Association between healthy lifestyle score and dyslipidemia; a population based study

doi:10.21203/rs.3.rs-3677422/v1

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Association between healthy lifestyle score and dyslipidemia; a population based study

https://doi.org/10.21203/rs.3.rs-3677422/v1

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Background

The prevalence of dyslipidemia in Iran is high, making it important to identify its risk factors. However, the association between dyslipidemia and the healthy lifestyle index (HLS), which encompasses various individual behaviors, has not been extensively studied. Therefore, the aim of this study was to investigate the association between HLS and dyslipidemia.

Methods

A cross-sectional study was conducted on 6,690 participants from the baseline phase of the Ravansar non-communicable diseases study (RaNCD) cohort study. The HLS score was determined based on four lifestyle components: physical activity, diet, smoking status, and obesity. Binary logistic regression models were used to analyze the association between dyslipidemia and HLS quartiles.

Results

The average age of the participants was 45.76 ± 7.76 years, with 47.49% being male and 41.26% residing in rural areas. Anthropometric indices decreased across the HLS quartiles (Ptrend < 0.001). The intake of whole grains, fruits, vegetables, legumes, white meat, eggs, and nuts was significantly higher in the fourth quartile compared to the first quartile of HLS (Ptrend < 0.05). After adjusting for age, sex, socioeconomic status, drinking habits, and residence, the odds of dyslipidemia decreased in the second (OR: 0.66, 95% CI: 0.57–0.81), third (OR: 0.55, 95% CI: 0.47–0.64), and fourth quartiles of HLS (OR: 0.37, 95% CI: 0.30–0.46) compared to the first quartile (Ptrend < 0.001).

Conclusion

This study shows that a higher HLS may be associated with a lower likelihood of dyslipidemia. Adopting a healthy lifestyle that includes smoking cessation, regular physical activity, weight control, and a nutritious diet may help prevent dyslipidemia.

Dyslipidemia

lifestyle

blood lipid

diet

physical activity

Dyslipidemia is defined as elevated levels of blood lipids, including low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), triglycerides (TG), or decreased levels of high-density lipoprotein cholesterol (HDL-C) and is recognized as a modifiable risk factor for cardiovascular diseases (CVD) [1–4] A study has demonstrated that for every 39 milligrams per deciliter reduction in LDL-C levels, there is a corresponding decrease in the mortality rate from all causes by 10% and from cardiovascular events by 22% [3].

A study conducted in 2021 reported a high prevalence of dyslipidemia in Iranian women (62.5%) and men (48.5%), with an overall prevalence of 56.6%. The 5-year incidence rate of dyslipidemia was also observed to be 2.58 persons/100 person-years (3.24 in women vs. 2.20 in men) [5].

Unhealthy lifestyles including sedentary behavior, short sleep, high salt and oil diet, smoking and obesity have been associated with dyslipidemia [6–9]. Indeed, scientific evidence has demonstrated that risk factors such as unhealthy lifestyles, including sedentary behavior, poor diet, smoking, and obesity, are interconnected. Previous studies have investigated the association between the lifestyle index and various chronic diseases, including cardio-metabolic conditions, neuroendocrine disorders, cancer, and fatty liver. The findings consistently indicate an inverse association between a higher healthy lifestyle index (HLS) and the occurrence of these diseases [10–12]. Gao et al.'s study in North China shows that dyslipidemia is negatively correlated with HLS [13]. In a study based on cluster analysis, a higher prevalence of dyslipidemia was reported in a cluster whose population had long-term sedentary behavior, the shortest sleep, and a high salt and oil diet [7]. A study on the Iranian population has shown that the use of statins for dyslipidemia patients is low, and 44.2% of patients who are aware of their dyslipidemia receive statin treatment [14]. Therefore, a healthy lifestyle is recommended as a primary strategy to prevent and control dyslipidemia without receiving lipid-lowering drugs [15].

Despite the high prevalence of dyslipidemia in Iran [5], there is limited research on the association between the HLS and dyslipidemia among the Iranian population. Therefore, the aim of this study is to investigate the potential association between HLS and dyslipidemia in adults residing in western Iran.

Participants

This study conducted a cross-sectional analysis of the baseline phase data from the Ravansar non-communicable diseases study (RaNCD) cohort. The RaNCD cohort is one of the 19 cohorts of Iranians that began in 2014 and is still ongoing in Ravansar city, located in Kermanshah province (16). The baseline phase of the RaNCD cohort included a total of 10,047 adults aged 35 to 65 who resided in both the city and villages of Ravansar (17). For the present study, all participants from the baseline phase were included initially. After applying exclusion criteria, a final sample of 6,690 participants was included for analysis (Fig. 1).

Measurements

Physical activity assessment

The physical activity of the participants was assessed using the Prospective Epidemiological Research Studies in Iran (PERSIAN) cohort standard questionnaire, which includes 22 questions about sports, work, and leisure activities over a 24-hour period, based on MET/hour per day [16].

Smoking assessment

Smoking status was assessed based on the criteria used in the National Health Interview Survey (NHIS). Individuals who reported having smoked more than 100 cigarettes in their lifetime were categorized as current smokers. On the other hand, the term "former smoker" was used to describe individuals who had smoked cigarettes and/or tobacco in the past but have since quit [17].

Anthropometry assessment

The participants' height was measured using the BSM 370 automatic stadiometer (Biospace Co., Seoul, Korea). Body composition and anthropometric indicators, including body mass index (BMI), visceral fat area (VFA), waist hip ratio (WHR), waist circumference (WC) and percent body fat (PBF) were measured using a Bio-Impedance Analyzer BIA (Inbody 770, Inbody Co, Seoul, Korea). BMI was calculated as body weight (kg) divided by height (m²). Participants with a BMI equal to or greater than 30 kg/m² were considered obese.

Healthy eating index

Nutritional information of the participants was collected using a semi-quantitative 118-item food frequency questionnaire (FFQ). The validity and reliability of this questionnaire have been previously confirmed and published [18].

The Healthy Eating Index-2015 (HEI-2015) was calculated based on the FFQ data using the method described by Krebs-Smith et al. [19]. The HEI-2015 consists of 13 food items that are divided into two categories: adequate and moderation. Food groups that are emphasized to be consumed in sufficient amounts include whole fruits, total fruits, total protein foods, seafood and plant proteins, greens and beans, total vegetables, whole grains, dairy products, and fatty acids. Participants with the highest intake of these items received the highest score. The moderation group includes refined grains, sodium, added sugars, and saturated fats. Participants who consumed the least of these items were given the highest score. Participants were given points from 0 to 10 for each food item, and then all the scores were added together to obtain a final score between 0 and 100. In general, a higher HEI-2015 score indicates better diet quality.

Healthy lifestyle score

The healthy lifestyle score in this study was calculated based on the Patel et al.'s study. The score consists of four dual components; each assigned a score of either 0 or 1. The four components are:

Physical activity: Participants were classified as either inactive or active, with a score of 0 assigned to inactive individuals and a score of 1 assigned to active individuals.

Diet: The HEI-2015 was used to assess diet. Participants were assigned a score of 0 for poor diet quality and a score of 1 for good diet quality.

Smoking status: Participants were classified as either non-smokers (score of 0) or smokers (score of 1).

Obesity: Participants were classified as either non-obese (score of 0) or obese (score of 1).

The scores obtained for each component were then summed to calculate the total HLS for each participant. The total scores ranged from 0 (indicating the least conformance to the HLS) to 4 (indicating the highest conformance to the HLS), (Fig. 2).

Outcome assessment

Dyslipidemia was defined as TC (mg/dL) ≥ 240 and/or LDL-C (mg/dL) ≥ 160 and/or HDL-C (mg/dL) < 40 and/or TG (mg/dL) ≥ 200 and/or taking medicine for dyslipidemia [16].

Assessment of other variables

Demographic data was collected using a digital questionnaire administered by trained experts. Socio-economic status (SES) was determined using the principal component analysis method, taking into account factors such as education, residence, welfare facilities, and wealth. SES was then classified into three groups: low, moderate, and high. Alcohol consumption was defined as drinking approximately 200 ml of beer or 45 ml of liquor once a week for at least six months. To measure liver enzymes, lipid profile, and blood sugar, plasma and serum samples were obtained from the participants' forearm vein after 8 to 12 hours, following the protocol of the RaNCD cohort study.

Statistical analysis

Descriptive results are presented with mean ± standard deviation (SD) or frequency (percentage). To compare basic characteristics, anthropometric indices, biochemical markers and dietary intake between HLS quartiles, one-way ANOVA and chi-square tests were conducted. Binary logistic regression analysis was employed in both crude and adjusted models to investigate the association between dyslipidemia and HLS quartiles, while controlling for potential confounding factors such as age, sex, SES, drinking and residence. P values < 0.05 were considered significant for all analyses. The statistical software STATA version 14.2 (Stata Corp, College Station, TX) was used for all data analysis.

This study examined a total of 6,690 participants, with an average age of 45.76 ± 7.76 years. Of the participants, 47.49% were male, and 41.26% resided in rural areas. The majority of both urban and rural populations were found in the second and third quartiles of the HLS. The percentage of current smokers was significantly lower in the highest quartile of HLS compared to the lowest quartile (P < 0.001). Additionally, the percentage of individuals who reported drinking alcohol was significantly lower in the fourth quartile of HLS compared to the first quartile (P < 0.001), (Table 1).

Table 1

Basic characteristics of study participants according to quartiles of healthy lifestyle score
Variables	Healthy lifestyle score				P value trend*
Variables	Q1 (n = 860)	Q2 (n = 2,487)	Q3 (n = 2,723)	Q4 (n = 620)	P value trend*
Demographic
Age, (year)	46.52 ± 7.84	46.03 ± 7.71	45.42 ± 7.76	45.15 ± 7.74	0.089
Sex
Men	551 (17.34)	1185 (37.30)	1132 (35.63)	309 (9.73)	< 0.001
Women	309 (8.80)	1320 (37.06)	1591 (45.29)	311 (8.85)	< 0.001
Residence
Urban	501 (12.75)	1383 (35.19)	1668 (42.44)	387 (9.62)	< 0.001
Rural	359 (13.01)	1104 (40.00)	1055 (38.22)	242 (8.77)	< 0.001
Socioeconomic status
Low	239 (11.10)	824 (38.27)	908 (42.17)	182 (8.45)	0.001
Moderate	272 (12.33)	828 (37.53)	873 (39.57)	233 (10.56)
High	348 (14.95)	835 (35.87)	940 (40.38)	205 (8.81)
Personal habits
Smoking
Never	417 (7.77)	1965 (36.59)	2415 (44.97)	573 (10.67)	< 0.001
Current	358 (45.84)	332 (42.51)	85 (10.88)	6 (0.76)
Former	40 (8.71)	162 (35.29)	210 (45.75)	47 (10.24)
Physical activity (MET/h per day)
Non-active	701 (35.78)	992 (50.64)	260 (13.27)	6 (0.30)	< 0.001
Active	159 (3.36)	1495 (31.60)	2457 (51.93)	620 (13.11)	< 0.001
Alcohol drinking
No	773 (12.10)	2387 (37.38)	2628 (41.15)	598 (9.36)	< 0.001
Yes	87 (28.62)	100 (32.89)	95 (31.25)	22 (7.24)	< 0.001
* P- value was obtained one-way ANOVA and Chi square tests.

The study found that the mean of VFA was significantly lower in the fourth quartile of the HLS compared to the first quartile (45.89 ± 129.79 vs. 26.10 ± 72.99, Ptrend < 0.001). Additionally, BMI, WHR, WC, and PBF showed a decreasing trend from the first to the fourth quartile of HLS (Ptrend < 0.001). Compared to individuals in the lowest quartile of HLS, those in the highest quartile had lower levels of FBS, TG, LDL-C, and TC and higher HDL-C (Ptrend < 0.001). Liver enzymes, including ALT and GGT, also showed a decreasing trend in quartiles of HLS (Ptrend < 0.001). Participants in the fourth quartile of HLS received a higher percentage of energy from protein (Ptrend = 0.001) and a lower percentage of energy from fats (Ptrend < 0.001) compared to those in the first quartile. The intake of whole grains was significantly lower in the first quartile of HLS compared to the fourth quartile (6.88 ± 0.41 vs. 12.50 ± 0.48, Ptrend = 0.001). Conversely, the intake of fruits, vegetables, legumes, white meat, eggs, and nuts was significantly higher in the fourth quartile than the first quartile of HLS. Furthermore, refined grains were lower in the fourth quartile compared to the first quartile of HLS (Ptrend = 0.013), (Table 2).

Table 2

Anthropometric, biochemical and nutritional characteristics of study participants
Variables	Healthy lifestyle score				P value trend*
Variables	Q1 (n = 860)	Q2 (n = 2,487)	Q3 (n = 2,723)	Q4 (n = 620)
Anthropometric indices
BMI (kg/m²)	28.63 ± 3.91	28.06 ± 4.25	26.61 ± 4.73	22.57 ± 1.99	< 0.001
VFA (m³)	129.79 ± 45.89	127.04 ± 48.80	114.23 ± 51.94	72.99 ± 26.10	< 0.001
WHR	0.96 ± 0.06	0.94 ± 0.06	0.93 ± 0.06	0.89 ± 0.04	< 0.001
WC (cm)	99.81 ± 9.10	98.40 ± 9.57	95.10 ± 10.70	87.28 ± 7.12	< 0.001
PBF	34.21 ± 8.59	34.66 ± 9.18	32.90 ± 9.71	26.35 ± 7.44	< 0.001
Biochemical markers
FBS (mg/dL)	91.13 ± 10.01	90.24 ± 9.43	89.72 ± 9.43	87.70 ± 8.60	< 0.001
TG (mg/dL)	154.01 ± 88.26	133.85 ± 71.17	124.02 ± 71.06	107.06 ± 62.66	< 0.001
LDL-C (mg/dL)	113.51 ± 30.14	112.13 ± 30.64	110.91 ± 30.73	106.14 ± 30.63	< 0.001
HLD-C (mg/dL)	42.94 ± 10.63	46.41 ± 11.42	47.90 ± 11.27	49.30 ± 11.31	< 0.001
TC (mg/dL)	187.13 ± 36.31	185.30 ± 36.63	183.62 ± 36.81	176.85 ± 35.74	< 0.001
ALT (mg/dL)	27.15 ± 18.10	25.01 ± 14.19	23.64 ± 14.04	22.10 ± 14.63	< 0.001
AST (mg/dL)	21.95 ± 12.45	21.29 ± 7.43	21.13 ± 8.14	21.13 ± 7.30	0.099
GGT (mg/dL)	27.53 ± 23.37	24.19 ± 19.68	21.90 ± 16.74	19.88 ± 15.24	< 0.001
Dietary intake
Energy (kcal/d)	2608.34 ± 723.96	2561.54 ± 736.43	2564.68 ± 726.42	2601.74 ± 764.35	0.272
Carbohydrate (%E)	60.67 ± 6.43	61.21 ± 6.32	61.59 ± 5.99	61.71 ± 5.64	0.004
Fat (%E)	27.57 ± 6.32	27.01 ± 6.15	26.77 ± 5.74	26.45 ± 5.32	0.001
Protein (%E)	13.30 ± 2.17	13.50 ± 2.31	13.79 ± 2.12	14.25 ± 2.14	< 0.001
Whole grains (g/d)	6.88 ± 0.41	8.95 ± 0.24	11.10 ± 0.23	12.50 ± 0.48	0.001
Refined grains(g/d)	521.58 ± 5.12	524.02 ± 3.01	509.58 ± 2.87	506.47 ± 6.01	0.013
Fruits (g/d)	200.22 ± 6.20	238.46 ± 3.64	298.28 ± 3.48	322.15 ± 7.30	< 0.001
Vegetables (g/d)	249.81 ± 5.25	251.92 ± 3.10	280.90 ± 2.95	268.92 ± 6.18	0.001
Dairy (g/d)	429.29 ± 11.56	438.20 ± 6.78	444.51 ± 6.49	427.85 ± 13.61	0.551
Legumes (g/d)	26.30 ± 0.95	30.26 ± 0.56	36.38 ± 0.54	45.19 ± 1.12	0.001
Red meat (g/d)	22.06 ± 0.90	20.63 ± 0.53	22.37 ± 0.52	22.81 ± 1.05	0.070
White meat (g/d)	48.32 ± 1.24	49.92 ± 0.73	50.29 ± 0.70	53.52 ± 1.46	0.055
Egg (g/d)	22.63 ± 0.64	20.86 ± 0.38	21.72 ± 0.36	23.57 ± 0.76	0.005
Nuts (g/d)	7.35 ± 0.34	7.86 ± 0.21	9.52 ± 0.19	10.42 ± 0.40	0.001
*P- value was obtained one-way ANOVA test The mean ± SE of dietary intake is adjusted for daily energy intake Abbreviation: BMI: Body mass index, HDL-C: High-density lipoprotein cholesterol, LDL-C: Low-density lipoprotein cholesterol, TG: Triglycerides, T-C: Total cholesterol, FBS: Fasting blood sugar, AST: Aspartate transaminase, ALT: Alanine transaminase, GGT: Gamma-glutamyl transferase; WHR: Waist hip ratio; VFA: Visceral fat area; PBF: Percent body fat ;WC: Waist circumference; Q: quartile

After adjusting for age and sex, the study found that the odds of dyslipidemia decreased in the second (OR: 0.68, 95% CI: 0.58, 0.80), third (OR: 0.56, 95% CI: 0.48, 0.57), and fourth quartiles of the HLS (OR: 0.38, 95% CI: 0.30, 0.48) compared to the first quartile (Ptrend < 0.001). Furthermore, after adjusting for age, sex, drinking, socioeconomic status (SES), and residence, the odds of dyslipidemia decreased across HLS quartiles (Ptrend < 0.001), (Table 3).

Table 3

Association between the healthy lifestyle score and dyslipidemia by logistic regression models
	Healthy lifestyle score				P value trend
	Q1	Q2	Q3	Q4	P value trend
Model 1	1	0.60 (0.52, 0.71)	048 (0.41, 0.56)	0.35 (0.28, 0.44)	< 0.001
Model 2	1	0.68 (0.58, 0.80)	0.56 (0.48, 0.57)	0.38 (0.30, 0.48)	< 0.001
Model 3	1	0.66 (0.57, 0.81)	0.55 (0.47, 0.64)	0.37 (0.30, 0.46)	< 0.001
Model 1: Unadjusted Model 2: Adjusted for age, sex Model 3: Adjusted for age, sex, drinking, SES and residence

The present study demonstrated that there is a significant inverse association between the HLS and dyslipidemia, independent of potential confounding factors. Therefore, a higher HLS may be associated with a reduced probability of developing dyslipidemia.

The present study, after adjusting for age, sex, smoking, physical activity, SES, BMI, and residence, found that the odds of dyslipidemia decreased by 34%, 45%, and 63% in the second, third, and fourth quartiles of the HLS, respectively. This study is the first of its kind in Iran to investigate the association between HLS and dyslipidemia. The findings are consistent with a study conducted in China, which also showed a negative correlation between dyslipidemia and HLS [13]. Another study in China, using cluster analysis, found a higher prevalence of dyslipidemia in a cluster characterized by long-term sedentary behavior, short sleep duration, and a high intake of salt and oil [7]. Several studies have explored the association between HLS and metabolic diseases such as diabetes, CVD, metabolic syndrome, and cancer, consistently reporting an inverse relationship [10–12]. While there are limited studies specifically examining the association between the HLS index and dyslipidemia in adults, the available evidence suggests that adopting a healthy lifestyle may reduce the risk of dyslipidemia [7, 13]. Lifestyle is a modifiable factor, and by improving it, dyslipidemia can be prevented. Moreover, in individuals already diagnosed with dyslipidemia, lifestyle modifications and cessation of unhealthy behaviors may help control the condition.

Antoniazzi et al.'s study found that the Mediterranean diet is associated with a reduction in blood lipid disorders and related inflammatory profiles in hypercholesterolemia [20]. Pasdar et al.'s study also demonstrated that an inflammatory diet, high protein and sugar diet, or an increased energy intake is associated with a higher probability of dyslipidemia [21]. Smoking has been identified as an independent risk factor for dyslipidemia.

Moreover, a study of 7,586 adults found that heavy smokers had significantly lower HDL-C and TC levels than light smokers after quitting smoking [22]. Sedentary behaviors and general and central obesity have also been reported to have a positive relationship with dyslipidemia in various studies [7, 8, 23].

In this study, it was observed that anthropometric indices significantly decreased across the quartiles of the HLS. This finding is consistent with previous research by Zaid et al., who found that an increase in anthropometric indices such as weight, body fat, BMI, WC and WHR is associated with an increased risk of dyslipidemia. These anthropometric indices are considered valid and accurate tools for predicting dyslipidemia [24, 25]. VFA, which is an important anthropometric indicator, has also been found to be related to dyslipidemia and CVD, and it is strongly influenced by lifestyle [26, 27]. Therefore, the evidence suggests that individuals who have a healthy lifestyle tend to have normal anthropometric indices, which reduces the risk of dyslipidemia.

Biochemical markers such as FBS and liver enzymes (ALT and GGT) were also found to decrease across the quartiles of HLS. Previous studies have shown a relationship between dyslipidemia and abnormal levels of biochemical factors [28]. Kerr et al.'s study, for example, reports that markers of insulin resistance are strongly associated with increased TG and decreased HDL-C levels [29]. Therefore, it is a plausible hypothesis that lifestyle factors can influence biochemical markers and contribute to the development of dyslipidemia.

Diet is indeed an important component of the HLS, and both the present study and previous research have consistently shown that diet plays a significant role in dyslipidemia [21, 29, 30]. Following a healthy diet, such as the Mediterranean diet, and avoiding western foods that are high in sugar and fat are strongly recommended for improving the HLS and preventing dyslipidemia.

This study is the first of its kind in Iran to investigate the combined role of lifestyle factors in predicting dyslipidemia. The large sample size is strength of this study, and the researchers were able to control for potential confounding factors. However, there are a few limitations to consider. Firstly, the study design is cross-sectional, which means that the observed relationships are not causal. To establish causality, prospective studies would be needed. Secondly, the use of a FFQ introduces inherent measurement errors. However, given that the data was collected in a RaNCD study center by trained experts, the impact of this error can be considered minimal.

The present study demonstrated that there is a significant inverse association between the HLS and dyslipidemia, independent of potential confounding factors. This suggests that adopting a higher HLS may be associated with a reduced probability of developing dyslipidemia. To prevent dyslipidemia, it is recommended to improve lifestyle factors such as quitting smoking, engaging in regular physical activity, maintaining a healthy weight, and following a healthy diet. Subsequently, the burden of cardio-metabolic diseases can be reduced in communities.

HLS Healthy lifestyle index

RaNCD Ravansar non-communicable diseases

LDL-C Low-density lipoprotein cholesterol

TC Total cholesterol

TG Triglycerides

HDL-C High-density lipoprotein cholesterol

CVD Cardiovascular diseases

PERSIAN Prospective Epidemiological Research Studies in Iran

NHIS National Health Interview Survey

BMI Body mass index

WHR Waist hip ratio

VFA Visceral fat area

PBF Percent body fat

WC Waist circumference

FFQ Food frequency questionnaire

HEI-2015 Healthy Eating Index-2015

SES Socio-economic status

PCA Principal Component analysis

OR Odds ratio

CI Confidence interval

Authors’ contributions

Sepehr Sadafi and Ali Azizi conceived the idea of the study. Farid Najafi developed the statistical analysis plan and conducted statistical analyses. Yahya Pasdar, Sepehr Sadafi and Ali Azizi contributed to the interpretation of the results. Sepehr Sadafi and Ali Azizi drafted the original manuscript. Ali Azizi, Yahya Pasdar and Farid Najafi supervised the conduct of this study. All authors reviewed the manuscript draft and revised it critically on intellectual content. All authors approved the final version of the manuscript to be published.

Ethics approval and consent to participate

The study was approved by the ethics committee of Kermanshah University of Medical Sciences (KUMS.REC.1394.318). All methods were carried out in accordance with relevant guidelines and regulations. All the participants were provided oral and written informed consent. All methods were carried out by relevant guidelines and regulations. This study was conducted by the Declaration of Helsinki.

Consent for publication

Not applicable.

Availability of data and materials

The data analyzed in the study are available from the corresponding author upon reasonable request.

Competing interests

The authors declare no conflicts of interest.

Funding Sources

This research was supported by Kermanshah University of Medical Sciences (grant number: 92472). The Iranian Ministry of Health and Medical Education has also contributed to the funding used in the PERSIAN Cohort through Grant no 700/534.

Acknowledgements

The authors thank the PERSIAN cohort Study collaborators and of Kermanshah University of Medical Sciences.

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No competing interests reported.

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Association between healthy lifestyle score and dyslipidemia; a population based study

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Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Participants

Measurements

Physical activity assessment

Smoking assessment

Anthropometry assessment

Healthy eating index

Healthy lifestyle score

Outcome assessment

Assessment of other variables

Statistical analysis

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1