Connections Between Stress, Behavioral Factors And Body Mass Index Among Romanian Adults

CÎNPEANU Oana Cristina1*, ROȘESCU Ruxandra2, GLIGA Florina3, TARCEA Monica4

1County Hospital “Dr. Gheorghe Marinescu” Tarnaveni (ROMANIA)

2Emergency Clinical Hospital for Children Cluj_Napoca (ROMANIA)

3Emergency Clinical Hospital Mures county (ROMANIA)

4Department of Community Nutrition and Food Safety, University of Medicine, Pharmacy, Science and Technology from Targu-Mures (ROMANIA)

*Corresponding author: Cinpeanu Oana Cristina, str. Victor Babes 1-3, 103 C/3, Tarnaveni, Mures county, Romania; e-mail: cristina.cinpeanu@outlook.com

ABSTRACT

Introduction. The lifestyle represents a set of values, principles, attitudes and practices, which have consequences on the health status. Unhealthy eating habits, smoking, excessive alcohol consumption, sedentary lifestyle are associated with increased risk of developing obesity and all its complications. The purpose of this study was to identify and establish a connection between the determinants of lifestyle, level of stress and obesity in a sample of adults from Arges county. Methods. This was a pilot study carried out during September – November 2019, upon 103 adults, euthyroid patients from Arges county, monitored and questioned during a consultation at the family doctor. Findings. Most respondents declared to be sensitive to stress, being directly related to the body mass index. Smoking, alcohol and coffee consumption were not statistically significantly associated with obesity, but physical activity was negatively correlated with their body mass index.

Discussions. It is necessary to monitor more closely the behavioral risk factors among the healthy population and to carry out health education measures in order to reduce the risk of related illness.

Keywords: stress, lifestyle, alcohol, eating habits, sedentary, body mass index, obesity.

Introduction 

Obesity is a serious health problem, which shows no signs of being resolved, and the prevalence continues to grow steadily. It is estimated that 650 million people over the age of 18 are affected worldwide, ie 13% of the adult population, both the rich and the poor [1]. This pathology is a complex one resulting from the combination of several factors, genetic, behavioral, environmental, cultural and socio-economic. Obesity groups a number of comorbidities: cardiovascular disease, stroke, hypertension, chronic kidney disease, type 2 diabetes, gout, certain types of cancer, sleep apnea, dyslipidaemia, inflammation and hypercoagulability [2,3].

The lifestyle represents the characteristics that the inhabitants of a particular region have in the spatial and temporal context in which they are located, including the daily behavioral and functional traits of each person in relation to the diet, the workplace, the activities they have. The lifestyle has an important influence on the physical and cognitive health of the human being [4]. Diets low in fruits and vegetables, physical inactivity along with high levels of alcohol and tobacco consumption are estimated to contribute to two thirds of cardiovascular disease, cancer and other causes of mortality [5].

It has been observed that weight gain, with a high body mass index (BMI) and chronic psychosocial stress can lead to a harmful adaptation of the neuroendocrine system to the whole body. For a better understanding of the pathophysiology of obesity, it is important to consider the functioning of the hypothalamo-pituitary-cortical-suprarenal axis under stress in people with obesity [6]. There are studies that have shown a high level of cortisol in response to hypothalamo-pituitary-cortico-suprarenal axis in subjects with high BMI [7]. 

Nutrition plays a very important role in determining a healthy lifestyle. High consumption of vegetables and fruits and healthy eating habits are associated with reducing the risk of developing chronic diseases, decreasing mortality and morbidity caused by diabetes, cardiovascular disease, neurodegenerative diseases and cancer. A high calorie diet favors the occurrence of diabetes, obesity and high blood pressure, subsequently, obesity being a risk factor for the development of cardiovascular diseases or certain types of cancer [8]. A diet based on fruits, vegetables, monounsaturated and polyunsaturated fats, fish and antioxidants is a protection against decreased cognitive function, while refined carbohydrates and saturated fats affect cognitive function [9]. Of particular importance is the regular consumption of meals, starting with breakfast every day. 

Epidemiological studies have shown that skipping breakfast is associated with poor quality diet, adverse cardiometabolic consequences, obesity, other risk factors for cardiovascular disease and micronutrient deficiencies in adults [10-12]. There are also studies that argue that those who work at night shifts are more likely to develop cardiometabolic diseases due to a lower quality diet and a disordered eating schedule (13]. Interprandial snacks or snacking between main meals provide about 10% of daily energy, the recommendations being two snacks in a day, encouraging healthy snacks, which have nutritional value, high fiber foods and avoiding snacks, low in nutrients and high in calories [14].

Physical activity is one of the basic needs of man, which inevitably accompanies daily activities, learning, work, breaks, locomotion and so on. Physical inactivity brings high costs to health and the economy, and sedentarim prevention methods are a key point in combating this problem [15,16]. The benefits of physical activity are well known and include lowering the risk of cardiovascular disease, high blood pressure, diabetes and colon and breast cancer, but also positive effects on mental status, delaying the onset of dementia and maintaining body weight within normal limits. Inactivity can speed up the process of aging and development of various chronic diseases. Sedentarism was described as a major risk factor, independent of physical activity, ≈5.3 million deaths being attributed to physical inactivity. Exercise and weight loss lead to reduced insulin resistance and improved blood sugar control, consequently, to improving blood pressure and serum lipid levels [17-19].

Alcohol consumption is the third leading cause of early death after smoking and obesity in the United States. Starting with September 2019, the American College of Cardiovascular and American Heart Association recommend limited alcohol consumption [20]. It is estimated that 107,800 deaths are caused annually by alcohol consumption which leads to both incidents of violence and accidents, as well as to health problems, from cancers to mental disorders. In addition to the many problems that excessive alcohol consumption brings, the relationship between alcohol consumption and body weight has also been studied. Given that 1 gram of alcohol provides 7.1 kcal, studies show that a positive association between alcohol consumption and weight gain is possible [21, 22]. High alcohol consumption and excess body weight lead to increased gamma-glutamyltransferase, alaninaminotransferase, these changes contributing to the risk of developing both intrahepatic (alcoholic liver cirrhosis) and extrahepatic (metabolic syndrome and cardiovascular and cerebral events). In the case of hemorrhagic vascular accidents, it has been observed that the alcohol consumed in excess has dose-dependent effects, but high risks exist also regarding the onset of a heart attack. By the proinflammatory effect, alcohol also facilitates the carcinogenic activity of smoking, and both factors potentiate its deleterious effects in the occurrence of esophageal and laryngotracheal cancer [23, 24].

The purpose of this study was to establish a connection between the level of stress, the behavioral risk factors (diet, alcohol and tobacco consumption, physical inactivity), and body mass index of adult euthyroid patients from Arges county.

Methods

The present paper was based on a pilot study carried out on 103 patients from Arges county, monitored and questioned during a routine consultation at the family doctor. Data were collected during September – October 2019, with the approval of the family doctor that have the patients on the list, based on a collaboration between Mures and Arges county. All participants in this study agreed with the informed consent. Our study was approved by the University Scientific Ethical Committee also. The inclusion criteria in this study were obese patients with BMI ≥ 30, over 18 years of age, with normal thyroid status (thyroid stimulating hormone, thyroxine and anti-thyroid peroxidase antibodies with normal limits), who were presented in the family doctor’s office. Patients with a history of thyroid dysfunction, those who were given thyroid drugs, patients with diabetes, cardiovascular disease, chronic lung disease, cancer, kidney failure, autoimmune disease, or pregnancy were excluded.

Demographic data were collected, such as age, gender, residency and level of education, as well as BMI. To assess the level of stress and lifestyle risk factors, two questionnaires of 11 and respectively 14 questions were used, which followed information related to perceived stress level, consumption of coffee, alcohol, water, smoking and physical activities. The main questions we asked the participants are the following: “Are you a stress sensitive person”, “How asses your stress level?”, “What does food represent to you?”, “Do you find yourself eating something when you are affected by something?”, “Do you give yourself time for daily rest?”, “Time allowed for rest/week”, “What are the ways you spend your free time?”, “How often do you exercise?”, “What kind of exercise do you do?”.   

Statistical data processing and analysis was performed using GraphPad and Excel statistical software for statistical processing and analysis. Depending on the results of the normality test we applied the Student test for the variables with Gaussian distribution and the Mann Withney test for the variables with non-Gaussian distribution, and if we had more than two samples, we used the One-way Anova test for the parametric data, respectively Kruskal – Wallis test for nonparametric data. Regarding the qualitative variables we applied the Chi² test. We used Spearman-type correlations for nonparametric data and Pearson-type for parametric data to analyze the link between two variables and the intensity of this link. 

Findings

The study included 103 people, who came for a routine consultation at the family doctor’s office. The age of the participants was ranged from 21 to 78 years and included 41 men (39.8%) and 62 women (60.19%).

The reaction to stress. We followed the stress reaction through questions related to stress sensitivity, stress level, the role they attach to nutrition and whether due to stress they choose to eat. We analyzed these aspects by sex, age, medium of origin and BMI (Table I).

Alcohol consumption. In our sample, 4.85% of subjects were consuming 1 glass of alcohol per day, and 0.97% more than 3 glasses per day. For a better evaluation, we have asked the participants which is approximately the amount of alcohol consumed per day and we noticed that 48.15% of the alcohol users drink below 200 ml daily, and the rest of them more; 40% of them prefer wine, 25.71% beer, and 8.57% spirits (Fig. 1).

 

Fig. 1. Distribution of Arges sample according to BMI, and alcohol consumption

Coffee and water consumption. Regarding coffee consumption, 31.07% consume 2-3 cups of coffee, and 2.91% consume more than 3 cups daily. We compared the habits of the Arges county patients related to coffee consumption by gender, age, residency and BMI. Related to the water consumption we observed that only ¼ were consuming more than 6 glasses of water in a day. 

Physical activity and health interest. We have followed data regarding the level and kind of physcial activity, also their interest about health and weight changes, and situations to go seeing the family doctors (Table II). 

Discussions

The lifestyle can significantly influence the health status of individuals,  it is known that there are a variety of risk factors that determine lifestyle, from genetic predisposition, environment of provenance and housing, socio-economic conditions to personality traits. Knowing these risk factors is important for the prevention of a series of pathologies, the identification of the errors related to the lifestyle, respectively, for carrying out programs of education of the general population regarding a healthy lifestyle. Life expectancy is higher, the smaller the number of reduced risk factors on lifestyle [25].

Most subjects from our group said they were sensitive to stress and rated the stress level as moderate. Most have stated that they do not eat stress, but need, then, pleasure, relaxation or a desire to socialize. There are no statistically significant differences between stress reaction and BMI (p=0.69). In the literature there are studies that, on the contrary, report consistent associations between unhealthy food consumption and the level of stress perceived among adults [26, 27].

Regarding alcohol consumption, 42.86 % patients between 41-50 years old were consuming 1-2 glasses a week, followed by those between the ages of 51-60 and those over 60. Most of the Arges county patients prefer wine, then beer and distilled drinks and in smaller numbers have two, three preferences related to the type of alcohol, with no statistically significant differences. Women consume less alcohol than men, as is the case with the elderly, rural dwellers and those with higher BMI values, these differences being not statistically significant (p>0.05).

A recent WHO report shows that rates of harmful alcohol use in Europe have not fallen as expected, although all countries have signed the European Action Plan to Reduce Harmful Alcohol Consumption 2012-2020. The “Status Report on Alcohol Use, Damage and Policy Responses in 30 European Countries 2019”, which uses data collected from 2010 to 2016, shows that over 290,000 people lose their lives in Europe a year due to alcohol-related causes. and calls for stronger political action by countries to help reduce the number [28].

Men spend more time for daily or weekly rest with women, as well as obese people with BMI below 30 kg/m2, but the differences are statistically insignificant (p>0.05). There is a statistically significant difference regarding the greater time allocated for the weekly rest and the older age, (p=0.02). There is no statistically significant difference between the frequency of physical exercises and the demographic data, but we noticed that the higher the BMI value, the less physical activity is. Also, women and urban dwellers are more physically active than men and those in rural areas. Regarding the type of physical exercises, there is a statistically significant relation with the age groups (p=0.04), gender (p=0.03) and residency (p<0.0001). Women prefer walking or running, and men do more physical work. People between the ages of 21-40 are more physically active by walking or running while subjects over 40 are more active through the physical work they do. In the urban environment, participants choose walking or running for physical activity as compared to those from rural areas who are doing more physical work. In the European Region, one in five people has little or no physical activity, with higher levels of inactivity in Eastern countries. In the European Union, two thirds of the adult population does not reach the recommended levels of activity. As a result, it is estimated that physical inactivity deprives Europeans of over 8 million days of healthy life every year, on average [29].

In our Arges county sample study, we discover that increased BMI is srelated to low physical activity, stress level and lifestyle.  Increasing scientific evidence regarding the benefits of healthy dietary patterns attributed to people with obesity and poor diet quality, triggered national calls for increased dietary counseling in the outpatient setting.

Currently, major reforms in undergraduate and postgraduate medical education designed to incorporate advancement into the science of learning and to better prepare physicians for health care delivery in the 21st century provide new impetus and new ways to expand nutrition education and training. medical. opportunities to achieve this objective through curricular programs, pedagogies, technologies and multidimensional competences, based on evaluations.

The results of this study confirm the need to develop a strategy with a role in the prophylaxis of Obesity, which should be addressed to different population groups and which should set the following objectives:

• Awareness of the population regarding a healthy lifestyle and the risk factors of obesity;
• Implementation of measures of health education related to the healthy lifestyle among the population, according to the needs by age groups;
• Establishing effective mechanisms of collaboration between doctor, nutritionist and psychologist for health promotion.

Multiple changes in health behaviors, such as smoking cessation or excessive alcohol use, prevent mortality and morbidity. It is recommended to address the measures of prevention of different diseases through methods of health education and change the bad habits of the lifestyle. 

Acknowledgments: No conflict of interest.  Conflicts of interest: The authors declare that no conflicts of interest exist. Funding: No funding to declare.

REFERENCES

1. World Health Organization (2015) – Obesity and overweight, date accessed on November 10, www.who.int/nut/obs.html. 
2. Hruby A, Hu FB. (2015). The epidemiology of obesity: a big picture. Pharmacoeconomics. 33 (7): 673-689. Doi: 10.1007/s40273-014-0243-x. 
3. Engin A. (2017). The definition and prevalence of obesity and metabolic syndrome. Adv Exp Med Bio 960:1-17. Doi: 10.1007/978-3-319-48382-5_1. 
4. Farhud DD. (2015). Impact of lifestyle on health, Iran I Public Health, 44(11):1442-44.
5. Wilding S., Conner M, et al. (2019). Using the question-behavior effect to change multiple health behaviors: an exploratory randomized controlled trial, Journal of Experimental Social Psychology, 81: 53-60.
6. Van der Valk E., Savas M, van Rossum EF. (2018). Stress and obesity: are there more susceptible individuals? Curr Obes Rep.; 7(2): 193-203.
7. Herhaus B, Petrowski K. (2018). Cortisol stress reactivity to the trier social stress test in obese adults, Obes Facts, 11: 491-500.
8. Sharps M, Robinson E. (2016). Encouraging children to eat more fruit and vegetables: health vs. descriptive social norm-based messages, Appetite, 100: 18-25.
9. Mumme KD, von Hurst PR, et al. (2019). Study protocol: associations between dietary patterns, cognitive function and metabolic syndrome in older adults – a cross-sectional study, BMC Public Health, 19: 535.
10. Wadolowska L, Hamulka J, et al. (2019). Skipping breakfast and a meal at school: its correlates in adiposity context. Report from the ABC of healthy eating study of Polish teenagers, Nutrients, , 11: 1563-65.
11.  Leech RM, Livingstone KM, et al. (2016). Meal frequency but not snack frequency is associated with micronutrient intakes and overall diet quality in Australian men and women, J Nutr, 146: 2027-34.
12. Leech RM, Worsley A, et al. (2015). Characterizing eating patterns: a comparison of eating occasion definitions, Am J Clin Nutr, 102: 1229-37.
13. Leech RM, Timperio A, et al. (2017). Temporal eating patterns: associations with nutrient intakes, diet quality, and measures of adiposity, Am J Clin Nutr, 106:1121-30.
14. Potter M, Vlassopoulos A, et al. (2018). Snacking recommendations worldwide: a scoping review, Adv Nutr, 9: 86-98.
15. Sygit KM, Sygyt M, et al. (2019). Physical activity as an important element in organizing and managing the lifestyle of populations in urban and rural environments, Ann Agric Environ Med, 26(1): 8-12.
16. Pinto Pereira SM, Li L, et al. (2018). Lifetime risk factors for leisure-time physical inactivity in mid-adulthood, Preventive Medicine Reports, 11: 23-30.
17. Guthold R, Stevens AG, et al. (2018). Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1,9 million participants, Lancet Glob Health, 6: e1077-86.
18. Wing RR, Goldstein MG, et al. (2001). Lifestyle changes related to obesity, eating behavior, and physical activity, Diabetes Care, 24: 117-123.
19. Thivel D, Trembley A, et al. (2018). Physical activity, inactivity, and sedentary behaviors: definitions and implications in occupational health, Frontiers in Public Health, 6(288): 15-17.
20. Primary prevention of Cardiovascular Disease Clinical Practice Guideline (ACC/AHA 2019). American College of Cardiovascular and American Heart Association. https://www.ahajournals.org/doi/10.1161/CIR.0000000000000678
21. Traversy G, Chaput JP. (2015). Alcohol consumption and obesity: an update, Curr Obes rep, 4:122-130.
22. Boden-Albala B, Sacco RL. (2000). Lifestyle factors and stroke risk: exercise, alcohol, diet, obesity, smoking, drug use, and stress, Current Atherosclerosis reports, 2:160-166.
23. Nivukoscki U, Niemela M, et al. (2019). Impacts of unfavorable lifestyle factors on biomarkers of liver function, inflammation and lipid status, PloS ONE, 14(6): e0218463.
24. ***EUROSTAT Statistical boog, European Commission –  Health statistics – Atlas on mortality in the European Union 2009 edition, Office for Official Publications of the European Communities, Luxembourg, 1-212.
25. Kontis V, Bennett J.E., Mathers C.D., Li G., Foreman K, Ezzati M. (2017). Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble. Lancet. 389(10076): 1323-1335.
26. Ulrich-Lai Y. M., Fulton S., Wilson M., Petrovich G., Rinaman L. (2015). Stress exposure, food intake, and emotional state. Stress. 18(4): 381-399.
27. Mikolajczyk R.T., El Ansari W, Maxwell A.E. (2009). Food consumption frequency and perceived stress and depressive symptoms among students in three European countries. Nutr J. 8: 31.    
28. World health Organization (2019) – In Europe, people continue to consume more alcohol than in any other place in the world, date of ccess on November 17, http://euro.who.int.
29. Gomes M, Figueiredo D, Teixeira L, et al. (2017). Physical inactivity among older adults across Europe based on the SHARE database. Age Ageing. 46(1):71-77.