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Abstract

Dyslipidemia is linked to various health complications, including cardiovascular disease and inflammation. This study aimed to assess the association between smoking and lipid profile in the Tabari cohort population. Data from the Tabari Cohort Study involving 4,149 men were analyzed. A standardized questionnaire collected smoking history, while blood samples measured lipid levels and anthropometric measurements were recorded. Statistical analysis utilized chi-square tests and logistic regression, adjusting for potential confounders. The prevalence of smoking was 893 (21.52%; urban: 20.6%, mountainous: 23.8%, significant level: .024). The adjusted odds ratio (OR) of low high-density lipoprotein (HDL) among smokers 1.48 (95% confidence interval [CI]: 1.25–1.77, p < .001) was the same as non-smokers. The adjusted OR of high low-density lipoprotein (LDL) in men with 1 to 10, 11 to 20, and more than 20 cigarettes per day was 0.95 (95% CI: 0.73–1.25), 1.30 (95% CI: 0.99–1.71), and 2.64 (95% CI: 1.32–5.27) and low HDL was equal to 1.34 (95% CI: 1.06–1.68), 1.61 (95% CI: 1.26–2.05), and 2.24 (95% CI: 1.13–4.42) compared with non-smokers, respectively. The study findings indicate that smoking is associated with lower HDL levels, even after adjusting for potential confounders. The odds of low HDL and high LDL increases with higher smoking intensity. The low HDL and high LDL levels in individuals smoking over 20 cigarettes/day, respectively, show a 2.24-fold and a 2.64-fold increased odds compared to non-smokers. These findings highlight the importance of smoking cessation in relation to lipid profiles and related health risks.

Keywords

lipid profile smoking cohort study cholesterol HDL LDL triglycerides dose–response

Introduction

The human serum contains a variety of lipid subclasses that can exert diverse impacts on the individual’s physiological state. The maintenance of optimal levels of each subclass is crucial for the promotion and preservation of overall health and well-being (Ambroży et al., 2022).

The presence of dyslipidemia in the male population can heighten the susceptibility to a range of health complications, such as cardiovascular disease, metabolic syndrome, and atherosclerosis (Ambroży et al., 2022; Grandys et al., 2021; Ostovar et al., 2017; Parikh et al., 2022).

Elevated levels of triglycerides (TG) have been demonstrated to increase the likelihood of developing cardiovascular disease (Folin & Contiero, 1994). Similarly, high levels of total cholesterol (TC) have been linked to an augmented risk of cardiovascular disease (Mills & Wilkinson, 1966). On the other hand, high-density lipoprotein (HDL), commonly referred to as “good cholesterol”, has been associated with a reduced risk of cardiovascular disease (Folin & Contiero, 1994). Conversely, low-density lipoprotein (LDL), or “bad cholesterol”, has been associated with an increased risk of cardiovascular disease (Folin & Contiero, 1994). The adoption of lifestyle modifications to maintain optimal levels of these serum lipids can help mitigate the risk of developing cardiovascular disease. In certain instances, pharmacological interventions may also be necessary to manage high serum lipid levels. Ongoing monitoring of serum lipid levels through regular consultations with a health care provider can aid in the early detection and management of potential issues (Folin & Contiero, 1994; Grandys et al., 2021; Mills & Wilkinson, 1966).

In men, smoking has been associated with dyslipidemia, characterized by elevated levels of TC, TG, and LDL-cholesterol, as well as decreased levels of HDL-cholesterol. The severity of dyslipidemia has been observed to increase with the extent of smoking (Jeong, 2022). A study involving healthy middle-aged men revealed that heavy smokers exhibited significantly lower levels of serum HDL, LDL, and TC, while their TG levels were significantly higher compared to non-smokers (Wakabayashi, 2008). Several studies have indicated a potential link between smoking and aging, with a noteworthy prevalence of smoking, particularly cigarette smoking, observed among the elderly population in Iran (Huang et al., 2022; Vatankhah et al., 2020). Notably, a research has demonstrated a significant association between the prevalence of smoking, including waterpipe tobacco smoking, and age, as well as the presence of cardiovascular disease, among Iranian adults aged 35 to 70 (Moghadam et al., 2023; Zahirian Moghadam et al., 2021).

Some findings suggest that smoking can have implications in relation to aging, and that the prevalence of smoking, particularly cigarette smoking, is notable among the elderly in Iran.

It is considerable that smoking is frequently linked to other lifestyle factors that can influence serum lipid levels, which can complicate the isolation of the precise effects of smoking on these levels. Numerous studies have consistently reported an association between smoking and elevated levels of TC, TG, and LDL-cholesterol, as well as reduced levels of HDL-cholesterol, all of which are established risk factors for cardiovascular disease (Linna et al., 2008; Wakabayashi, 2008; Yamamoto et al., 2003).

However, further research is required to comprehensively elucidate the dissimilarities in the impact of smoking on serum lipid. In the present study, we aimed to determine the association between smoking and lipid profile in the male population aged 35 to 70 years old based on cohort study.

Method

Design

The present study is a cross-sectional study conducted within the data collected in the enrolment phase of the Tabari Cohort Study (TCS). TCS is a part of the Prospective Epidemiological Research Studies in Iran (PERSIAN), a national cohort study, that confirmed by the Mazandaran University of Medical Science ethical committee (IR.MAZUMS.REC.95.2524). In the first phase of TCS, which took place from June 2015 to November 2017, 10,255 individuals aged 35 to 70 years old, from two urban (Sari) and mountainous (Kiasar) regions were enrolled. Details and methods of both TCS and PERSIAN have been published in previous articles (Eghtesad et al., 2017; Kheradmand et al., 2019; Poustchi et al., 2018).

For the present study, we have utilized data of all men who have participated in TCS (4,149 individuals). In total, 2,946 individuals from the urban and 1,203 individuals from the mountainous regions were analyzed.

Data Collection

Data collection methods consist of questionnaires, anthropometric measurements, and laboratory examination. A standardized questionnaire with established reliability and validity, approved by the central team of the PERSIAN cohort, was used to collect data. The questionnaire included multiple items related to general information, socioeconomic status, employment history, physical activity, and smoking history. In this study, individuals classified as smokers were defined as those who reported a consistent smoking habit of at least one cigarette per day for a minimum duration of 1 month or those who had recently quit smoking, specifically within a month prior to their participation in the research (Moghadam et al., 2023).

After a 12-h fasting, blood samples were taken from all participants in TCS for laboratory tests. TC, TG, LDL-cholesterol, and HDL-cholesterol levels were measured using a BT 1500 analyzer (Biotecnica, Italy).

Then, trained personnel measured anthropometric indices, including waist circumference (WC) and hip circumference, according to the standard protocol (Poustchi et al., 2018).

Covariates

Selected covariates included age (categorized 35–39/40–49/50–59/60–69 years), residency (urban/mountainous), marital status (single/widow/divorced/married), social economic status (from the first quintile as poorest to the fifth quintile as richest groups), education years (no schooling/1–5/6–8/9–12 years/university or college academic degree), metabolic equivalent of task (MET; ≥median/), WC (<102/≥102), waist to hip ratio (WHR; ≤0.9/>0.9), and waist to height ratio (WHtR; <0.05/≥0.05) as previously defined (Poustchi et al., 2018).

Statistical Analysis

The following criteria were considered as normal levels of lipid profile: TC ≤ 200, TG ≤ 150, LDL-cholesterol ≤ 130, and HDL-cholesterol ≥ 40 (male) (Fulks et al., 2009; Luxia et al., 2021).

In the present study, SPSS version 26 was used for data analysis. Variables were described in terms of percentage and number. Comparison of categorical variables between the groups was performed using the chi-square test. Logistic regression analysis was used to investigate the relationship between smoking and lipid profile by adjusting the effect of variables suspected to be confounder. It should be noted that in addition to presenting the results and comparison according to current daily smoking (yes/no), analysis with the dose–response approach and p for the trend of smoking intensity and the pack-year have also been presented.

Results

In this study, 4,149 (40.45%) of the 10,255 cohort population were men aged 35 to 70 years old, all of whom were included in the study. Of the total studied population, 2,946 (71%) resided in urban areas, while 1,203 (29%) were mountain dwellers. The majority of the studied population (1,360 [32.77%]) belonged to the age group of 50 to 59.

Smoking Status According to Participants’ Demographic Variables

Among the male participants, a total of 893 (21.52%) were found to be smokers. The prevalence of the rate of smoking in the urban and mountain populations was 607 (20.6%) and 286 (23.8%), respectively (p = .027; Table 1)

Table 1.

Demographic and Smoking Status of Study Participants

		Smoking (893)	Not smoking (3,256)	p-value
Variables		N (%)	N (%)	p-value
Age (years)	35–39	148 (25.3)	438 (74.7)	<.001
	40–49	317 (24.7)	965 (75.3)
	50–59	295 (21.7)	1,065 (78.3)
	60–70	133 (14.4)	788 (85.6)
Residency	Urban	607 (20.6)	2,339 (79.4)	.027
Residency	Mountainous	286 (23.8)	917 (76.2)	.027
Marital status	Single/widow/divorced	18 (28.6)	45 (71.4)	.170
Marital status	Married	875 (21.4)	3,211 (78.6)	.170
Social economic status	Quintile 1	163 (25.0)	490 (75.0)	.143
	Quintile 2	175 (22.0)	622 (78.0)
	Quintile 3	176 (21.6)	639 (78.4)
	Quintile 4	184 (20.1)	731 (79.9)
	Quintile 5	195 (20.1)	774 (79.9)
Education years	University/college	229 (17.2)	1,104 (82.8)	<.001
	9–12 years	335 (25.2)	993 (74.8)
	6–8 years	128 (25.4)	375 (74.6)
	1–5 years	161 (23.9)	513 (76.1)
	No schooling	40 (12.9)	271 (87.1)
MET	<median	404 (20.1)	1,606 (79.9)	.031
MET	≥Median	489 (22.9)	1,650 (77.1)	.031
BMI (kg/m²)	<25	391 (27.9)	1,009 (72.1)	<.001
	25–29.9	357 (18.8)	1,539 (81.2)
	≥30	145 (17.0)	708 (83.0)
WC	<102	751 (22.3)	2,620 (77.7)	.014
WC	≥102	142 (18.3)	639 (81.7)	.014
WHR	≤0.9	430 (26.5)	1,195 (73.5)	<.001
WHR	>0.9	463 (18.3)	2,061 (81.7)	<.001
WHtR	<0.05	277 (32.1)	587 (67.9)	<.001
WHtR	≥0.05	616 (18.8)	2,669 (81.2)	<.001

Note. MET = metabolic equivalent of task; BMI = body mass index; WC = waist circumference; WHR = waist to hip ratio; WHtR = waist to height ratio.

Table 1 demonstrates a significant association between participants’ age and smoking prevalence. Notably, the prevalence of smoking increased with advancing age, with rates of 148 (25.3%), 317 (24.7%), 295 (21.7%), and 133 (14.4%) observed among smokers aged 35 to 39, 40 to 49, 50 to 59, and 60 to 70, respectively, compared to non-smokers in the same age groups (p < .001). In addition, a significant relationship was found between years of education and smoking behavior. The prevalence of smoking among individuals with different levels of education was as follows: 229 (17.2%), 335 (25.2%), 128 (25.4%), 161 (23.9%), and 40 (12.9%) for those with university/college degrees, 9 to 12 years of education, 6 to 8 years of education, 1 to 5 years of education, and no schooling, respectively, compared to non-smokers with the same educational backgrounds (p < .001).

Our findings also revealed positive correlation between smoking and the MET index, with rates of 404 (20.1%) and 489 (22.9%) observed among smokers below and above the median, respectively, compared to non-smokers below and above the median (p = .031).

Conversely, an inverse relationship was observed between smoking and BMI (391 [27.9%], 357 [18.8%], and 145 [17.0%] were observed for BMI categories <25, 25–29.9, and ≥30 kg/m², respectively, compared to non-smokers in the same categories, p < .001), WC (751 [22.3%] and 142 [18.3%] were observed for smokers with WC measurements <102 and ≥102 cm, respectively, compared to non-smokers, p = .014), WHR (430 [26.5%] and 463 [18.3%] were observed among smokers with ratios ≤0.9 and >0.9, respectively, compared to non-smokers, p < .001), and WHtR (277 [32.1%] and 616 [18.8%] were observed among smokers with WHtR <0.05 and ≥0.05, respectively, compared to non-smokers, p < .001; Table 1).

Lipid Profile According to Participants’ Demographic Variables

Regarding abnormal lipid profile levels, the study identified 959 (23.1%) cases of abnormal HDL, 741 (17.9%) cases of abnormal LDL, 1,303 (31.4%) cases of abnormal TC, and 1,921 (46.3%) cases of abnormal TG among the 4,149 male individuals from the Tabari cohort.

The analysis revealed that high level of LDL, TG, and TC as well as low level of HDL were significantly different in different age group and socioeconomic status. Abnormal lipid profile levels were significantly higher in urban residents compared to other areas, with rates of 569 (19.3%), 724 (24.6%), 1,540 (52.3%), and 1,010 (34.3%) for LDL, HDL, TG, and TC, respectively (p < .001). However, no significant differences were observed in abnormal lipid profiles based on marital status. Additional demographic and clinical variables are detailed in Table 2.

Table 2.

Demographic and Clinical Variables of Study Participants

		LDL (≥130)	p-value	HDL (≤40)	p-value	TG (≥150)	p-value	TC (≥200)	p-value
Variables		N (%)	p-value	N (%)	p-value	N (%)	p-value	N (%)	p-value
Age (years)	35–39	88 (15.0)	.017	153 (26.1)	.053	284 (48.5)	<.001	176 (30.0)	.007
	40–49	222 (17.3)		308 (24.0)		677 (52.8)		433 (33.8)
	50–59	277 (20.4)		311 (22.9)		623 (45.8)		443 (32.6)
	60–70	154 (16.7)		187 (20.3)		337 (36.6)		251 (27.3)
Residency	Urban	569 (19.3)	<.001	724 (24.6)	<.001	1,540 (52.3)	<.001	1,010 (34.3)	<.001
Residency	Mountainous	172 (14.3)	<.001	235 (19.5)		381 (31.7)		293 (24.4)
Marital status	Single/widow/divorced	15 (23.8)	.214	11 (17.5)	.283	28 (44.4)	.766	24 (38.1)	.249
Marital status	Married	726 (17.8)	.214	948 (23.2)		1,893 (46.3)		1,279 (31.3)
Social economic status	Quintile 1	101 (15.5)	.002	125 (19.1)	.052	199 (30.5)	<.001	161 (24.7)	<.001
	Quintile 2	133 (16.7)		179 (22.5)		317 (39.8)		234 (29.4)
	Quintile 3	135 (16.6)		186 (22.8)		413 (50.7)		255 (31.3)
	Quintile 4	157 (19.2)		229 (25.0)		487 (53.2)		296 (32.3)
	Quintile 5	215 (22.2)		240 (24.8)		505 (52.1)		357 (36.8)
Education years	University/college	279 (20.9)	.002	314 (23.6)	.202	707 (53.0)	<.001	466 (35.0)	<.001
	9–12 years	219 (16.5)		327 (24.6)		638 (48.)		431 (32.5)
	6–8 years	88 (17.5)		100 (19.9)		224 (44.5)		152 (30.2)
	1–5 years	95 (14.1)		154 (22.8)		263 (39.0)		163 (24.2)
	No schooling	60 (19.3)		64 (20.6)		89 (28.6)		91 (19.3)
MET	<Median	384 (19.1)	.043	518 (25.8)	<.001	1,101 (54.8)	<.001	690 (34.3)	<.001
MET	≥Median	357 (16.7)	.043	441 (20.6)		820 (38.3)		613 (28.7)
BMI (kg/m²)	<25	221 (15.8)	.044	253 (18.1)	<.001	348 (24.9)	<.001	349 (24.9)	<.001
	25–29.9	357 (18.8)		459 (24.2)		1,030 (54.3)		658 (34.7)
	≥30	163 (19.1)		247 (29.0)		543 (63.7)		296 (34.7)
WC	<102	600 (17.8)	.831	732 (31.7)	<.001	1,421 (42.2)	<.001	1,047 (31.1)	.317
WC	≥102	141 (18.1)	.831	227 (29.2)		500 (64.3)		256 (32.9)
WHR	≤0.9	260 (16.0)	.012	289 (17.8)	<.001	495 (30.5)	<.001	433 (26.6)	<.001
WHR	>0.9	481 (19.1)	.012	670 (26.5)		1,426 (56.5)		870 (34.5)
WHtR	<0.05	108 (12.5)	<.001	141 (16.3)	<.001	161 (18.6)	<.001	165 (19.1)	<.001
WHtR	≥0.05	633 (19.3)	<.001	818 (24.9)		1,760 (53.6)		1,138 (34.6)

Note. MET = metabolic equivalent of task; BMI = body mass index; WC = waist circumference; WHR = waist to hip ratio; WHtR = waist to height ratio; LDL = low-density lipoprotein; HDL = high-density lipoprotein; TC = total cholesterol; TG = triglycerides.

There was a significant relationship between demographic variables such as age, socioeconomic status, and education level, and abnormally high levels of LDL, TG, and TC. Place of residence, MET index, BMI, WHR, and WHtR were significantly associated with abnormal levels of all four lipid profile factors (LDL, HDL, TG, and TC). However, only WC showed a significant relationship with abnormal HDL and TG. No significant relationship was found between marital status and lipid profile levels. These demographic variables were examined to determine the role of confounding factors in the study (Table 2).

Analysis of Lipid Profiles Based on Smoking Status

Among total men participated in TCS, 893 individuals (21.52%) were smokers. Among the smokers, 27.8% (248 individuals) had abnormal HDL levels, while among non-smokers, 21.8% (711 individuals) had abnormal HDL levels (p = .000). As Table 3 shows, TC, LDL, and TG levels were not significantly different between smokers and non-smokers (p = .594, p = .578, p = .089, respectively), whereas HDL level (p < .001) was significantly different between the two groups.

Table 3.

The Comparison of Smoking and Non-Smoking Group Based on Their Lipid Profiles

		Smoking (893)	Non-smoking (3,256)
Lipid profile		N (%)	N (%)	p-value
LDL	≥130	165 (18.5)	576 (17.7)	.587
LDL	<130	728 (81.5)	2,680 (82.3)	.587
HDL	≥40	645 (72.2)	2,545 (78.2)	.000
HDL	<40	248 (27.8)	711 (21.8)	.000
TG	≥150	391 (43.8)	1,530 (47)	.089
TG	<150	502 (56.2)	1,726 (53)	.089
TC	≥200	287 (32.1)	1,016 (31.2)	.594
TC	<200	606 (67.9)	2,240 (68.8)	.594

Note. LDL = low-density lipoprotein; HDL = high-density lipoprotein; TC = total cholesterol; TG = triglycerides

Logistic Regression Analysis of Smoking and Lipid Profile

Results of multivariate regression analysis showed that odds of low-level HDL in smoker participant was 1.48 (1.25–1.77) higher than non-smoker participant (p < .001; Table 5). Our finding showed after adjusting for possible confounding variables including age, BMI, MET index, residency, marital status, social economic level, educational level, and WHR, only the frequency of people with low HDL was significantly higher in smokers, 38% (odds ratio [OR]: 1.38) more than in non-smokers (Table 4).

Table 4.

Association Between Current Smoking and Lipid Profile Using Univariate and Multivariate Regression

		Univariate		Multivariate^a
Lipid profile	OR [95% CI]	p-value	OR [95% CI]	p-value
LDL	1.05 [0.87–1.27]	.587	1.15 [0.95–1.41]	.143
HDL	1.38 [1.16–1.62]	<.001	1.48 [1.25–1.77]	<.001
TG	0.87 [0.75–1.02]	.089	1.05 [0.89–1.24]	.523
TC	1.04 [0.89–1.22]	.594	1.15 [0.98–1.36]	.084

Adjusted variables: age, body mass index, metabolic equivalent of task, residency, marital status, social economic status, education years, waist circumference, waist to hip ratio, and waist to height ratio. LDL = low-density lipoprotein; HDL = high-density lipoprotein; TC = total cholesterol; TG = triglycerides; OR = odds ratio; CI = confidence interval.

In addition, we analyzed the association between severity of smoking with lipid profile in smoker participants. Results showed a significant relationship between pack-year smoking with HDL and LDL level, based on the result analysis of p for trend with multivariate logistic regression (Table 5). When examining the severity of smoking in relation to non-normal lipid profile levels, a significant direct relationship was observed for LDL (p for trend = .015) and HDL (p for trend < .001) after adjusting for confounder variables (Table 5). The adjusted OR of high LDL in men with 1 to 10, 11 to 20, and more than 20 cigarettes per day was 0.96 (95% CI: 0.73–1.25), 1.30 (95% CI: 0.99–1.71), and 2.64 (95% CI: 1.32–5.27) and low HDL was 1.34 (95% CI: 1.06–1.68), 1.61 (95% CI: 1.26–2.05), and 2.24 (95% CI: 1.13–4.42) compared with non-smokers, respectively. In other words, the OR of LDL and HDL disorders has significantly increased with the increase in the number of cigarettes smoked per day (Table 6).

Table 5.

The Number of Pack-Year Smoking Groups Based on Lipid Profile (Chi-Square and p for Trend With Multivariate Logistic Regression)

Lipid profile	<10 pack-years (459)	11–19 pack-years (396)	≥20 pack-years (38)	p-value	Multivariate^a
Lipid profile	N (%)	N (%)	N (%)		p for trend
LDL ≥130	635 (17.8)	31 (14.3)	71 (20.5)	.174	.015
HDL <40	788 (22.0)	76 (7.9)	95 (9.9)	<.001	.000
TG ≥150	1,682 (46.9)	98 (45.2)	141 (40.6)	.076	.434
TC ≥200	1,130 (31.5)	65 (30.0)	108 (31.1)	.884	.138

Table 6.

Association Between Severity of Smoking and Lipid Profile Using Univariate and Multivariate Logistic Regression

	Reference	UnivariateOR [95% CI]p-value			Reference	Multivariate^aOR [95% CI]p-value
Variables	No	1–10	11–20	>20	No	1–10	11–20	>20
LDL	Reference	0.92 [0.71–1.20].55	1.10 [0.84–1.44].46	2.41 [1.20–4.75].010	Reference	0.95 [0.73–1.25].756	1.30 [0.99–1.71].057	2.64 [1.32–5.27].006
HDL	Reference	1.29 [1.03–1.62].02	1.41 [1.11–1.78].00	2.08 [1.07–4.05].030	Reference	1.34 [1.06–1.68].011	1.61 [1.26–2.05].000	2.24 [1.13–4.42].020
TG	Reference	0.96 [0.79–1.17].74	0.78 [0.63–0.97].28	0.82 [0.42–1.56].549	Reference	1.04 [0.84–1.29].698	1.08 [0.85–1.37].498	0.92 [0.45–1.87].0833
TC	Reference	0.99 [0.80–1.23].98	1.10 [0.88–1.37].38	1.01 [0.51–2.02].960	Reference	1.03 [0.83–1.27].785	1.33 [1.06–1.68].014	1.10 [0.54–2.23].780

Adjusted variables: age, body mass index, metabolic equivalent of task, residency, marital status, social economic status, education years, waist circumference, and waist to hip ratio. LDL = low-density lipoprotein; HDL = high-density lipoprotein; TC = total cholesterol; TG = triglycerides; OR = odds ratio; CI = confidence interval.

Discussion

Association Between Smoking and Lipid Profile

The present study aimed to investigate the association between smoking and lipid profile in a population-based cohort study aged between 35 and 70 years old conducted within the TCS. The results of the present study showed that smoker participants had low level of HDL and this finding remained consistent after adjusting for possible confounding variables. The severity of dyslipidemia was observed to increase with the extent of smoking, indicating a dose–response relationship between smoking and its impact on lipid profile. The dose–response analysis showed that the chance of abnormal level of LDL and HDL in participants who smoked more than 20 pack-years, respectively, is 2.64 and 2.24 times more than participants who smoked less than 10 pack-years.

Previous research has already concluded that smoking is linked to unfavorable changes in lipid profile, including elevated TC, LDL, and TG, as well as decreased HDL levels, all of which are indicators of cardiovascular risk (Girish & Harish, 2018). Hence, based on our findings, it can be concluded that smoking has a significant negative impact on HDL-cholesterol levels, even after accounting for other factors.

Smoking can affect HDL-cholesterol levels and function through various mechanisms, such as altering lipid transport enzymes, causing oxidative modifications, and reducing HDL-cholesterol levels (Assmann et al., 1984). In summary, smoking-induced changes in cholesterol metabolism involve alterations in lipid transport enzymes, oxidative modifications of lipoproteins, and changes in serum cholesterol levels. These effects can impact HDL metabolism, contribute to changes in lipid profile, and potentially increase the risk of cardiovascular disease (He et al., 2013).

Association Between Smoking Intensity and Lipid Profile

Considering our findings, as smoking intensity increases, there is a tendency for TC and TG, while LDL and HDL levels significantly disturb. Such alterations in lipid profile may contribute to the development of dyslipidemia and elevate the risk of cardiovascular disease (Girish & Harish, 2018). Previous researches inferred that smoking has the potential to influence abnormal blood LDL levels by affecting oxidative stress (Ho et al., 2021). Further investigation is required to gain a comprehensive understanding of the underlying mechanisms. Our study did not find significant differences in TC, LDL-cholesterol, and TG levels between smokers and non-smokers and this finding contrasts with some previous research. However, it is important to note that smoking is often associated with other lifestyle factors that can influence lipid levels, making it challenging to isolate the precise effects of smoking alone. Further research is needed to comprehensively elucidate the disparities in the impact of smoking on different lipid subclasses.

Association Between Demographic and Smoking Status of Study Participants

In the present study, the prevalence of smoking was found to be higher in mountainous areas compared to urban areas. This finding is consistent with numerous other studies that have shown a higher likelihood of smoking among rural residents compared to their urban counterparts. This association can be attributed to various factors, including socioeconomic conditions, cultural norms, and limited access to health care facilities. Consequently, there is a clear link between rural residency and smoking, with higher smoking rates observed in rural communities (Axelson et al., 1988; Gergianaki et al., 2019).

Furthermore, our study aligns with several other investigations that highlight a distinct connection between lower education levels and a higher prevalence of smoking. Collectively, these studies indicate that this association is likely influenced by a combination of factors, such as limited access to information regarding the health risks associated with smoking and fewer resources to effectively manage stress (Menvielle, Franck, Radoï, et al., 2016; Menvielle, Franck, Stücker, & Luce, 2016; Yu et al., 2018). In addition, our findings reveal a direct relationship between smoking and MET index, while indicating a negative relationship between smoking and BMI, WC, WHR, and WHtR indices.

Certain studies have suggested two potential reasons for the lower weight indices observed in smokers: an increase in the rate of metabolism and a decrease in appetite. The exact cause of the heightened metabolic rate in smokers remains uncertain, although it is hypothesized that nicotine in cigarettes may stimulate the sympathetic nervous system, or that smoking could enhance the production of adrenaline (Warwick et al., 1987).

Association Between Lipid Profile and Demographic Variables in Smokers

There is no clear evidence regarding demographic characteristics and its relationship with smoking and lipid profile. The present study revealed a significant relationship between the age of smokers and high levels of LDL, TC, and TG. This finding aligns with previous research indicating that middle-aged individuals (45–64 years) who smoke are more susceptible to health disorders (Dube et al., 2019).

Our study established a direct relationship between MET index, BMI, WHR, WHtR, and all four lipid profile factors. This was expected since these indices are commonly associated with obesity, consistent with previous findings (Stępień et al., 2014).

It is crucial to highlight that quitting smoking has the potential to improve serum lipid levels and reduce the risk of cardiovascular disease in some individuals (He et al., 2013). In addition to smoking cessation, there are several lifestyle modifications that can be beneficial in improving the lipid profile of smokers. These include adopting a healthy diet, engaging in regular physical activity, maintaining a healthy weight, and effectively managing stress (Joshi et al., 2013).

It is advisable to seek guidance from a health care professional for personalized advice on improving lipid profile as a smoker. They can offer tailored recommendations and closely monitor progress over time. Consulting with a health care professional ensures that the interventions are suitable for individual circumstances and can maximize the effectiveness of the efforts to enhance lipid profile. Overall, this study emphasizes the need for comprehensive smoking cessation interventions and highlights the role of HDL as a crucial biomarker for assessing cardiovascular risks in individuals who smoke. By prioritizing efforts to reduce smoking prevalence and promote healthier lifestyles, we can empower individuals to make informed decisions for their cardiovascular well-being and ultimately strive for a healthier, smoke-free future.

Strengths and Limitations

Significant strength of this study lies in its large sample size, the use of a standardized questionnaire with established reliability and validity, and the inclusion of comprehensive laboratory measurements for lipid profile assessment. However, there are some limitations to consider. One of the limitations of the current study is its cross-sectional design, which introduces a potential distortion in establishing a causal relationship between lipid profile and smoking. To overcome this limitation, we performed a dose–response analysis (cigarettes per day). It is recommended that future research will explore this relationship using prospective studies. Longitudinal studies would provide more robust evidence in this regard. Second, the study population consisted of Iranian men aged 35 to 70 years old, which may limit the generalizability of the findings to other populations. Future research should aim to replicate these findings in diverse populations.

Further research is needed to gain a deeper understanding of the underlying mechanisms linking smoking and lipid metabolism and to explore the impact of smoking cessation on lipid profile improvement. Longitudinal studies involving diverse populations would provide valuable insights into the long-term effects of smoking on lipid profile and cardiovascular health.

Conclusion

In conclusion, this study provides compelling evidence that smoking is associated with low levels of HDL in men, and this correlation remains even after accounting for potential confounding variables. The dose–response analysis further elucidates the impact of smoking intensity on LDL and HDL levels. Participants with a smoking history surpassing 20 pack-years exhibited an abnormal amount of lipid that was more than twice that of those with a smoking history of less than 10 pack-years. These findings highlight the need for comprehensive smoking cessation interventions and emphasize the role of HDL as a crucial biomarker for assessing cardiovascular risks in individuals who smoke. By prioritizing efforts to reduce smoking prevalence and promote healthier lifestyles, we can empower individuals to make informed decisions for their cardiovascular well-being and ultimately strive for a healthier, smoke-free future.

Footnotes

Acknowledgements

The authors would like to thank all the members of the PERSIAN cohort study (Ministry of Health and Medical Education and Mazandaran University of Medical Sciences).

Authors’ Contributions

Mahmood Moosazadeh and Mona Modanloo acquired data, performed the statistical analyses, interpreted data, drafted and revised the manuscript for important intellectual content, and approved the final version. Motahareh Kheradmand, Pedram Ebrahimnejad, Mehrasa Rostamian, Shamim Mahboobi, Fatemeh Mardanshah, Marzieh Dehghanzadegan, Fatemeh kianmehr, and Aysa Safajoo interpreted data, reviewed the analyses, and approved the final version. All authors have read and approved the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the research deputy of Mazandaran University of Medical Science (grant no. 2524). The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing or decision to publish this manuscript.

Ethics Approval and Consent to Participate

TCS was confirmed by the Mazandaran University of Medical Science ethical committee (IR.MAZUMS.REC.95.2524). All ethical principles of the Helsinki ethical declaration have been met and written informed consent was obtained from all the participants.

Consent for Publication

Not applicable.

ORCID iDs

Motahareh Kheradmand

Mona Modanloo

Availability of Data and Materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

Ambroży

Rydzik

Ł.

Obmiński

Spieszny

Szczepanik

Ambroży

Basiaga-Pasternak

Spieszny

Niewczas

Jaszczur-Nowicki

(2022). Effect of high-intensity strength and endurance training in the form of small circuits on changes in lipid levels in men aged 35–40 years. Journal of Clinical Medicine, 11(17), 5146.

Assmann

Schulte

Schriewer

(1984). The effects of cigarette smoking on serum levels of HDL cholesterol and HDL apolipoprotein AI. Findings of a prospective epidemiological study on employees of several companies in Westphalia. West Germany, 22(6), 397–402.

Axelson

Andersson

Desai

Fagerlund

Jansson

Karlsson

Wingren

(1988). Indoor radon exposure and active and passive smoking in relation to the occurrence of lung cancer. Scandinavian Journal of Work, Environment & Health, 14(5), 286–292.

Dube

S. R.

Liu

Fan

A. Z.

Meltzer

M. I.

Thompson

W. W.

(2019). Assessment of age-related differences in smoking status and health-related quality of life (HRQoL): Findings from the 2016 Behavioral Risk Factor Surveillance System. Journal of Community Psychology, 47(1), 93–103.

Eghtesad

Mohammadi

Shayanrad

Faramarzi

Joukar

Hamzeh

Farjam

Sakhvidi

M. J. Z.

Miri-Monjar

Moosazadeh

Hakimi

(2017). The PERSIAN cohort: Providing the evidence needed for healthcare reform. Archives of Iranian Medicine, 20(11), 691–695.

Folin

Contiero

(1994). Relationship between subcutaneous fat distribution and serum lipids and blood pressures in Italian men. American Journal of Human Biology, 6(4), 457–463.

Fulks

Stout

R. L.

Dolan

V. F.

(2009). Association of cholesterol, LDL, HDL, cholesterol/HDL and triglyceride with all-cause mortality in life insurance applicants. Journal of Insurance Medicine, 41(4), 244–253.

Gergianaki

Fanouriakis

Adamichou

Spyrou

Mihalopoulos

Kazadzis

Chatzi

Sidiropoulos

Boumpas

D. T.

Bertsias

(2019). Is systemic lupus erythematosus different in urban versus rural living environment? Data from the Cretan Lupus Epidemiology and Surveillance Registry. Lupus, 28(1), 104–113.

Girish

Harish

(2018). I. A comparative study of effects of smoking on lipid profile among healthy smokers and non-smokers. Journal of Evidence Based Medicine and Healthcare, 5, 696–698.

10.

Grandys

Majerczak

Zapart-Bukowska

Duda

Kulpa

J. K.

Zoladz

J. A.

(2021). Lowered serum testosterone concentration is associated with enhanced inflammation and worsened lipid profile in men. Frontiers in Endocrinology, 12, 735638.

11.

B.-m.

Zhao

S.-p.

Peng

Z.-y.

(2013). Effects of cigarette smoking on HDL quantity and function: Implications for atherosclerosis. Journal of Cellular Biochemistry, 114(11), 2431–2436.

12.

K.-J.

Chen

T.-H.

Yang

C.-C.

Chuang

Y.-C.

Chuang

H.-Y.

(2021). Interaction of smoking and lead exposure among carriers of genetic variants associated with a higher level of oxidative stress indicators. International Journal of Environmental Research and Public Health, 18(16), 8325.

13.

Huang

Sun

Lee

Maslov

A. Y.

Shi

Waldman

Marsh

Siddiqui

Dong

Peter

Sadoughi

(2022). Single-cell analysis of somatic mutations in human bronchial epithelial cells in relation to aging and smoking. Nature Genetics, 54(4), 492–498.

14.

Jeong

(2022). Association between dual smoking and dyslipidemia in South Korean adults. PLOS ONE, 17(7), e0270577.

15.

Joshi

Shah

Mehta

Gokhle

(2013). Comparative study of lipid profile on healthy smoker and non smokers. International Journal of Medical Science and Public Health, 2(3), 622–626.

16.

Kheradmand

Moosazadeh

Saeedi

Poustchi

Eghtesad

Esmaeili

Alizadeh-Navaei

Hedayatizadeh-Omran

Nikaeein

Rafiei

Janbabaee

(2019). Tabari cohort profile and preliminary results in urban areas and mountainous regions of Mazandaran, Iran. Archives of Iranian Medicine, 22(6), 279–285.

17.

Linna

M. S.

Ahotupa

Irjala

Pöllänen

Huhtaniemi

Mäkinen

Perheentupa

Vasankari

T. J.

(2008). Smoking and low serum testosterone associates with high concentration of oxidized LDL. Annals of Medicine, 40(8), 634–640.

18.

Luxia

Jingfang

Songbo

Xulei

Lihua

Weiming

Ying

Gaojing

Qianglong

Yujuan

Dan

(2021). Correlation between serum TSH levels within normal range and serum lipid profile. Hormone and Metabolic Research, 53(01), 32–40.

19.

Menvielle

Franck

J.-e.

Radoï

Sanchez

Févotte

Guizard

A. V.

Stücker

Luce

(2016). Quantifying the mediating effects of smoking and occupational exposures in the relation between education and lung cancer: The ICARE study. European Journal of Epidemiology, 31, 1213–1221.

20.

Menvielle

Franck

J.-e.

Stücker

Luce

(2016). P330 Quantifying mediating effects of smoking and occupational exposures in the relation between education and lung cancer. Occupational and Environmental Medicine, 73(Suppl. 1), A232.

21.

Mills

Wilkinson

P. A.

(1966). Plasma lipid levels and the diagnosis of coronary arteriosclerosis in England. British Heart Journal, 28(5), 638–645.

22.

Moghadam

T. Z.

Zandian

Fazlzadeh

Kalan

M. E.

Pourfarzi

(2023). Socioeconomic and environmental factors associated with waterpipe tobacco smoking among Iranian adults: A PERSIAN cohort-based cross-sectional study. BMC Public Health, 23(1), 1295.

23.

Ostovar

Fereidooni

Ansari

Haerinejad

Darabi

Raeisi

Heidari

Larijani

Mehrdad

Shafiee

Sharifi

(2017). The prevalence of hyperlipidemia among older people, Bushehr Elderly Health (BEH) program. Iranian South Medical Journal, 20(4), 399–415.

24.

Parikh

H. N.

Shah

Patel

Malhotra

(2022). Effect of serum lipid profile and its association with cardiovascular diseases in patients of Type 2 diabetes mellitus: Rethinking targets? National Journal of Physiology, Pharmacy and Pharmacology, 13(7), 1368–1368.

25.

Poustchi

Eghtesad

Kamangar

Etemadi

Keshtkar

A. A.

Hekmatdoost

Mohammadi

Mahmoudi

Shayanrad

Roozafzai

Sheikh

(2018). Prospective epidemiological research studies in Iran (the PERSIAN Cohort Study): Rationale, objectives, and design. American Journal of Epidemiology, 187(4), 647–655.

26.

Stępień

Wlazeł

R. N.

Paradowski

Banach

Rysz

(2014). Assessment of the relationship between lipid parameters and obesity indices in non-diabetic obese patients: A preliminary report. Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, 20, 2683.

27.

Vatankhah

Naghdi

Ghiasvand

Armoon

Ahounabr

(2020). Current cigarette smoking among Iranian elders; what are the prevalence, inequality and socioeconomic determinants? An analysis on Iranian Rural and Urban Income-Expenditure Survey 2017. Journal of Addictive Diseases, 38(3), 257–262.

28.

Wakabayashi

(2008). Associations of alcohol drinking and cigarette smoking with serum lipid levels in healthy middle-aged men. Alcohol & Alcoholism, 43(3), 274–280.

29.

Warwick

Chapple

Thomson

(1987). The effect of smoking two cigarettes on resting metabolic rate with and without food. International Journal of Obesity, 11(3), 229–237.

30.

Yamamoto

Temba

Horibe

Mabuchi

Saito

Matsuzawa

Kita

Nakamura

(2003). Life style and cardiovascular risk factors in the Japanese population-from an epidemiological survey on serum lipid levels in Japan 1990 Part 1: Influence of life style and excess body weight on HDL-cholesterol and other lipid parameters in men. Journal of Atherosclerosis and Thrombosis, 10(3), 165–175.

31.

Shen

Wang

R.-L.

Zhang

S.-Z.

Dong

L.-L.

Gui

Yang

(2018). Smoking in middle school students in priority areas for HIV control in Liangshan. Sichuan da xue xue bao. Yi xue ban= Journal of Sichuan University. Medical Science Edition, 49(2), 299–303.

32.

Zahirian Moghadam

Zandian

Pourfarzi

Poustchi

. (2021). Environmental and economics-related factors of smoking among Iranian adults aged 35–70: A PERSIAN cohort-based cross-sectional study. Environmental Science and Pollution Research, 28(33), 45365–45374.

Association Between Smoking and Lipid Profile in Men Aged 35 to 70 Years: Dose–Response Analysis

Abstract

Keywords

Introduction

Method

Design

Data Collection

Covariates

Statistical Analysis

Results

Smoking Status According to Participants’ Demographic Variables

Lipid Profile According to Participants’ Demographic Variables

Analysis of Lipid Profiles Based on Smoking Status

Logistic Regression Analysis of Smoking and Lipid Profile

Discussion

Association Between Smoking and Lipid Profile

Association Between Smoking Intensity and Lipid Profile

Association Between Demographic and Smoking Status of Study Participants

Association Between Lipid Profile and Demographic Variables in Smokers

Strengths and Limitations

Conclusion

Footnotes

Acknowledgements

Authors’ Contributions

Declaration of Conflicting Interests

Funding

Ethics Approval and Consent to Participate

Consent for Publication

ORCID iDs

Availability of Data and Materials

References