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Cardiometabolic profile of 15057 elderly Spanish workers: association of sociodemographic variables and tobacco consumption

BMC Geriatrics volume 22, Article number: 872 (2022) Cite this article

Abstract

Background

Aging of the world population is one of the most significant demographic changes of our time. Populations older than 60 years are heterogeneous, and age is an independent cardiovascular risk factor aggravated by frailty, obesity, and diabetes, and influenced by several factors, including sex and socioeconomic status. The objective of this study was to calculate cardiovascular risk in workers of both sexes over 60 years of age and to assess whether there are difference s by sex, social class, smoking, and type of job.

Methods

A cross-sectional study was carried out in 15,057 elderly Spanish workers from different autonomous communities in Spain and with different labor occupations. Anthropometric, sociodemographic, clinical, and laboratory values were determined. People were classified according to age from 60 to 64 years inclusive and from 65 to 69 years, smokers and non-smokers, and both blue-collar and white-collar workers. Subsequently, a multivariate analysis was carried out.

Results

Men, blue-collar workers, smokers, and aging were factors that influenced cardiovascular risk: with an OR of 3.27 (95% CI: 2.64–4.05) in people 65 years of age or older versus the younger group, and an OR of 3.15 (95% CI: 2.69–3.69) in smokers versus non-smokers. A stronger independent association was found between smoking, age, and cardiovascular risk. The risk of developing non-alcoholic fatty liver and liver fibrosis was much higher in men than in women, with an OR of 4.06 (95% CI: 3.66–4.50) for the former and an OR of 2.10 (95% CI: 1.95–2.26) for the BARD index.

Conclusions

The highest risk groups were observed in male subjects with a history of smoking and blue-collar workers and, as such, should be considered for cardiovascular risk screening programs.

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Introduction

Aging of the world population is one of the most important demographic changes of our time. The world population is aging rapidly: since 1980, the number of people aged 60 and over has doubled to approximately 810 million and is expected to grow to approximately 2 billion by 2050. Thus, it has been forecast that 22% of the total population will be over the age of 60 and about 5% will be over the age of 80 in 2050 [1].

Age is an independent cardiovascular risk factor in adults that is aggravated by other factors such as frailty, obesity, and diabetes [2,3,4]. The American Heart Association (AHA) found that the incidence of cardiovascular disease (CVD) in US men and women is approximately 75% between the ages of 60 and 79, and 86% in those older than 80 years [5]. In the same way, in Spain, the Annual Report of the National Health System 2022 presents a prevalence of ischemic heart disease of 20.9 cases per 1000 inhabitants, more than two-fold in men than in women (29.2 compared to 13.0); while cerebrovascular disease has an incidence of 15.1 cases per 1000 inhabitants, with very close values in men and women (15.9 compared to 14.3). Prevalence of these diseases clearly increases with age, rising after 40 years, and reaching maximum values at 85–94 years [6]; thereby representing an important burden for the national health system.

Populations over 60 years of age are heterogeneous and influenced by several factors, among which we find sex and socioeconomic level [7]; in such a way that over 60 years of age, cardiovascular diseases are more frequent in women and increase with age [8]. Therefore, it is very important to study Cardiovascular Risk Factors (CVRF) separately between women and men [9].

Despite their increase in incidence with advancing age, the World Health Organization (WHO) considers that a large proportion of these diseases could be avoided, and cardiovascular mortality reduced by almost three quarters by adopting changes in lifestyle aimed at controlling CVRF [10].

The objective of this study was to calculate cardiovascular risk in workers of both sexes over 60 years of age and to assess whether there are differences by sex, social class, smoking, and type of job, in order to raise awareness among this population as to what CVRFs are more altered, which is the first step to controlling them. This would be effective in preventing morbidity and mortality in adulthood.

Materials and methods

Study design

A cross-sectional study was carried out in 15,057 elderly Spanish workers from different autonomous communities in Spain (the Balearic Islands, Andalusia, the Canary Islands, the Valencian Community, Catalonia, Madrid, Castilla La Mancha, Castile and León, and the Basque Country) and with different labor occupations, the most represented of which are hospitality, construction, commerce, health, public administration, transport, education, industry and cleaning, between January 2019 and June 2020. Workers were selected based on their attendance to periodic occupational medical examinations.

Inclusion criteria

  • Belonging to one of the participating companies.

  • Agreeing to participate in the study and consenting to the use of the data for epidemiological purposes.

  • Aged 60 years and over.

  • Having the parameters to calculate different cardiovascular risk scales.

The workers finally included in the study and reasons for exclusion are presented in the flow chart (See Fig. 1).

Fig. 1
figure 1

Study participant flow chart

Full size image

Anthropometric measurements of height and weight, as well as clinical and analytical data, were collected by health personnel from the occupational health units participating in the study, after standardization of the measurement techniques. According to the International standards for anthropometric assessment of the ISAK [11].

Glycemia, total cholesterol, and triglycerides: These were determined by automated enzymatic methods and HDL by precipitation with dextran sulfate Cl2Mg. LDL was estimated by the Friedewald formula (when triglycerides ≤400 mg/dl; LDL = total cholesterol -HDL- triglycerides/5). All results are expressed in mg/dl.

To measure weight (in kilograms) and height (in cm), a height bar scale (model: SECA 700) with an added SECA 220 telescopic height bar was used.

Abdominal waist circumference (WC) was measured in cm using a tape measure: SECA model 20, with an interval of 1–200 cm and millimetric division. For the evaluation, each person was placed in a standing position, feet together and trunk erect, abdomen relaxed and upper limbs hanging down at their sides. The measuring tape was placed parallel to the floor at the level of the last floating rib.

Blood pressure was measured with a well-calibrated OMRON M3 automatic sphygmomanometer after 10 minutes of rest. Three measurements were taken at one-minute intervals, obtaining the mean value of the three.

The following anthropometric indexes and formulas were applied:

  • Visceral adiposity index [12] (VAI)

    $${\displaystyle \begin{array}{c} VAI=\left(\frac{{\textrm{WC}}^{\boldsymbol{Male}:}}{39,68+\left(1,88\times \textrm{BMI}\right)}\right)\times \left(\frac{TG}{1,03}\right)\times \left(\frac{1,31}{HDL}\right)\\ {} VAI=\left(\frac{{\textrm{WC}}^{\boldsymbol{Female}:}}{36,58+\left(1,89\times \textrm{BMI}\right)}\right)\times \left(\frac{TG}{0,81}\right)\times \left(\frac{1,52}{HDL}\right)\end{array}}$$

  • Dysfunctional adiposity index [13]

[WC/[22.79 + [2.68*BMI]]]*[triglycerides (TG, mmol/L)/1.37]* [1.19/high density lipoprotein-cholesterol (HDL-C, mmol/L)] for males, and [WC/[24.02 + [2.37*BMI]]]* [TG (mmol/L)/1.32]*[1.43/HDL-C (mmol/L)] for females.

  • Body shape index (ABSI) [12]

    $$ABSI\equiv \frac{WC}{BMI^{2/3}\times {height}^{1/2}}$$

  • Normalized weight-adjusted index (NWAI) [12]

[(weight/10) – (10 x height) + 10] with weight measured in kg and height in m.

  • Conicity index [14]

    $$\frac{\textrm{waist}\ \textrm{circumference}\ \left(\textrm{in}\ \textrm{meters}\right)}{0.109}\times 1/\sqrt{\frac{\textrm{weight}\ \left(\textrm{in}\ \textrm{kilogram}\right)}{\textrm{height}\ \left(\textrm{in}\ \textrm{meters}\right)}}$$

  • Body Roundness Index [12] (BRI)

    $$\textrm{BRI}=364.2-365.5\times \sqrt{1-\left(\frac{{\left(\textrm{WC}/\left(2\pi \right)\right)}^2}{{\left(0.5\ \textrm{height}\right)}^2}\right)}$$

  • Body mass index (BMI) was calculated by dividing weight by height in squared meters [15].

  • Waist-to-height ratio was considered risky over 0.50 [16].

  • Body surface index [11] (BSA) is calculated from the body surface area (BSA) where w represents weight in kg and h height in cm.

    $$\textrm{BSA}={\textrm{w}}^{0.425}\ast {\textrm{h}}^{0.725}\ast 0.007184\kern0.5em \textrm{BSI}=\frac{\textrm{WEIGHT}}{\sqrt{\textrm{BSA}}}$$

  • Relative fat mass [16] Women: 76- (20 x (height/waist))

    Men: 64- (20 x (height/waist)) Height and waist circumference are expressed in meters.

  • CUN BAE [15] (Clinic University of Navarra Body Adiposity Estimator):

    $$-44.988+\left(0.503\ \textrm{x}\ \textrm{age}\right)+\left(10.689\ \textrm{x}\ \textrm{sex}\right)+\left(3.172\ \textrm{x}\ \textrm{BMI}\right)-\left(0.026\ \textrm{x}\ {\textrm{BMI}}^2\right)+\left(0.181\ \textrm{x}\ \textrm{BMI}\ \textrm{x}\ \textrm{sex}\right)-\left(0.02\ \textrm{x}\ \textrm{BMI}\ \textrm{x}\ \textrm{age}\right)-\left(0.005\ \textrm{x}\ {\textrm{BMI}}^2\ \textrm{x}\ \textrm{sex}\right)+\left(0.00021\ \textrm{x}\ {\textrm{BMI}}^2\ \textrm{x}\ \textrm{age}\right)$$

  • ECORE-BF (Equation Cordoba Estimator Body Fat) [15]

− 97.102 + 0.123 (age) + 11.9 (sex) + 35.959 (LnBMI).

In CUN BAE and ECORE-BF, male is 0 and female 1, and cut-off points for obesity are 35% in women 25% in men.

  • Palafolls formula [15]

    $$\textrm{Men}=\left(\textrm{BMI}/\textrm{waist}\right]\ast 10\Big)+\textrm{BMI}.\textrm{Women}=\left(\textrm{BMI}/\textrm{waist}\Big]\ast 10\right)+\textrm{BMI}+10$$

  • Deuremberg formula [15]

    $${\displaystyle \begin{array}{cc}1.2\ \textrm{x}\ \left(\textrm{BMI}\right)+0.23\ \textrm{x}\ \left(\textrm{age}\right)-10.8\ \textrm{x}\ \left(\textrm{sex}\right)-5.4& \textrm{Male}=0\kern0.5em \textrm{Female}=1\end{array}}$$

Three atherogenic indexes were calculated [17]:

  • Cholesterol/HDL (Castelli index) (considered high when > 5 in men and > 4.5 in women)

  • LDL-c/HDL-c (Kannel index) (high values > 3)

  • Triglycerides/HDL (high values > 3)

Metabolic syndrome (MetS) was determined using three models:

  1. a)

    NCEP ATP III (National Cholesterol Educational Program Adult Treatment Panel III) [18], which establishes metabolic syndrome when three or more of the following factors are present: waist circumference is greater than 88 cm in women and 102 in men; triglycerides > 150 mg/dl or specific treatment is followed; blood pressure > 130/85 mmHg; HDL < 40 mg/dl in women or < 50 mg/dl in men or specific treatment is followed; and fasting blood glucose > 100 mg/dl or specific glycemic treatment is followed.

  2. b)

    The International Diabetes Federation (IDF) model [19], which considers the presence of central obesity necessary, defined as a waist circumference of > 80 cm in women and > 94 cm in men, in addition to two of the other abovementioned factors for ATP III (triglycerides, HDL, blood pressure, and glycemia).

  3. c)

    The JIS model [20], which follows the same criteria as NCEP ATPIII, but the waist circumference cut-off values are 80 cm in women and 94 cm in men.

In order to determine vascular age, calibrated tables were used. The REGICOR scale, which is an adaptation of the Framingham scale to the characteristics of the Spanish population [21], estimates the risk of suffering a cardiovascular event over a period of 10 years. This scale can be applied to people between 35 and 74 years of age, where the risk is moderate > 5% or high > 10% [22]. The Framingham model23 assesses age, sex, HDLc, total cholesterol, systolic blood pressure, hypertension treatment, tobacco use, and diabetes, and can be used in people over 30 years of age. The SCORE scale recommended for Spain estimates the risk of suffering a fatal cerebrovascular event in a period of 10 years [23, 24]. It is applied to people between 40 and 65 years old, and risk is considered moderate at > 4% and high at > 5% 28. The SCORE model [25] considers age, sex, systolic blood pressure, tobacco use, and total cholesterol.

An interesting concept that can be applied to vascular ages using both these models is ALLY (avoidable lost life years), which can be defined as the difference between biological age (BA) and vascular age (VA) [26].

$$\textrm{ALLY}=\textrm{VA}-\textrm{BA}$$

Other indicators related to cardiovascular risk were determined:

  • Hypertriglyceridemic waist [27]: waist circumference greater than 94 cm in men and greater than 80 cm in women, and triglycerides greater than 150 mg/dl, or under treatment.

  • Waist triglyceride index [28]: Waist circumference (cm) x - triglycerides (mmol).

  • Cardiometabolic index [29]

Waist-to-height ratio x atherogenic index triglycerides /HDL-c.

  • Triglyceride glucose index [30] = LN (triglycerides [mg/dl] × glycaemia [mg/dl]/2).

  • Triglyceride glucose index-BMI, Triglyceride glucose index-waist [31]

    • TyGindex-BMI = TyGindex x BMI

    • TyGindex-waist = TyGindex x waist

Non-alcoholic fatty liver scales:

  • Fatty liver index (FLI) [32]

    $$\textrm{FLI}=\left({\textrm{e}}^{0.953}\ast {\log}_{\textrm{e}}\ \left(\textrm{triglycerides}\right)+0.139\ast \textrm{BMI}+0.718\ast {\log}_{\textrm{e}}\ \left(\textrm{ggt}\right)+0.053\ast \textrm{waist}\ \textrm{circumference}-15.745\right)/\left(1+{\textrm{e}}^{0.953}\ast {\log}_{\textrm{e}}\ \left(\textrm{triglycerides}\right)+0.139\ast \textrm{BMI}+0.718\ast {\log}_{\textrm{e}}\ \left(\textrm{ggt}\right)+0.053\ast \textrm{waist}\ \textrm{circumference}-15.745\right)\ \textrm{x}\ 100$$

Cut-off for high risk, 60.

  • Hepatic steatosis index (HSI) [33]

    $$\textrm{HSI}=8\ \textrm{x}\ \textrm{ALT}/\textrm{AST}+\textrm{BMI}\ \left(+2\ \textrm{if}\ \textrm{type}\ 2\ \textrm{diabetes}\ \textrm{yes},+2\ \textrm{if}\ \textrm{female}\right)$$

  • Zhejian University index (ZJU) [34]

    $$\textrm{BMI}+\textrm{FPG}\ \textrm{mmol}\ \textrm{L}+\textrm{TG}\ \textrm{mmol}\ \textrm{L}+3\ \textrm{ALT}/\textrm{AST}\kern0.5em +2\ \textrm{if}\ \textrm{female}$$

  • Fatty liver disease index (FLD) [35]

    $$\textrm{BMI}+\textrm{TG}+3\times \left(\textrm{ALT}/\textrm{AST}\right)+2\times \textrm{Hyperglycemia}\ \left(\textrm{presence}=1;\textrm{absence}=0\right)$$

Values < 28.0 or > 37.0 exclude the possibility of NAFLD

  • Lipid accumulation product [36]

    $${\displaystyle \begin{array}{c}\textrm{In}\ \textrm{men}:\left(\textrm{waist}\ \textrm{circumference}\ \left(\textrm{cm}\right)-65\right)\ \textrm{x}\ \left(\textrm{triglyceride}\ \textrm{concentration}\ \left(\textrm{mMol}\right)\right)\\ {}\textrm{In}\ \textrm{women}:\left(\textrm{waist}\ \textrm{circumference}\ \left(\textrm{cm}\right)-58\right)\ \textrm{x}\ \left(\textrm{triglyceride}\ \textrm{concentration}\ \left(\textrm{mMol}\right)\right)\end{array}}$$

Atherogenic dyslipidemia is defined by triglycerides > 150 mg/dL, HDL < 40 mg/dL in men and < 50 mg/dL in women, and normal LDL. If LDL is > 130 mg/dL we speak of lipid triad [37].

Fibrosis hepatic scale

  • BARD scoring system [38]

The BARD score was calculated by assigning 0 to 2 points to the following parameters: BMI ≥ 28 kg/m2 = 1 point, BMI < 28 kg/m2 = 0 points; AST/ALT ratio ≥ 0.8 = 2 points, AST/ALT ratio < 0.8 = 0 points; diabetes mellitus = 1 point. A total of 2–4 points indicates significant fibrosis.

A smoker was considered to be any person who had regularly consumed at least 1 cigarette/day (or the equivalent in other types of consumption) in the last month, or had quit smoking less than 1 year before.

Social class was determined from the 2011 National Classification of Occupations (CNO-11), based on the proposal of the group of social determinants of the Spanish Society of Epidemiology [39]. It is classified in 3 categories: Class I: Directors/managers, university professionals, athletes, and artists; Class II: Intermediate occupations and self-employed workers without employees; Class III: Unskilled workers.

Statistical analysis

A descriptive analysis of the categorical variables was carried out, by calculating the frequency and distribution of responses for each one. For quantitative variables, the mean and standard deviation were calculated, whereas for qualitative variables the percentage was calculated. A bivariate association analysis was performed using the χ2 test (with a correction using the Fisher’s exact statistical test, when conditions required so) and Student’s t-test for independent samples. For the multivariate analysis, binary logistic regression was conducted using the Wald method, with an Odds-ratio calculation and a Hosmer-Lemeshow goodness-of-fit test. Statistical analysis was performed with the SPSS 27.0 program, and a p value of < 0.05 was considered statistically significant.

Ethical considerations and aspects

The study was approved by the Clinical Research Ethics Committee of the Balearic Islands Health Area (n° IB4383/20). All procedures were performed in accordance with the ethical standards of the institutional research committee and with the 2013 Declaration of Helsinki. All patients signed written informed consent documents prior to participation in the study.

Results

All the anthropometric, sociodemographic, clinical, and analytical values evaluated were more unfavorable in men, except for total cholesterol and LDL-c, and the differences observed between both sexes were statistically significant. Of the people making up the sample, 93% were between 60 and 64 years old, with only the remaining 7% aged between 65 and 69 years. This is logical, since in the case of workers, the retirement age in Spain is 65 years (majority of the sample) although a small proportion of workers can extend their working life to 70 years (forced retirement). About 80% of men and women belonged to social class III, and almost a third of them were smokers, with no significant differences by sex. All data are shown in Table 1.

Table 1 Anthropometric, sociodemographic, clinical and analytical values of elderly people
Full size table

In the mean values of the different scales differentiated by sex, is worth noting that in the overweight and obesity scales, men obtained worse results than women, except for the formulas in which sex is taken into account. In these cases, the results were significantly more unfavorable for women (CUN-BAE, ECORE-BF, Palafolls, Relative Fat Mass, Deuremberg). The atherogenic indexes analyzed in this study reveal higher mean values in men, with the differences observed between sexes for all scales showing statistical significance. In the non-alcoholic fatty liver risk scales, the results were also more unfavorable for women in the formulas that included sex. In the BARD score system, where sex is not taken into account, the worst results were for men. When applying the ALLY through both the REGICOR scale and the SCORE scale, the potential years of life lost were much higher in men than in women. These results can be seen in Table 2.

Table 2 Mean values of different scales related to cardiovascular risk in elderly people by sex
Full size table

Prevalence of altered values in the overweight and obesity scales presents a higher percentage for men in all the formulas used, and in all of them significantly. Only by applying the CUN-BAE formula did we find a slightly higher result in women, but without statistical significance, therefore it should be rejected.

The percentage of the population with hypertension was also significantly higher in men than in women. As far as laboratory values are concerned, we found that Triglyceride and Glucose figures were more frequent in men, while women had much higher total Cholesterol and LDL-c figures much more frequently.

When evaluating MetS using the three formulas, the percentage of men who present MetS by both the NCEP ATPIII and JIS criteria was much higher in men, with significant statistical significance. However, when evaluating it according to the IDF criteria, this percentage was higher in women, despite it lacking a significant value.

Atherogenic indexes also showed higher percentages in men, all with statistical significance.

As for the risk scales for non-alcoholic fatty liver disease, the results show a higher risk of non-alcoholic fatty liver disease in men regardless of whether the sex factor is included in the formula. In most cases, as can be seen in Table 3, the differences observed show statistical significance.

Table 3 Prevalence of altered values of different scales related to cardiovascular risk in elderly people by sex
Full size table

In the multivariate analysis, when making the comparison between men and women, all the parameters measured were more unfavorable in men except for Relative fat mass obesity, Total Cholesterol, and LDL-c.

In the analysis of social class, social class III was compared to social class II plus social class I. In this case, only four variables showed significant differences: diabetes, metabolic syndrome according to the ATP-III criteria, and metabolic syndrome according to the JIS criteria were more frequent in social classes I and II; while high fatty liver disease index occurred more frequently in social class III compared to the other two.

Blue-collar workers performed worse than white-collar workers in all variables that increase cardiovascular risk with statistical significance (obesity, diabetes, arterial hypertension, metabolic syndrome, and AI LDL-c/HDL-c high). They also presented a higher cardiovascular risk with both the REGICOR and SCORE scales, as well as a worse result in the Bard scoring system high. On the other hand, white-collar workers only presented worse results in the Lipid accumulation product high.

Smoking only showed significant results in the calculation formulas for obesity, total cholesterol, LDL-c and cardiovascular risk. Except for cardiovascular risk, in the rest of the cases it presented an OR less than 0, which must be interpreted as a protective factor. The complete data can be consulted in Table 4.

Table 4 Multivariate analysis using binary logistic regression
Full size table

Discussion

We studied a sample of 15,057 workers of both sexes between 60 and 69 years of age. Of these, 5555 were women and 9502 men, corresponding to different social classes, and with 80% blue-collar and 20% white-collar workers.

When comparing the calculation of obesity according to BMI with the rest of the formulas used, we observe that the percentage of obese in both sexes was more than double that of any of the other formulas. Various studies have already warned of these differences, obtaining results of two to six times higher by methods other than BMI, which implies that BMI has a good specificity for identifying excess weight, but low sensitivity [40, 41].

When studying the mean values of the different scales with which we measured overweight and obesity, we found more unfavorable results for men except in the formulas that include sex in their calculation. In these cases, the results were significantly more unfavorable for women (CUN-BAE, ECORE-BF, Palafolls, Relative Fat Mass, Deuremberg), which coincides with the results obtained in other studies where the sex factor seems to bear an influence [42, 43].

The prevalence of MetS defined by the three criteria used (NCEP ATPIII, IDF, JIS) was high in both men and women; with a significantly higher percentage in males, except for the IDF, which did not present significant differences between sexes. These data differ from those obtained by other authors, in which MetS is more common in women over 60 years of age [44,45,46]. Continuing the comparison with the study by Shasha et al., our results do not coincide either with regard to triglyceride or glycemia values, which we found to be significantly higher in men, while they obtained the highest triglycerides in women. Neither did they find any differences between sexes in blood glucose levels. However, there was a coincidence in figures for higher total cholesterol and LDL-c in women than in men, with a significantly high value in both studies.

It is possible that these results are due to differences in the sample, since in our study the oldest patients were 69 years old, whereas in the aforementioned study the sample evaluated the population up to 80 years of age. The authors refer in their article to the fact that older women were significantly more likely to have MetS, abdominal obesity, hypertriglyceridemia, and lower HDL-c levels, which would favor this hypothesis.

Non-alcoholic fatty liver disease (NAFLD) is a chronic, progressive disease that can be asymptomatic and increases cardiovascular risk factors [47]. Much of the published literature finds a higher prevalence of NAFLD in men [48, 49]. However, we know that as age increases, so does the prevalence of NAFLD, and it tends to be more common in women [50, 51]. For this reason, we considered it interesting to assess whether a similar alteration occurred in our population. In our study, the NAFLD indexes give us a significantly higher percentage in men in all cases except for the Zhejiang University index. Nonetheless, even with a higher percentage in men than in women, it does not present statistical significance. Thus, we do not observe this reversal by sex in NAFLD above 60 years of age.

When we calculated the potential years of life lost (ALLY) using both the REGICOR scale [21] and the SCORE scale [52], these were much higher in men than in women. This result is reasonable, since sex is involved in both formulas. The main variable constitutes a value that increases cardiovascular risk. Having obtained more unfavorable values for men in the other variables that are part of the formula, and as the average age and percentage of smokers is the same in both sexes, the potential years of life lost must necessarily offer a higher result in men, and present much higher cardiovascular risk (CVR) with statistical significance.

In the multivariate analysis using binary logistic regression, being a white-collar/blue-collar worker, being male, tobacco use, and social class III were established as covariates.

When comparing men versus women, the multivariate analysis found an OR greater than 1 in all the formulas studied except for Relative fat mass obesity, Total Cholesterol, and LDL-c, with an OR less than 1. What this tells us is that all the factors studied occur more frequently in males except for the three factors mentioned with an OR less than 1. Levels of elevated cholesterol are known to be lower in younger women compared to men, while they increase after menopause [53], which is in agreement with our results when treating our entire sample of women aged 60 years or over. Atherogenic indexes also showed higher percentages in men, all with statistical significance. This alerts us to a higher risk of suffering a cardiovascular event.

Although at present we must consider whether high cholesterol figures in the population over 60 years decrease mortality from all causes and increase survival, Liang et al. found an association between higher cholesterol levels and a decreased risk of all-cause and non-cardiovascular mortality in the elderly [54]. Hence, more studies are needed in order to clarify the relationship between high cholesterol levels and longer survival in the elderly.

When analyzing social classes, we found a higher risk of obesity, diabetes, hypertension, MetS, atherogenic index, CVR, and BARD scoring system in blue-collar compared to white-collar workers. These results are consistent with other published studies that found that shift work is associated with an increased risk of CVD [55]. Socioeconomic and demographic differences are associated with an uneven distribution of health [56], in such a way that socioeconomic level presents an inverse association with the prevalence of MetS and diabetes mellitus, with a worse health status in the population with less purchasing power [57].

Blue-collar workers with low wages and limited education are more likely to develop unhealthy lifestyles and have poorer living conditions than white-collar workers. A study by Prihartono et al. in 2018 detected differences in CVRF prevalence between white-collar and blue-collar workers by obtaining diagnostic information from the health care provider, in such a way that, according to these reports, the prevalence of CVRF was higher in white-collar than in blue-collar workers. However, when CVRFs were assessed by symptoms, the prevalence of CVRFs was higher in blue-collar workers. This difference may be due to socioeconomic class, which would determine access to health services [58]. The latter is very important in order to detect CVRFs in blue-collar workers, reduce avoidable lost life years, and increase quality of life.

It is noteworthy that only in diabetes, metabolic syndrome, and Fatty liver disease index high do the results obtained coincide (with statistical significance) between the social classes with lower purchasing power and being a blue-collar worker, which could be the seed of future research.

Regarding the smoking habit, we found that in all the formulas to evaluate obesity, the OR was less than 1 with very narrow confidence intervals. This indicates that smoking is a protective factor against obesity, meaning that there is a strong relationship between smoking and not being obese. This may be due to lower food intake and increased metabolism produced by nicotine and consequently a lower weight in smokers, an effect already known and that in no case would justify recommending smoking to avoid these pathologies [59].

Likewise, when calculating CVR (REGICOR) in smokers versus non-smokers, we found an OR of 3.15. This indicates an almost 76% higher probability of suffering a cardiovascular event among smokers, thereby consolidating the need to give up smoking. This association between smoking and CVR is consistent with that published in other studies, with a strong association between smoking and CVR [60, 61].

Strengths and limitations

Some limitations of the study should also be considered.

Our study has a cross-sectional design, which does not allow causal relationships to be established, so no conclusions can be drawn about changes in anthropometric measurements over time. Second, the population of this study was ethnically homogeneous, as all the patients in this study were Spanish, which could limit the generalization of our findings. Third, since the participants are workers attending their annual medical check-up at the prevention service, a selection bias may occur, since only those who are more concerned about their health or who get sick less often attend.

Conclusions

Anthropometric, sociodemographic, clinical and analytical values are more unfavorable in men, except Total cholesterol and LDL-c. CVR and risk of developing NAFLD is also higher in men.

Blue-collar workers have worse health outcomes (obesity, diabetes, hypertension, MetS, Atherogenic index) and higher CVR than white-collar workers, which should make us alert in order to act to prevent these factors, reduce morbidity and mortality among these people, and increase their quality of life.

Smokers over 60 years of age have a much higher CVR than non-smokers. Due to population aging, it would be interesting to have CVR calculation tables for people over 70 years of age, which could give rise to new investigations.

Availability of data and materials

Data available on request due to restrictions (privacy, ethical). Contact corresponding author.

References

  1. Naciones Unidas. En Asuntos DoEaS, ed. Envejecimiento de la Población y Desarrollo. Nueva York: Naciones Unidas; 2012.

  2. Veronese N, Koyanagi A, Smith L, Musacchio C, Cammalleri L, Barbagallo M, et al. Multidimensional frailty increases cardiovascular risk in older people: an 8-year longitudinal cohort study in the osteoarthritis initiative. Exp Gerontol. 2021;147:111265. https://doi.org/10.1016/j.exger.2021.111265 Epub 2021 Feb 1.

    Article  PubMed  Google Scholar 

  3. Gorostidi M, Sánchez-Martínez M, Ruilope LM, Graciani A, de la Cruz JJ, et al. Chronic kidney disease in Spain: prevalence and impact of accumulation of cardiovascular risk factors. Nefrologia (Engl Ed). 2018;38(6):606–15. https://doi.org/10.1016/j.nefro.2018.04.004 Epub 2018 Jun 18.

    Article  Google Scholar 

  4. Kulmala J, Nykanen I, Hartikainen S. Frailty as a predictor of all-cause mortality in older men and women. Geriatr Gerontol Int. 2014;14:899–905. https://doi.org/10.1111/ggi.12190.

    Article  PubMed  Google Scholar 

  5. Yazdanyar A, Newman AB. The burden of cardiovascular disease in the elderly: morbidity, mortality, and costs. Clin Geriatr Med. 2009;25:563–77. https://doi.org/10.1016/j.cger.2009.07.007.

    Article  PubMed  PubMed Central  Google Scholar 

  6. https://www.sanidad.gob.es/estadEstudios/estadisticas/sisInfSanSNS/tablasEstadisticas/InfAnualSNS2020_21/INFORME_ANUAL_2020_21.pdf. Access 2 September 2022.

  7. Marí-Dell'olmo M, Gotsens M, Borrell C, Martinez-Beneito MA, Palència L, et al. Trends in socioeconomic inequalities in ischemic heart disease mortality in small areas of nine Spanish cities from 1996 to 2007 using smoothed ANOVA. J Urban Health. 2014;91(1):46–61. https://doi.org/10.1007/s11524-013-9799-6.

    Article  PubMed  Google Scholar 

  8. Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, et al. Heart disease and stroke Statistics-2019 update: a report from the American Heart Association. Circulation. 2019. https://doi.org/10.1161/CIR.0000000000000659.

  9. Schreuder MM, Boersma E, Kavousi M, Visser LE, Roos-Hesselink JW, et al. Reporting of sex-specific outcomes in trials of interventions for cardiovascular disease: has there been progress? Maturitas. 2021;144:1–3. https://doi.org/10.1016/j.maturitas.2020.09.007 Epub 2020 Sep 28.

    Article  CAS  PubMed  Google Scholar 

  10. World Health Organization. 65th world health assembly closes with new global health measures: World Health Organization. https://apps.who.int/mediacentre/news/releases/2012/wha65_closes_20120526/es/index.html.Access 5 March 2022

  11. Stewart A, Marfell-Jones M, Olds T, Ridder H. International standards for anthropometric assessment. Lower Hutt, New Zealand: International Society for the Advancement of Kinanthropometry - ISAK0; 2011.

    Google Scholar 

  12. Sedeño Argilagos C, González San Miguel HM, Martínez-Sánchez G, Pérez-Davison G, Rigo Vives MM. Determination of different scales of cardiovascular risk in 1.979 spanish informatics workers. Med Balear (Academic Journal of Health Sciences). 2022;37(2):33–41. https://doi.org/10.3306/AJHS.2022.37.02.33.

    Article  Google Scholar 

  13. Reyes-Barrera J, Sainz-Escárrega VH, Medina-Urritia AX, Jorge-Galarza E, Osorio-Alonso H, Torres-Tamayo M, et al. Dysfunctional adiposity index as a useful clinical tool for early identification of adipose tissue Morpho-functional abnormalities and Cardiometabolic disorders in apparently healthy subjects. Res Square. 2020; Available at: https://www.researchsquare.com/article/rs-97962/v1.

  14. Andrade MD, Freitas MC, Sakumoto AM, Pappiani C, Andrade SC, Vieira VL, et al. Association of the conicity index with diabetes and hypertension in Brazilian women. Arch Endocrinol Metab. 2016;60(5):436–42.

    Article  PubMed  Google Scholar 

  15. López-González AA, Ramírez Manent JI, Vicente-Herrero MT, García Ruiz E, Albaladejo Blanco M, López SN. Prevalence of diabesity in the Spanish working population: influence of sociodemographic variables and tobacco consumption. An Sist Sanit Navar. 2021;0(0). https://doi.org/10.23938/ASSN.0977.

  16. Rangel-Baltazar E, Cuevas-Nasu L, Shamah-Levy T, Rodríguez-Ramírez S, Méndez-Gómez-Humarán I, Rivera JA. Association between high waist-to-height ratio and cardiovascular risk among adults sampled by the 2016 half-way National Health and nutrition survey in Mexico (ENSANUT MC 2016). Nutrients. 2019;11(6):1402. https://doi.org/10.3390/nu11061402.

    Article  CAS  PubMed Central  Google Scholar 

  17. López González ÁA, Rivero Ledo YI, Vicente Herrero MT, Gil Llinás M, Tomás Salvá M, Riutord FB. Índices aterogénicos en trabajadores de diferentes sectores laborales del área mediterránea española. Clin Investig Arterioscler. 2015;27(3):118–28.

    PubMed  Google Scholar 

  18. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486-97. https://doi.org/10.1001/jama.285.19.2486.

  19. Zimmet P, MM Alberti KG, Serrano Ríos M. A new international diabetes federation worldwide definition of the metabolic syndrome: the rationale and the results. Rev Esp Cardiol. 2005;58(12):1371–6.

    Article  PubMed  Google Scholar 

  20. Cabrera-Roe E, Stusser B, Cálix W, Orlandi N, Rodríguez J, Cubas-Dueñas I, et al. Concordancia diagnóstica entre siete definiciones de síndrome metabólico en adultos con sobrepeso y obesidad. Rev Peru Med Exp Salud Publica. 2017;34(1):19–27.

    Article  Google Scholar 

  21. Marrugat J, Subirana I, Comín E, Cabezas C, Vila J, Elosua R, et al. Validity of an adaptation of the Framingham cardiovascular risk function: the VERIFICA study. J Epidemiol Community Health. 2007;61:40–7.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Marrugat J, D’Agostino R, Sullivan L, Elosua R, Wilson P, Ordovas J, et al. An adaptation of the Framingham coronary risk function to southern Europe Mediterranean areas. J Epidemiol Community Health. 2003;57(8):634–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Sans S, Fitzgerald AP, Royo D, Conroy R, Graham I. Calibrating the SCORE cardiovascular risk chart for use in Spain. Rev Esp Cardiol. 2007;60(5):476–85.

    Article  PubMed  Google Scholar 

  24. Buitrago F, Cañón Barroso L, Díaz Herrera N, Cruces E. Analysis of predictive value of Framingham-REGICOR and SCORE functions in primary health care. Med Clin (Barc). 2007;129(20):797.

    Article  Google Scholar 

  25. Cuende JL. La edad vascular frente al riesgo cardiovascular: aclarando conceptos. Rev Esp Cardiol. 2016;69(3):243–6.

    Article  PubMed  Google Scholar 

  26. Cuende JI. Edad vascular, RR, ALLY, RALLY y velocidad de envejecimiento, basados en el SCORE: relaciones entre nuevos conceptos de prevención cardiovascular. Rev Esp Cardiol. 2018;71:399–400.

    Article  PubMed  Google Scholar 

  27. Sam S, Haffner S, Davidson MH, D'Agostino RB, Feinstein S, Kondos G, et al. Hypertriglyceridemic waist phenotype predicts increased visceral fat in subjects with type 2 diabetes. Diabetes Care. 2009;32(10):1916–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Yang RF, Liu XY, Lin Z, Zhang G. Correlation study on waist circumference-triglyceride (WT) index and coronary artery scores in patients with coronary heart disease. Eur Rev Med Pharmacol Sci. 2015;19(1):113–8.

    CAS  PubMed  Google Scholar 

  29. Wakabayashi I, Daimon T. The "cardiometabolic index" as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. ClinChim Acta. 2015;438:274–8.

    Article  CAS  Google Scholar 

  30. Unger G, Benozzi SF, Peruzza F, Pennacchiotti GL. Triglycerides and glucose index: a useful indicator of insulin resistance. Endocrinol Nutr. 2014;61(10):533–40.

    Article  PubMed  Google Scholar 

  31. Zheng S, Shi S, Ren X, Han T, Li Y, Chen Y, et al. Triglyceride glucose-waist circumference, a novel and effective predictor of diabetes in first-degree relatives of type 2 diabetes patients: cross-sectional and prospective cohort study. J Transl Med. 2016;14(1):260.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Bedogni G, Bellentani S, Miglioli L, Masutti F, Passalacqua M, Castiglione A, et al. The fatty liver index: a simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006;6:33.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Lee JH, Kim D, Kim HJ, Lee CH, Yang JI, Kim W, et al. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease. Dig Liver Dis. 2010;42(7):503–8.

    Article  CAS  PubMed  Google Scholar 

  34. Wang J, Xu C, Xun Y, Lu Z, Shi J, Yu C, et al. ZJU index: a novel model for predicting nonalcoholic fatty liver disease in a Chinese population. Sci Rep. 2015;5:16494.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Fuyan S, Jing L, Wenjun C, Zhijun T, Weijing M, Suzhen W, et al. Fatty liver disease index: a simple screening tool to facilitate diagnosis of nonalcoholic fatty liver disease in the Chinese population. Dig Dis Sci. 2013;58(11):3326–34.

    Article  PubMed  Google Scholar 

  36. Chiang JK, Koo M. Lipid accumulation product: a simple and accurate index for predicting metabolic syndrome in Taiwanese people aged 50 and over. BMC Cardiovasc Disord. 2012;12:78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bestehorn K, Smolka W, Pittrow D, Schulte H, Assmann G. Atherogenic dyslipidemia as evidenced by the lipid triad: prevalence and associated risk in statin-treated patients in ambulatory care. Curr Med Res Opin. 2010;26(12):2833–9.

    Article  PubMed  Google Scholar 

  38. Sun W, Cui H, Li N, Wei Y, Lai S, Yang Y, et al. Comparison of FIB-4 index, NAFLD fibrosis score and BARD score for prediction of advanced fibrosis in adult patients with non-alcoholic fatty liver disease: a meta-analysis study. Hepatol Res. 2016;46(9):862–70. https://doi.org/10.1111/hepr.12647 Epub 2016 Feb 16.

    Article  CAS  PubMed  Google Scholar 

  39. Domingo-Salvany A, Bacigalupe A, Carrasco JM, Espelt A, Ferrando J, Borrell C. Propuesta de clase social neoweberiana y neomarxista a partir de la Clasificación Nacional de Ocupaciones 2011. Gac Sanit. 2013;27(3):263–72.

    Article  PubMed  Google Scholar 

  40. Yoon JL, Cho JJ, Park KM, Noh HM, Park YS. Diagnostic performance of body mass index using the Western Pacific regional Office of World Health Organization reference standards for body fat percentage. J Korean Med Sci. 2015;30(2):162–6. https://doi.org/10.3346/jkms.2015.30.2.162 Epub 2015 Jan 21.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Okorodudu DO, Jumean MF, Montori VM, Romero-Corral A, Somers VK, Erwin PJ, et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. Int J Obes. 2010;34(5):791–9. https://doi.org/10.1038/ijo.2010.5 Epub 2010 Feb 2.

    Article  CAS  Google Scholar 

  42. Davila-Batista V, Molina AJ, Fernández-Villa T, Romaguera D, Pérez-Gómez B, et al. The relation of CUN-BAE index with body mass index and waist circumference in adults aged 50 to 85 years: the MCC-Spain study. Nutrients. 2020;12(4):996. https://doi.org/10.3390/nu12040996.

    Article  PubMed Central  Google Scholar 

  43. Molina-Luque R, Yañez AM, Bennasar-Veny M, Romero-Saldaña M, Molina-Recio G, López-González ÁA. A comparison of equation Córdoba for estimation of body fat (ECORE-BF) with other prediction equations. Int J Environ Res Public Health. 2020;17(21):7940. https://doi.org/10.3390/ijerph17217940.

    Article  PubMed Central  Google Scholar 

  44. Shasha Y, Guo X, Li GX, Yang H, Zheng L, Sun Y. Gender discrepancy in the predictive effect of metabolic syndrome and its components on newly onset cardiovascular disease in elderly from rural China. BMC Geriatr. 2021;21:505. https://doi.org/10.1186/s12877-021-02393-6.

    Article  Google Scholar 

  45. Sergi G, Dianin M, Bertocco A, Zanforlini BM, Curreri C, Mazzochin M, et al. Gender differences in the impact of metabolic syndrome components on mortality in older people: a systematic review and meta-analysis. Nutr Metab Cardiovasc Dis. 2020;30(9):1452–64. https://doi.org/10.1016/j.numecd.2020.04.034 Epub 2020.

    Article  CAS  PubMed  Google Scholar 

  46. He X, Zhang W, Pang G, et al. Prevalence and clinical profile of metabolic syndrome in longevity: study from Guangxi Zhuang autonomous region. China BMC Geriatr. 2017;17:169. https://doi.org/10.1186/s12877-017-0536-y.

    Article  PubMed  Google Scholar 

  47. Powell EE, Wong VW, Rinella M. Non-alcoholic fatty liver disease. Lancet. 2021;397(10290):2212–24. https://doi.org/10.1016/S0140-6736(20)32511-3 Epub 2021 Apr 21. PMID: 33894145.

    Article  CAS  PubMed  Google Scholar 

  48. Makri E, Goulas A, Polyzos SA. Epidemiology, pathogenesis, diagnosis and emerging treatment of nonalcoholic fatty liver disease. Arch Med Res. 2021;52(1):25–37. https://doi.org/10.1016/j.arcmed.2020.11.010 Epub 2020 Dec 14. PMID: 33334622.

    Article  CAS  PubMed  Google Scholar 

  49. Roberts SK, Majeed A, Glenister K, Magliano D, Lubel JS, Bourke L, et al. Prevalence of non-alcoholic fatty liver disease in regional Victoria: a prospective population-based study. Med J Aust. 2021;215(2):77–82. https://doi.org/10.5694/mja2.51096 Epub 2021 May 24.

    Article  PubMed  Google Scholar 

  50. European Association for the Study of the Liver (EASL), European Association for the Study of Diabetes (EASD), European Association for the Study of Obesity (EASO). EASL-EASD-EASO clinical practice guidelines for the management of non-alcoholic fatty liver disease. J Hepatol. 2016;64:1388–402 Epub 2016 Apr 7. PubMed PMID: 27062661.

    Article  Google Scholar 

  51. Younossi Z, Tacke F, Arrese M, Sharma BC, Mostafa I, Bugianesi E, et al. Global perspectives on non-alcoholic fatty liver disease and non-alcoholic steatohepatitis. Hepatology. 2019;69(6):2672–82. https://doi.org/10.1002/hep.30251.

    Article  PubMed  Google Scholar 

  52. Ramírez M. La edad vascular como herramienta de comunicación del riesgo cardiovascular. Centro Integral para la Prevención de Enfermedades Crónicas. 2010. Disponible en: http://pp.centramerica.com/pp/bancofotos/267-2570.pdf.

  53. Appelman Y, van Rijn BB, Ten Haaf ME, Boersma E, Peters SA. Sex differences in cardiovascular risk factors and disease prevention. Atherosclerosis. 2015;241(1):211–8. https://doi.org/10.1016/j.atherosclerosis.2015.01.027 Epub 2015 Jan 28. PMID: 25670232.

    Article  CAS  PubMed  Google Scholar 

  54. Liang Y, Vetrano DL, Qiu C. Serum total cholesterol and risk of cardiovascular and non-cardiovascular mortality in old age: a population-based study. BMC Geriatr. 2017;17:294. https://doi.org/10.1186/s12877-017-0685-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Gamboa Madeira S, Reis C, Paiva T, Moreira CS, Nogueira P, Roenneberg T. Social jetlag, a novel predictor for high cardiovascular risk in blue-collar workers following permanent atypical work schedules. J Sleep Res. 2021;30(6):e13380. https://doi.org/10.1111/jsr.13380 Epub 2021 May 4. PMID: 33942925.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Wagstaff A. Poverty and health sector inequalities. Bull World Health Organ. 2002;80(2):97–105 PMID: 11953787; PMCID: PMC2567730.

    PubMed  PubMed Central  Google Scholar 

  57. Khambaty T, Schneiderman N, Llabre MM, Elfassy T, Moncrieft AE, Daviglus M, et al. Elucidating the multidimensionality of socioeconomic status in relation to metabolic syndrome in the Hispanic community health study/study of Latinos (HCHS/SOL). Int J Behav Med. 2020;27(2):188–99. https://doi.org/10.1007/s12529-020-09847-y PMID: 31933127; PMCID: PMC7375751.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Prihartono NA, Fitriyani F, Riyadina W. Cardiovascular disease risk factors among blue and white-collar Workers in Indonesia. Acta Med Indones. 2018;50(2):96–103 PMID: 29950527.

    PubMed  Google Scholar 

  59. Pankova A, Kralikova E, Zvolska K, Stepankova L, Blaha M, Ovesna P, et al. Early weight gain after stopping smoking: a predictor of overall large weight gain? A single-site retrospective cohort study. BMJ Open. 2018;8(12):e023987. https://doi.org/10.1136/bmjopen-2018-023987.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Gonçalves-Martins G, Gil-Sala D, Tello-Díaz C, Tenezaca-Sari X, Marrero C, Puig T, et al. Prevalence of peripheral arterial disease and associated vascular risk factors in 65-years-old people of northern Barcelona. J Clin Med. 2021;10(19):4467. https://doi.org/10.3390/jcm10194467 PMID: 34640483; PMCID: PMC8509737.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Fa-Binefa M, Clará A, Pérez-Fernández S, Grau M, Dégano IR, Marti-Lluch R, et al. Early smoking-onset age and risk of cardiovascular disease and mortality. Prev Med. 2019;124:17–22. https://doi.org/10.1016/j.ypmed.2019.04.022 Epub 2019 May 2. PMID: 31054906.

    Article  PubMed  Google Scholar 

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Institutional review board statement

The study was carried out after the authorization of the Ethical Committee of the Balearic Islands, with the prior informed consent of the study subjects and following the norms of the Helsinki Declaration. The confidentiality of the subjects included will be guaranteed at all times in accordance with the provisions of the Organic Law 3/2018, of December 5, on the Protection of Personal Data and guarantee of digital rights and Regulation (EU) 2016/679 of the European Parliament and the Council of April 27, 2016 on Data Protection (RGPD).

Informed consent statement

Informed consent was obtained from all subjects involved in the study.

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This research received no external funding. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Authors and Affiliations

  1. Investigation Group IUNICS, Palma, Spain

    J. I. Ramírez-Manent, B. Altisench Jané, S. Arroyo Bote, C. López Roig, H. González San Miguel & A. A. López-González

  2. Balearic Islands Health Service, Balearic Islands, Palma, Spain

    J. I. Ramírez-Manent, B. Altisench Jané & A. A. López-González

  3. University of the Balearic Islands, Palma, Spain

    J. I. Ramírez-Manent

  4. IDISBA, Balearic Islands Health Research Institute Foundation, Palma, Spain

    J. I. Ramírez-Manent & A. A. López-González

  5. University School ADEMA Palma, Balearic Islands, Palma, Spain

    S. Arroyo Bote, C. López Roig & H. González San Miguel

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  1. J. I. Ramírez-Manent
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Contributions

Conceptualization: J.I.R.M, B.A.J, Á.A.L.G. Data collection and analysis: B.A.J, H.G.S, S.A.B, C.L.R. Methodology: J.I.R.M, B.A.J, Á.A.L.G. Draft: B.A.J, Á.A.L.G. Revision: J.I.R.M, B.A.J, Á.A.L.G, C.L.R. All authors have read and agreed to the published version of the manuscript.

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Correspondence to B. Altisench Jané.

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Ramírez-Manent, J.I., Altisench Jané, B., Arroyo Bote, S. et al. Cardiometabolic profile of 15057 elderly Spanish workers: association of sociodemographic variables and tobacco consumption. BMC Geriatr 22, 872 (2022). https://doi.org/10.1186/s12877-022-03547-w

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BMC Geriatrics

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