed by Krause et al. (2007), who showed that high-intensity OPA is associated with an accelerated progression of carotid atherosclerosis, measured as carotid IMT. Although IMT and PWV are different markers of vascular damage, they both detect subclinical vascular damage and both correlate with CV risk (Vlachopoulos et al., 2015). The pathophysiological mechanisms by which high-intensity OPA increases the risk of CV diseases are not completely clear. According to the hemodynamic theory of atherosclerosis (Glagov et al., 1988), the atherosclerotic process has a prediction for regions of arterial wall where the blood flow is not optimal and turbulences are generated. Normally, the blood has a laminar flow creating a uniform frictional force (the so-called shear stress) to the intima layer of the arteries, which is necessary for optimal functioning of the endothelium. During the cardiac cycle, the shear stress is not uniform but oscillates from positive to negative values creating turbulences in blood flow, which may induce endothelial damage and endothelial cell activation. While during diastole, the shear stress is positive and constant thus being optimal for endothelial functioning, during systole it is suboptimal and shifts from positive to negative values triggering turbulences to the arterial wall and facilitating the progression of atherosclerosis. In particular, a high heart rate should increase exposure of the outer and side walls of the carotid sinus to oscillatory flow fields, and this would increase the relative time spent in systole, compared to diastole, during each cardiac cycle, enhancing atherogenesis (Ku et al., 1985; Glagov et al., 1988; Krause et al., 2007). These blood turbulences may then induce an inflammatory process in the arterial wall and cause endothelial damage, facilitating the absorption of lipids into the arterial intima layer and triggering the atherosclerotic process, which leads to the formation of atherosclerotic plaques (Kumar et al., 2015). Another possible mechanism could be an increase in oxidative stress, which has been associated with high-intensity, but not with moderate- or low-intensity PA (Goto et al., 2003). Both endothelial damage and increased oxidative stress might worsen aortic stiffness acting on its “functional” rather than structural component, as demonstrated, for example, in diabetic individuals (Bruno et al., 2012). This is not necessarily in contrast with our data showing lack of correlation between FMD and OPA. Although many studies have shown that habitual PA improves endothelial function (Seals et al., 2009), in this peculiar population endothelial function is probably influenced by exposure to moderate altitude (Bruno et al., 2014), thus masking any possible relationship with PA.

Considering traditional CV risk factors, previous studies have reported increased concentration of LDL and total cholesterol, higher triglycerides, reduced HDL, increased prevalence of diabetes, and reduced prevalence of smoking among South Asians compared to Caucasians, possibly leading to a greater prevalence of CV disease (Anand et al., 2000; Tziomalos et al., 2008). Regarding hypertension, while earlier studies found higher BP values among South Asians (McKeigue et al., 1991), a systematic review found no difference in the prevalence of hypertension compared to Caucasians (Agyemang and Bhopal, 2002). The present study confirmed the high prevalence of abnormal profile in the high-altitude population (hypercholesterolemia, defined as LDL >130 mg/dL, was 56.3%, low HDL was 21.8%) and low prevalence of obesity (9.7%) (Hirschler, 2016). The dyslipidemic trend can be due to the dietary habits characterized by an increased use of lipid-containing products (i.e., dried cheese, brick tea flavored with butter, yak meat). The low HDL prevalence of the Sherpa population living in the rural village was similar to highlanders of Lhasa (Tibet) (30–70 years): 22.4% (Sherpa et al., 2011). In contrast with previous studies, the prevalence of diabetes was low, possibly as a consequence of the young age and to the high level of PA performed. Regarding BP, mean values were normal, while the self-reported prevalence of hypertension was 33.3%, which corresponds to the prevalence of hypertension in Nepal (Neupane et al., 2014). The median FRS for 10-year risk of CV events was 4.9%. However, it does not represent the overall risk of the whole population because the formula used excluded low-risk subjects younger than 30 and the overall risk of the population is probably lower. Furthermore, the use of a U.S.-based CV risk score of this population could not depict correctly the actual risk of a different ethnic group, as reported in current literature (Chia et al., 2014).

Finally, we found no association between PA and IMT. Previous studies have reported an association between OPA and increased IMT. The ARIC study, in a subgroup of black-race subjects, found an association between physical demand at work and IMT (Muntaner et al., 1998). More recently, Krause et al. (2007), in a population-based study showed that energy expenditure at work was associated with accelerated progression of carotid IMT. In the present study, IMT was not associated with an increased level of OPA. However, our finding is not in contrast with Krause's work. In his study, the author did not report any correlation between baseline OPA and IMT, but focuses the attention only on the progression of atherosclerosis during the follow-up period (Krause et al., 2007). Furthermore, Krause's study was conducted in a Caucasian population, while our population was a Sherpa population, characterized by a peculiar carotid remodeling to altitude. Our group, in a previous study, found a unique vascular phenotype among Nepalese altitude dwellers compared to Caucasian lowlanders characterized by increased carotid diameter, reduced IMT, and reduced FMD, which might have masked the possible effect of PA on IMT (Bruno et al., 2014). Finally, the methods we used to measure the IMT are slightly different from Krause's 2007 study. In the Finnish study of progression of atherosclerosis, Krause used the maximum IMT in a 1-cm-long section of the common carotid artery, including any atherosclerotic plaques. On the contrary, in our study among Sherpas, we measured the IMT in a segment free of atherosclerotic plaques excluding any potential IMT changes associated with plaque formation. These methodological differences in measuring IMT may represent a limit and may explain the lack of association between OPA and IMT.

Limitations

Some limitations of our study must be acknowledged. Dietary habits of this population or moderate-altitude exposure could induce modification of aortic stiffness and distensibility. Although Parati et al. (2013) showed that PWV does not change after exposure to high altitude, the lack of differentiation between the effect of PA and the effect of dietary habits or altitude represents a limitation of our study. Moreover, the PA level was measured using the IPAQ during a self-reported interview and does not represent an objective measure of PA and this may have limited our study. Another limitation was the low participation rate to some measurements. However, it is important to mention that the participation rate depended principally on technical problems related to the environmental setting, or the participant's acceptance to the technique and do not represent any selection bias. Finally, the absence of a follow-up study to better assess the association between PA and future CV events, and the small size of our population could represent two further limitations.

Conclusions

In conclusion, high-intensity OPA shows a positive, independent association with arterial stiffness in Himalayan moderate-altitude dwellers. This study suggests how vigorous OPA performed in moderate altitude may be a CV risk factor and thus should be included in the assessment of overall CV risk. Prospective follow-up studies are needed to better understand the association and its correlation with future CV events. The result of this study suggests the use of PWV in clinical practice for assessing CV risk in asymptomatic subjects.

Footnotes

Acknowledgments

We thank all the participants in the study living in Chaurikharka village and we hope that they can rebuild their village after the disastrous earthquake that recently occurred in Nepal. This work was conducted within the framework of SHARE—Stations at High Altitude for Research on the Environment Project, promoted by Ev-K2-CNR.

Author Disclosure Statement

No competing financial interests exist.

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