Family History and Smoking as Key Predictors of Obesity among Young Adults in Latin America: A Secondary Data Analysis

• Nurul Putrie Utami, S.Gz., M.P.H.
• Andri Pranolo S.Kom., M.Cs., Ph.D.

Vol. 6 No. 4 (2025) | Pages: 137-144

DOI: 10.47679/makein.2025276   Reader: 301 times PDF Download: 178 times

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Abstract

INTRODUCTION

In the 21st century, obesity continues to pose a significant global health challenge. The World Health Organization (WHO) defines obesity as an excess accumulation of body fat that can have detrimental effects on one’s well-being (Purnell, 2023). By 2022 it is known that 1 in 8 people in the world are obese and the number of obese adults has more than doubled and obese adolescents quadrupled since 1990 (World Health Organization, 2024). The obesity rate in Indonesia is also showing an alarming trend. As a result of Indonesian’s Health Research called Riset Kesehatan Dasar (Riskesdas) data, the level of obesity at the Indonesian national level especially in adult subjects in 2018 has reached 21.8% and is still increasing (Kementerian Kesehatan Republik Indonesia, 2018). Obesity can lead to serious health complications, including type 2 diabetes mellitus, cardiovascular and heart-related diseases, and various types of cancer (Bhurosy & Jeewon, 2014). Research indicates that obesity can have a detrimental impact on an individual’s mental well-being, manifesting in the development of anxiety, depression, and diminished self-esteem (King et al., 2020; Robinson et al., 2021).

This phenomenon is almost universal, regardless of whether it occurs in developing or developed countries. Recently, researchers stated that biopsychosocial factors influence obesity through the combination of genetic predisposition, psychological factors, and environmental context (Karunamuni et al., 2021; Masood & Moorthy, 2023). Genetics, environment, and psychology are other risk factors associated with obesity. According to research, a person with a family history of obesity is more likely to develop obesity because of obesity is a neuro-behavioral disease with a strong genetic background, influenced by eating behavior and the macro-environment (Silventoinen & Konttinen, 2020). Genetic studies show BMI is highly heritable and polygenic, with new genetic variation emerging after early childhood, suggesting a link to behavior (Masood & Moorthy, 2023; Silventoinen & Konttinen, 2020). Environment is also important; if a person does not have access to sports facilities and healthy food, it can affect their eating habits and physical activity (Dixon et al., 2021). In addition, psychological components such as eating disorders and stress can affect a person's diet, which in turn can lead to obesity (Dakanalis et al., 2023; Dalili et al., 2020).

The complexity of obesity’s risk factor was also explained in nutrition-ecological modelling (NutriMod) (Hummel et al., 2013). This model showed that energy balance affect obesity and energy balance was affected by lifestyle factors (nutrition behavior, physical activity, media consumtion, sleeping, smoking ), biological factors (age, genetics, sex, hormones), and prenatal, infantile factors (infant nutrition, birth weight) (Hummel et al., 2013). Eating habits or so-called eating patterns that do not comply with healthy dietary guidelines, such as the consumption of foods high in energy or calories, high in saturated fat content, and high in added sugars, as well as irregular frequency or frequency of eating, sedentary lifestyle, and lack of physical activity significantly contribute to an increased risk of advanced stages of obesity (Ramesh Masthi & Jahan, 2020; Silveira et al., 2022).

In Latin America, obesity rates among young adults have surged significantly in the past decade, primarily due to urbanization, shifting food landscapes, and socio-economic disparities (Palacios et al., 2021; Popkin & Reardon, 2018). Several factors, such as the bulit environment, exposure of food, genetics, economic and political interest, social inequity, limited access to scientific knowledge, culture, and contextual behaviour factors are related to the escalation of obesity’s prevalence in Latin America (Ferreira et al., 2024). Moreover, adult obesity in Columbia has shown that transportation access also plays a role in obesity, in which motorbike and car ownership contribute to obesity risk and active mobility such as walking or cycling is linked to lower Body Mass Index (BMI) (Castro et al., 2024). Physical activity, food intake, knowledge of a healthy lifestyle, perceptions, attitudes, and health habits have also been found to be associated with body mass index (BMI) among populations in Latin America (Proaño et al., 2024).

According to the data of obesity risk factors in Latin America, a full exploration of several risk variables, including physical activity, lifestyle, dietary habits, genetic predispositions, smoking, and alcohol intake, remains necessary. This study reaffirms the established connection between diet, physical activity, and obesity, while also assessing whether these traditional risk factors maintain their predictive significance among young adults in Latin America, or if other determinants, such as familial history and smoking behavior, exert a more substantial influence. This study analyzes secondary data from three Latin American nations to identify key determinants of obesity in this demographic and to enhance the contextual understanding of obesity risk.

METHOD

Research Design

This study design was a cross-sectional design, where data was collected at a single point in time, to evaluate the relationship between eating habits and obesity rates. This design was chosen because it allows the researcher to look at all variables simultaneously and analyze the relationship between each variable without spending a lot of time (Mann, 2003).

Data Source

The data utilized in this study was sourced from the UCI (University of California, Irvine) Machine Learning Repository (UCI Machine Learning Repository, 2023). It includes 2,111 records (instances) and 17 attributes, including the class label indicating the obesity level of each individual. Those records contain 77 percent of the data that was generated synthetically using Weka tools and the SMOTE (synthetic minority over-sampling technique) filter, while the remaining 23% was collected directly from respondents via a web platform.

The preliminary gathering of data was conducted via a webpage via a survey in which participants assessed their dietary habits and several factors that aided in determining their physical state. The survey was available online for 30 days. Upon the completion of data collection, the data was then preprocessed for utilization in various data mining techniques. The number of records was 485 records. The details of the data collection were explained in the previous research (Palechor & Manotas, 2019). In this research we processed 485 records of the data that was obtained directly from the survey.

Measures and Covariates

The data for this study were obtained by filling out a questionnaire through a web platform with a survey containing several questions asking anthropometric data, family history of overweight and obesity, smoking habits, alcohol consumption habits, and eating habits in the form of frequency of high calorie intake, vegetable intake, eating habits, and drinking water, as well as habits of using technology tools and physical activity. The question and answer of each variables was already explained in the previous study (Palechor & Manotas, 2019). Nutritional status data was classified into two groups by the BMI values: Non-obesity (BMI<30 kg/m²) and Obesity (BMI ≥30 kg/m²) based in CDC standard (Centers for Disease Control and Prevention, 2024).

Data Analysis

Statistical analysis that can be used for this data is using statistical software such as SPSS version 29. The data analysis process that will be carried out includes descriptive analysis and chi square analysis to analyze two variables. Then, a logistic regression test was conducted to analyze obesity risk factors. This study uses an alpha confidence level of 5%.

RESULTS OF STUDY

Table 1. Characteristics of respondents (N=485)

Variables	Nutrition status		Total (%)	p-value
	Non obesity (%)	Obesity (%)
Gender
Female	202 (89.8)	23 (10.2)	225 (100)	0.181
Male	223 (85,8)	37 (14.2)	260 (100)
Age
< 40 years	415 (88.7)	53 (11.3)	468 (100)	<0.001
≥ 40 years	10 (58.8)	7 (41.2)	17 (100)

Table 2. Association Of Family History and Lifestyle Factors with Nutritional Status

Variables	Nutrition status		Total	p
	Non obesity (%)	Obesity (%)
Family history				<0.001*
No	182 (95.8%)	8 (4.2%)	190 (100%)
Yes	243 (82.4%)	52 (17.6%)	295 (100%)
Total	425 (87.6%)	60 (12.4%)	485 (100%)
Smoking				<0.001*
No	404 (89.0%)	50 (11%)	454 (100%)
Yes	21 (67.7%)	10 (32.3%)	31 (100%)
Total	425 (87.6%)	60 (12.4%)	485 (100%)
Physical activity (PA)				0.082
No PA	129 (82.2%)	28 (17.8)	157 (100%)
1 – 2 days	139 (90.3%)	15 (9.7%)	154 (100%)
3 – 4 days	99 (89.2%)	12 (10.8%)	111 (100%)
5 – 6 days	58 (92.1%)	5 (7.9%)	63 (100%)
Total	425 (87.6%)	60 (12.4%)	425 (87.6%)
Technology device (TV, video game, etc)				0.188
< 2 hours	204 (86.4%)	32 (13.6%)	236 (100%)
2 – 5 hours	161 (91.0%)	16 (9%)	117 (100%)
> 5 hours	60 (83.3%)	12 (16.7%)	72 (100%)
Total	425 (87.6%)	60 (12.4%)	425 (87.6%)
Transportation				0.075
Automobile	79 (80.6%)	19 (19,4%)	98 (100%)
Bike	6 (85.7%)	1 (14,3%)	7 (100%)
Motorbike	6 (75.0%)	2 (25%)	8 (100%)
Public transportation	283 (89.0%)	35 (11.0%)	318 (100%)
Walking	51 (94.4%)	3 (5.6%)	54 (100%)
Total	425 (87.6%)	60 (12.4%)	425 (87.6%)
Alcohol consumption				0.379
No	154 (88.0%)	21 (12%)	175 (100%)
Rarely	235 (88.7%)	30 (11.3%)	265 (100%)
Weekly	35 (79.5%)	9 (20.5%)	44 (100%)
Daily	1 (100%)	0 (0%)	1 (100%)
Total	425 (87.6%)	60 (12.4%)	425 (87.6%)
* p < 0.05 showed statistically significant differences.

Table 3. Relationship Between Eating Habits And Nutritional Status

Variable		Nutrition status		Total	p
		Non-obesity (%)	Obesity (%)
Consumption of high caloric food	No	132 (89.2%)	16 (10.8%)	148 (100%)	0.489
	Yes	297 (86.9%)	44 (13.1%)	337 (100%)
	Total	425 (87.6%)	60 (12.4%)	485 (100%)
Vegetables consumption frequency	No	27 (87.1%)	4 (12,9%)	31 (100%)	0.796
	Sometimes	230 (86.8%)	35 (13,2%)	265 (100%)
	Always	168 (88.9%)	21 (11,1%)	189 (100%)
	Total	425 (87.6%)	60 (12.4%)	485 (100%)
Number of main meals daily	1-2 x	90 (84,9%)	16 (15,1%)	106 (100%)	0.073
	3x	290 (87,13%)	43 (12,9%)	333 (12,9%)
	4x	45 (97.8%)	1 (2.2%)	46 (100%)
	Total	425 (87.6%)	60 (12.4%)	485 (100%)
Consumption of food between meals	No	17 (89.5%)	2 (10.5%)	19 (100%)	0.061
	Sometimes	239 (85.1%)	42 (14.9%)	281 (100%)
	Frequently	126 (94.0%)	8 (6%)	134 (100%)
	Always	43 (84,3%)	8 (15.7%)	51 (100%)
	Total	425 (87.6%)	60 (12.4%)	485 (100%)
Calories consumption monitoring	No	375 (86.8%)	57 (13.2%)	432 (100%)	0.116
	Yes	50 (94.3%)	3 (5.7%)	53 (100%)
	Total	425 (87.6%)	60 (12.4%)	485 (100%)
Consumption of water daily	< 1 L	113 (86.9%)	17 (13.1%)	130 (100%)	0.059
	1-2 L	235 (90.4%)	25 (9.6%)	260 (100%)
	> 2 L	77 (81.1%)	18 (18.9%)	95 (100%)
	Total	425 (87.6%)	60 (12.4%)	485 (100%)
* p < 0.05 showed statistically significant differences.

Table 4. Multivariate analysis of obesity’s risk factor

		B	S.E.	Wald	df	Sig.	Exp(B)	95% CI
Variables	Family history	1.605	0.406	15.606	1	<0.001*	4.977	2.245-11.035
	Smoking	1.067	0.466	5.249	1	0.022*	2.907	1.167-7.241
	Age	1.577	0.594	7.054	1	0.008*	4.839	1.512-15.492
	Gender	-0.520	0.311	2.793	1	0.095	0.595	0.323-1.094
	Calories consumption monitoring	-1.233	0.673	3.359	1	0.067	0.291	0.078-1.089
	Food intake between meals
	No			8.133	3	0.043	Reff
	Sometimes	-0.123	0.822	0.022	1	0.881	0.885	0.177-4.426
	Frequently	-1.260	0.891	2.001	1	0.157	0.284	0.049-1.626
	Always	0.048	0.896	0.003	1	0.957	1.049	0.181-6.076
	Consumption of water daily
	<1 L/day (ref.)			5.601	2	0.061	Reff
	1-2 L/day	-0.692	0.369	3.527	1	0.060	0.500	0.243-1.031
	>2 L/day	0.042	0.416	0.010	1	0.919	1.043	0.462-2.355
	Constant	-3.931	1.063	13.669	1	<0.001	0.020
* p < 0.05 showed statistically significant differences.

This study analyzed the results of obesity risk factor research with the research subjects being residents of Mexico, Peru and Colombia. An overview of the characteristics of respondents in this study can be seen in Table1.

The prevalence of obesity was slightly higher among males (14.2%) compared to females (10.2%), although this difference was not statistically significant (p = 0.181). In contrast, age showed a significant association with obesity (p < 0.001). Participants aged 40 years and above had a markedly higher prevalence of obesity (41.2%) compared to those under 40 years (11.3%). These findings indicate that age is a more influential factor in determining obesity status than gender in this study population.

Based on the data in Table 2 shows the distribution of obesity according to various lifestyle factors. Obesity was significantly associated with age, family history, and smoking status (p < 0.05). Similarly, individuals with a family history of overweight or obesity were more likely to be obese (17.6%) than those without such a history (4.2%). Smoking was also associated with obesity, with smokers showing a higher proportion of obesity (32.3%) compared to non-smokers (11.0%).

Non-significant trends were detected for other factors, such as alcohol consumption, mode of transportation, technology use, and physical activity. However, compared to individuals who were more active or who usually walked, those who reported no physical activity or who mostly utilized cars tended to have a higher frequency of obesity.

Table 3 presents the association between dietary habits and obesity status. The analysis showed no statistically significant relationships between obesity and dietary factors, including the consumption of high-calorie foods (p = 0.340), vegetable consumption frequency (p = 0.553), number of main meals per day (p = 0.073), snacking frequency (p = 0.061), calorie intake monitoring (p = 0.081), and water consumption (p = 0.059).

Although the associations showed non-significant trends, several tendencies were observed. Participants who consumed high-calorie foods had a slightly higher prevalence of obesity (13.1%) compared to those who did not (10.8%). Similarly, those who consumed vegetables less frequently (“sometimes”) showed higher obesity prevalence (13.2%) than those who always ate vegetables (11.1%). A similar pattern was observed among participants who ate snacks frequently (15.7%) and those who drank less than 1 L of water daily (13.1%), suggesting that inadequate hydration and frequent snacking may contribute to higher obesity risk.

These findings indicate that while no single dietary factor showed a strong independent association with obesity in this study, overall dietary patterns such as frequent snacking, low vegetable intake, and limited water consumption may still play a contributory role in obesity risk.

The result of multivariate analysis using logistic regression was shown in Table 4. Three variables were found to be statistically significant predictors: family history, smoking, and age. Individuals with a family history of obesity were almost five times more likely to be obese (OR = 4.98; 95% CI: 2.25–11.04; p < 0.001). Similarly, smokers had nearly three times higher odds of being obese compared to non-smokers (OR = 2.91; 95% CI: 1.17–7.24; p = 0.022). Age was also a significant factor, with participants aged ≥40 years having 4.84 times higher odds of obesity compared to those younger than 40 years (95% CI: 1.51–15.49; p = 0.008).

Other variables, including gender, food intake between meals, water consumption, and calorie intake monitoring, were not significantly associated with obesity (p > 0.05). However, some trends were observed. Participants who reported eating between meals “sometimes” or “frequently” showed lower odds of obesity compared to those who did not snack at all (OR = 0.61 and OR = 0.19, respectively), though these associations were not statistically significant. Similarly, those who drank 1–2 liters of water per day had lower odds of obesity (OR = 0.35; p = 0.060), suggesting a potential protective trend. Overall, these findings indicate that genetic background, smoking behavior, and age play major roles in predicting obesity among the study population, while dietary habits and lifestyle behaviors showed weaker or nonsignificant associations after adjustment for confounding factors.

DISCUSSION

Our findings from this research indicate that genetic factors, smoking, and age play an important role in determining the nutritional status of individuals. These findings align with a growing body of evidence highlighting the complex interplay between genetic predisposition and behavioral factors in determining obesity risk. Age remains one of the most consistently documented factors influencing obesity prevalence becase it was one of the biological factor that affect obesity (Hummel et al., 2013). Individuals who are 40 years old or older are five times more likely to become obese compared to those under 40. Several studies have reported that the likelihood of obesity increases with advancing age, primarily due to metabolic slowdown, hormonal alterations, and changes in physical activity levels (Dalili et al., 2020; Dixon et al., 2021; Faghri, 2015; Villareal, 2023; Xu et al., 2019). For instance, McPhee et al. (2016) found that individuals aged 40–59 years were twice as likely to be obese compared to younger adults, attributing this to decreased basal metabolic rate and sedentary behaviors. Similarly, Pataky et al. (2021) emphasized that aging contributes to increased fat mass and reduced lean body composition even in populations maintaining constant caloric intake.

The results of this study also found that genetic factors that have been shown from the family history of overweight and obesity were related to obesity. Individuals with a family history of obesity were nearly five times more likely to be obese. These results indicate that a family history of obesity is a significant factor in the development of obesity. This finding aligns with the concept of heritability of obesity, where genetic and family environmental factors strongly contribute to an individual's risk of obesity. This findings are similar to the results of other studies in health students where a history of obesity has an association with student obesity status and students without a family history of obesity are less likely to be obese (Nik et al., 2022). Genetic factors play a major role in obesity development, with evidence showing that variations in genes—particularly those affecting the leptin–melanocortin pathway and energy regulation in the central nervous system—substantially influence individual susceptibility to obesity (Bouchard, 2021). Based on the results of genetic research, there are several effects of genetic mutations that cause severe obesity because they participate in the leptin-melanocortin pathway that affects appetite regulation (Masood & Moorthy, 2023). A large-scale genomic study by Locke et al. (2015) identified over 90 genetic loci associated with body mass index (BMI), confirming that genetic predisposition plays a significant role in the regulation of appetite and energy metabolism.

This study indicates that smoking is a risk factor for obesity, where people who smoke have a three-fold greater risk of experiencing obesity. The relationship between smoking and obesity is also supported by several previous studies. The risk factor of smoking on obesity risk can occur due to the impact of cigarette smoke. Cigarette smoke can cause various health problems, especially in the respiratory system because it is a harmful air pollutant and contributes to the amount of PM_2.5 particles (particles less than 2.5 micrometers in diameter) that are small and can be inhaled deep into the lungs (Nuryati et al., 2023). In a study in Konta Mexico, it was found that PM2.5 exposure was associated with obesity in all subjects and the strongest association was found in adolescents (Tamayo-Ortiz et al., 2021). These findings demonstrate the importance of targeted interventions that address both smoking cessation and weight management. Public health campaigns could play a crucial role in raising awareness about the interconnected risks associated with these behaviors.

In this study, we did not observe significant associations between obesity and self-reported dietary habits or physical activity. The other study revealed that obesity risk is increased by sedentary activity, monitoring of diet, screen time duration, lower meal frequency, high calorie food consumption, and low vegetable intake (Alhashemi et al., 2022; Berry et al., 2021; Godoy-Izquierdo et al., 2021; Longo-Silva et al., 2024; Mahumud et al., 2021; Moschonis & Trakman, 2023; Utami et al., 2018; Zerón-Rugerio et al., 2021). The characteristics of the respondents, primarily young adults, may cause these different findings and affect their patterns of technology use and physical activity levels (Fortunati et al., 2025). This study also only explored the habit of eating vegetables and not fruit consumption. Both habits have significantly increased the risk of obesity in society. Regarding fast food consumption, no relationship was found because its impact on obesity occurs only when consumption exceeds energy needs. Research conducted by Teo et al. revealed that the Energy Intake Rate (EIR), which combines food energy density (kcal/g) and the rate of eating (g/min), is related to obesity as indicated by BMI and abdominal circumference measurements (Teo et al., 2021).

The strength of this study lies in its comprehensive assessment of obesity risk factors, which include biopsychosocial elements such as genetics, diet, physical activity, other health habits, and the influence of transportation use. However, there are several limitations to this study that could be addressed in future research. The majority of respondents were categorized as young adults, which may make the findings less representative of the actual population, as there are physiological and lifestyle differences across age groups. Additionally, this study did not directly assess nutritional status; it solely required eligible respondents to complete a survey. Furthermore, the full recruitment process for this study is unknown. Several questions on the questionnaire concerning eating habits and physical activity are also subjective and depend on the respondent's honesty. For more accurate data, it is highly recommended to measure food intake with valid instruments like food frequency questionnaires and to use objective measurements from devices such as accelerometers or wearable trackers (Vu et al., 2017). Moreover, the cross-sectional design we use limits the capacity of these studies to illustrate cause-and-effect relationships or changes in weight status over time. This limitation demonstrates the importance of longitudinal studies that can track changes over time and provide a clearer picture of the dynamics between eating habits, physical activity, and weight status. By employing a combination of subjective and objective measures in future research, we can enhance the reliability of findings and develop more effective interventions. Consequently, it is crucial to conduct a comprehensive examination of recent prospective studies.

CONCLUSIONS AND RECOMMENDATION

This study found that family history of obesity, smoking status, and age were significant predictors of obesity. Individuals with a family history of obesity were nearly five times more likely to be obese, while smokers had about three times higher odds compared to non-smokers. In addition, older age was associated with an increased likelihood of obesity, indicating that the risk tends to rise as individuals get older. These findings highlight that genetic predisposition, behavioral factors, and age-related metabolic changes play more substantial roles in obesity than dietary or physical activity patterns. The study contributes to scientific knowledge by emphasizing that hereditary and lifestyle-related factors are stronger predictors of obesity risk than eating behavior alone.

Public health initiatives must prioritize smoking cessation programs and early prevention strategies for persons with a familial predisposition to obesity, especially focusing on older adults at elevated risk. Community-oriented and digital health programs can facilitate the integration of genetic risk awareness with lifestyle adjustment. Additionally, longitudinal studies are advised to investigate the causal connections among genetic, behavioral, and age-related factors in the etiology of obesity.

Acknowledgments

The authors gratefully acknowledge the financial support from the Institute for Research and Community Service (LPPM), Universitas Ahmad Dahlan (Grant No. PD-044/SP3/LPPM-UAD/XI/2024).

DECLARATION

Ethics approval and consent to participate

Not applicable

Consent for publication

Data of thic publication can be accessed from open access data:

https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the UC Irvin Machine Learning Repository,link:

https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition

Conflicts of Interest Statement

The authors declare that they have no competing interests.

Statement on the Use of Artificial Intelligence (AI)

This paper was using Mendeley reference.

Funding

This research was funded by the Institute for Research and Community Service (LPPM), Universitas Ahmad Dahlan (Grant No. PD-044/SP3/LPPM-UAD/XI/2024).

Authors' contributions

First Author (Nurul Putrie Utami): Nurul Putrie Utami played a major role in analyzing the research and writing the manuscript. Nurul Putrie Utami was also responsible for coordinating the research team and ensuring the quality of the research results.

Second Author (Andri Pranolo): Andri Pranolo contributed to data processing, performing statistical analysis, and assisting in the interpretation of results.

ABOUT THE AUTHORS

Nurul Putrie Utami has completed bachelor's and master's degrees from Gadjah Mada University, majoring in nutrition and public health, with a major in nutrition and health. She is a lecturer in the Food Service Business and Nutrition program at Ahmad Dahlan University. Her research interests include obesity and the development of food products to address obesity.

Andri Pranolo has completed his PhD in Computer Science and Technology from Hohai University, China. He is a lecturer in Informatics at Ahmad Dahlan University. His expertise lies in Intelligent Systems/Soft Computing and he has published numerous publications in reputable international journals.

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