Actual prevalence of obesity and its correlation with medical and social factors among the employed population in the Saratov region

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BACKGROUND

Obesity is a common, multifactorial disease that significantly contributes to other chronic noncommunicable diseases (cardiovascular diseases, stroke, type 2 diabetes, cancer, etc.), which are the leading causes of premature mortality worldwide [1]. Higher obesity incidence rate in the employed population is an extremely relevant issue as lower productivity and the treatment cost of the effects of obesity is a serious burden on the public economy [2]. Despite the active implementation of various strategies and government programs [3] aimed at improving the state of health of various populations, obesity rates continue growing. According to forecasts, its prevalence in the Russian Federation may increase several times by 2030 [4].

Thus, obesity incidence monitoring in the working-age population in the Russian Federation and its constituent entities, analysis and determination of major modifiable factors based on regional differences are urgent tasks of preventive medicine [5, 6].

AIM: The study aimed to assess the actual prevalence of obesity in the employed population in the Saratov Region and its correlation with medical and social factors.

MATERIALS AND METHODS

A cross-sectional study was conducted at the Saratov Medical Scientific Center of Hygiene in 2021–2023. During regular medical examinations, 3,721 employees aged 22 to 75 were examined including 2,409 (64.74%) men (mean age 45.60±11.56) and 1,312 (35.26%) women (mean age 47.69±11.04). All study participants were clinically healthy.

Inclusion criteria are the employee population of the Saratov Region; men aged 22–65, and women aged 21–60. Exclusion criteria are individuals taking antihypertensive and lipid-lowering drugs and individuals who did not sign informed consent.

To assess obesity grade, we used the Quételet index (BMI=kg/m²) based on the criteria recommended by WHO (1997) (normal body weight with a BMI of 18.5–24.9; excess body weight with a BMI of 25.0–29.9; obesity with a BMI of 30.0 and higher). The body fat proportion was determined by bioimpedance analysis (computerized cardiorespiratory system and tissue hydration monitoring complex KM-AR-01 DIAMANT) [7]. Based on the body fat percentage scale, three levels were distinguished: 7.2% and less is the low level; 7.3–14.5% is the normal level; 14.6% and higher is the high level.

Age groups were determined by the classification adopted at the 7th All-Union Conference on Age Morphology, Physiology, and Biochemistry (Moscow, 1965), i.e. Early Adulthood group (22–35 years for men, 21–35 years for women); Middle Age group (36–60 years for men, 36–55 years for women); and Late Adulthood group (61–74 years for men, 56–74 years for women).

To collect data on education (high school/university degree) and place of residence (urban/rural), we used a standard questionnaire. The high school group included participants with general high school, vocational, and intermediate vocational education; the university degree group included participants with university degrees. The urban group included participants living in cities and towns; the rural group included participants living in villages, rural areas, and urban-type settlements.

We studied the relationship between BMI, physical activity, and employment factors (work intensity and stress). To assess physical activity, we used the International Physical Activity Questionnaire (IPAQ) as it records all types of daily physical activity at home and at work in points. Hypodynamia criteria for Early Adulthood group participants are less than 21 points, less than 14 points for Middle Age group participants, and less than 7 points for the Late Adulthood group.

Metabolic syndrome was assessed by the 2005 Adult Treatment Panel III (ATP III) [8], i.e. waist circumference of 102 cm or more for men and 88 cm or more for women, indicating abdominal obesity (in case of hereditary predisposition to cardiovascular diseases or type 2 diabetes mellitus, the borderline waist circumference in men is reduced to 94 cm); fasting hyperglycemia (higher than 5.6 mmol/L); hypertriglyceridemia (higher than 1.7 mmol/L); low high density lipoprotein (HDL) (less than 1.04 mmol/L for men and 1.29 mmol/L for women); blood pressure (higher than 130/85 mmHg).

The R programming language was used for statistical processing. Pearson’s chi-square test was used to compare groups. A probit regression was built using the generalized linear model (GLM) function with the parameter “family = binomial (link = “logit”).”

The studied indicators are presented as binary variables. The response is also a binary value (normal body weight or obesity). The probability of obesity in relation to certain factors was determined by a probit regression model:

$p=Ф({\beta }_0+\sum _{i=1}^n{\beta }_i{x}_i)$

where p is the probability of obesity; β₀ is the intercept; x_i may take the value of 0 or 1, if the regression does not have or has the factor with i number; β_i are the coefficients corresponding to x_i factors; Φ is the Laplace transform.

The study was conducted in accordance with bioethical requirements after the participants had signed informed consent (Minutes No. 8 of the local Ethics Committee meeting dated April 29, 2024).

RESULTS

Obesity prevalence is significantly higher in women of all age groups compared to men. In men, obesity is significantly more common in age Middle Age group and the Late Adulthood group compared to Early Adulthood group. In women, a significant difference in the prevalence of obesity is noted between all age groups; this value increases with age (see Table 1).

 

Table 1. Prevalence of obesity in the employee population by sex and age

Age groups	Male (n=2,409)	Female (n=1,313)	Pearson’s chi-square test
			Male	Female
I (early adolescence), n = 713	105 (19.02%)	44 (27.33%)	χ²=39.573; р1–2 <0.001 χ²=0.393; р2–3=0.531 χ²=12.856; р1–3 <0.001	χ²=6.946; р1–2=0.009 χ²=9.044; р2–3=0.003 χ²=18.838; р1–3 <0.001
	χ²=5.204; p=0.023
II (middle age), n = 2,483	544 (33.15%)	322 (38.24%)
	χ²=6.352; р=0.012
Late adolescence, n = 526	67 (31.02%)	149 (48.06%)
	χ²=15.284; р <0.001
Total	716 (29.72%)	515 (39.22%)
	χ²=34.659; р <0.001

Note: Note: p_1–2 is the statistical significance of the indicators between Early Adulthood group and Middle Age group; p_2–3 is the statistical significance of the indicators between Middle Age group and the Late Adulthood group; p_1–3 is the statistical significance of the indicators between Early Adulthood group and the Late Adulthood group.

 

In the examined sample, a significant relationship was found between obesity and medical and social factors, including above-normal waist circumference, hyperglycemia, hypertriglyceridemia, low HDL, arterial hypertension, university degree, work intensity, and physical inactivity. However, there is no relationship between obesity and work stress, place of residence, smoking, and marital status (see Table 2).

 

Table 2. Analysis of the relationship between obesity and medical and social factors

Factors	Obesity incidence, %		χ2; р
	No	Yes
Metabolic syndrome factors
Above-normal waist circumference	0.05	0.69	786.44; <0.001
Hyperglycemia	0.29	0.49	66.68; <0.001
Hypertriglyceridemia	0.29	0.54	83.54; <0.001
Low HDL	0.28	0.49	67.96; <0.001
Arterial hypertension	0.22	0.43	101.81; <0.001
Social and behavioral factors
University degree	0.34	0.28	10.80; 0.001
Work intensity	0.37	0.27	11.51; 0.001
Work stress	0.34	0.33	0.00; 0.97
Urban resident	0.34	0.32	1.10; 0.29
Hypodynamia	0.31	0.44	5.61; 0.02
Smoking	0.38	0.35	3.05; 0.08
Marital status	0.325	0.329	0.03; 0.8

 

In both the general group and the male group, regression analysis showed a positive relationship of obesity with above-normal waist circumference, hyperglycemia, low HDL, and arterial hypertension, i.e. indicators of cardiometabolic risk. In the female group, there was a positive relationship of obesity with above-normal waist circumference and arterial hypertension. In all observation groups, obesity was not related to triglyceride levels (see Table 3).

 

Table 3. Analysis of the relationship between obesity and some clinical indicators in the employed population (probit regression)

Factors (ratio)	All participants (n=730)		Male (n=526)		Female (n=204)
	Value	p	Value	p	Value	p
Intercept (β0)	–3.64	<0.001	–3.58	<0.001	–3.84	<0.001
Above-normal waist circumference (β1)	3.71	<0.001	3.66	<0.001	3.96	<0.001
Hyperglycemia (β2)	0.56	0.009	0.58	0.03	0.46	0.20
Hypertriglyceridemia (β3)	–0.27	0.27	–0.24	0.45	–0.33	0.36
Low HDL (β4)	0.73	0.001	0.87	0.006	0.61	0.06
Arterial hypertension (β5)	1.01	<0.001	0.98	<0.001	0.99	0.001

 

Regression analysis indicates that a university degree and intensive work significantly reduce the probability of obesity; whereas physical inactivity, on the contrary, contributes to its development in the general group of subjects. Sex classification of participants showed that for men intensive work reduces the probability of obesity; whereas a university degree reduces the probability of obesity in women (see Table 4).

 

Table 4. Analysis of the relationship between obesity and some social factors in the employed population (probit regression)

Factors (ratio)	All participants (n=730)		Мale (n=526)		Female (n=204)
	Value	р	Value	р	Value	р
Intercept (β0)	–0.49	<0.001	–0.35	0.001	–0.22	0.07
University degree (β1)	–0.33	0.05	–0.10	0.40	–0.48	0.02
Work intensity (β2)	–0.38	0.02	–0.24	0.04	–0.10	0.69
Low physical activity (β3)	0.44	0.04	0.27	0.10	0.39	0.12

 

We analyzed body fat percentage in individuals with normal body weight, overweight individuals, and individuals with obesity (see Fig. 1).

 

Fig. 1. Body fat in individuals with normal body weight, overweight, and obese individuals.

Note: BMI, body mass index.

 

It is significant that 0.75% individuals with normal BMI have a high body fat percentage; whereas 30.34% obese individuals have a normal body fat percentage, and 10.94% obese individuals have a low body fat percentage.

DISCUSSION

In all age groups, obesity is more prevalent in women than in men. It may be caused by a few reasons, including socio-cultural and biological factors, phenotype, behavior, metabolic differences, etc. [9, 10]. In women, the prevalence of obesity increases with age, reaching the highest values in the Late Adulthood group, which is consistent with data from other studies [11, 12]. In men, the prevalence of obesity also increases with age, but there is no difference between Middle Age group and the Late Adulthood group. This is consistent with the study, which found that BMI starts to increase at 35–40 years of age in men and at 55–60 years of age in women [13].

This study showed that the prevalence of obesity is higher in individuals with low physical activity; whereas it is lower in individuals whose work is considered intense, which is probably caused by higher physical activity [14, 15]. In the contemporary job market, most professions involve using the computer for work; thus, employees are hypodynamic as they have to spend most of their working hours in a sitting position [14]. In turn, balance between energy consumption and energy requirements is a key condition for maintaining body weight.

Individuals with a university degree are less likely to develop obesity, apparently due to a commitment to a healthy lifestyle, awareness of the adverse effects of low physical activity, poor nutrition, and other factors associated with the development of obesity and other diseases [16, 17]. Some studies show that the incidence of obesity is significantly lower in women with a university degree, which is evidenced by this study; whereas in men, the level of education is often not related to this indicator [18].

The relationship between BMI and most metabolic syndrome indicators is reasonable and evidenced by a population study by Ortegа et al. [19]. They found that high BMI has a closer relationship with cardiometabolic risk than other obesity indices. However, there is ingoing debate about the obesity paradox providing that the survival prognosis in cardiovascular diseases is higher in individuals with BMI-defined obesity [20]. Elagizi et al. described the relationship between favorable outcomes in individuals with cardiovascular diseases, excess body weight, and Grade I obesity rather than more severe obesity [21]. Today, opinions on the obesity paradox are controversial. It is possible that this phenomenon may be caused by an obesity assessment error, incorrect study design, etc. In addition, the mechanisms of the positive effect of obesity on the cardiovascular system have not been described [22]. Thus, a high BMI in a patient is still a reason for including him/her in the cardiometabolic risk group [21].

The study shows that some individuals with a normal BMI have higher body fat percentage. It may be indicative of excess visceral fat, which can increase cardiometabolic risk as visceral adipose tissue acts as an independent endocrine organ and has a pronounced influence on carbohydrate and lipid metabolism, unlike subcutaneous adipose tissue [23]. However, in overweight and obese cases, body fat percentage can be either high or normal; therefore, in clinical practice, the possible metabolically healthy obesity should be considered [24]. In this case, the BMI of athletes can significantly exceed 25 kg/m² due to high muscle mass; whereas the proportion of fat tissue is low. Thus, to assess obesity, including cardiometabolic risks, it is advisable to rely on check-ups and assess risks in individuals with normal body weight as well [25].

The observed relationship between obesity and other indicators of metabolic syndrome (abdominal obesity, hyperglycemia, low HDL, arterial hypertension) is an evidence of the synergistic effect in the development of cardiometabolic disorders and obesity-associated diseases (cardiovascular diseases, type 2 diabetes mellitus, etc.) as evidenced by the studies [26–28]. Indeed, this paper does not study all the factors associated with obesity that contribute to its development (nutrition, smoking, alcohol, etc.). It should be considered that numerous factors related to the probability of obesity have complex interdependencies and a synergistic effect; thus, it is extremely difficult to assess their interaction and study the probability of obesity.

The study may be the basis for software and digital technologies, personalized sensor technologies, and digital strategies for assessing individual and group risks of obesity and associated diseases in the employed population [29, 30]. It is highly relevant for the implementation of the federal project Development of Incentive System to Encourage Healthy Lifestyle, Including Healthy Nutrition and Giving Up Bad Habits as part of the national Demography Project.

CONCLUSION

A representative sample of the employed population in the Saratov Region was used to establish the obesity prevalence patterns in males and females in various age groups. The study identified the major medical and social obesity risk factors (sex, age, education, work intensity, physical activity). It is essential to consider the probability of a high body fat percentage in individuals with normal BMI to prevent and mitigate cardiometabolic risks. The findings of this study are potentially beneficial for the creation of preventative strategies integrated into employee wellness initiatives focused on maintaining the wellbeing, quality of life, and professional longevity of the employed population.

ADDITIONAL INFO

Authors’ contribution. V.N. Dolich — the concept and design of the study, writing a text; N.E. Komleva — writing a text, editing; M.V. Pozdnyakov — data collection and processing; S.I. Mazilov — statistical data processing; I.V. Zaikina — writing text; I.N. Lutcevich — writing text. All authors confirm that thei authorship meets the international ICMJE criteria (all authors have made a significant contribution to the development of the concept, research and preparation of the article, read and approved the final version before publication).

Funding source. This study was not supported by any external sources of funding.

Competing interests. The authors declares that there are no obvious and potential conflicts of interest associated with the publication of this article.

Consent for publication. Written consent was obtained from all the study participants before the study screening in according to the study protocol approved by the local ethic committee.

About the authors

Vladimir N. Dolich

Saratov Hygiene Medical Research Center

Author for correspondence.
Email: vndolich@mail.ru
ORCID iD: 0000-0002-8980-5117
SPIN-code: 4085-7055
Russian Federation, Saratov

Natalia E. Komleva

Saratov Hygiene Medical Research Center; Saratov State Medical University n.a. V.I. Razumovsky

Email: nekomleva@yandex.ru
ORCID iD: 0000-0003-4099-9368
SPIN-code: 7145-3073

MD, Dr. Sci. (Medicine)

Russian Federation, Saratov; Saratov

Mikhail V. Pozdnyakov

Saratov Hygiene Medical Research Center; Saratov State Medical University n.a. V.I. Razumovsky

Email: mpozdnyakov@yandex.ru
ORCID iD: 0000-0002-2067-3830
SPIN-code: 6726-4542

Cand. Sci. (Physics and Mathematics)

Russian Federation, Saratov; Saratov

Svyatoslav I. Mazilov

Saratov Hygiene Medical Research Center

Email: smazilov@yandex.ru
ORCID iD: 0000-0002-8220-145X
SPIN-code: 2048-0643

Cand. Sci. (Biology)

Russian Federation, Saratov

Inna V. Zaikina

Saratov Hygiene Medical Research Center

Email: innaza2@mail.ru
ORCID iD: 0000-0003-4234-7056
SPIN-code: 9644-0101

MD, Cand. Sci. (Medicine)

Russian Federation, Saratov

Igor N. Lutsevich

Saratov Hygiene Medical Research Center; Saratov State Medical University n.a. V.I. Razumovsky

Email: ilutsevich@yandex.ru
ORCID iD: 0000-0003-2008-6895
SPIN-code: 2435-6069

MD, Dr. Sci. (Medicine)

Russian Federation, Saratov; Saratov

References

Supplementary files