Abstract
Research has established a positive association between food insecurity and obesity, typically assessed by body mass index (BMI); However, studies examining the relationship between food insecurity and measures of body fat content and distribution are lacking. The aim of this study was to examine the association between food insecurity and obesity ([BMI] ≥ 30 kg/m2) and body fat indicators assessed by body roundness index ([BRI] > 6.72) and a body shape index ([ABSI] > 0.08). This is a cross-sectional study using NHANES data 2007–2020. Household food security was assessed by U.S. Food Security Survey Module questionnaire. Multivariable-adjusted binary logistic regression analyses were used to calculate odds ratios and 95% CIs. Compared to those with full food security, the adjusted ORs for obesity were 1.28 (95% CI:1.18, 1.39), 1.40 (95% CI:1.28, 1.53), and 1.43 (95% CI:1.30, 1.57) for those with marginal, low and very low food security, respectively. The corresponding ORs for high BRI were, respectively, 1.39 (95% CI:1.26, 1.52), 1.50 (95% CI:1.36, 1.66), and 1.60 (95% CI:1.43, 1.78). Similar results were observed for ABSI. The analyses of BMI suggested a potential sex difference, as significant associations were found in women, but not in men. This study confirms previous evidence of the positive association between food insecurity and obesity among US adults.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-024-74108-x.
Keywords: Food security, Obesity, Body mass index, Body roundness index, A body shape index
Subject terms: Nutrition, Epidemiology
Introduction
Food security is a state that all people at all times have access to enough food for an active, healthy life1; In contrast, food insecurity is a condition of inadequate access to safe, healthy and nutritious food, which can endanger health and hinder normal growth and development2. According to the U.S. Department of Agriculture (USDA), 10.2% of US households experienced food insecurity in 20213. The most severe form of food insecurity is hunger, which results from a lack of food4.
Current evidence shows a parallel increase in food insecurity and obesity prevalence among Americans since 19994. Research suggests a positive association between food insecurity and obesity across the general U.S. population and most subgroups5. Food insecurity can lead to obesity by provoking excessive consumption of energy-dense, low-quality foods high in saturated fats and sugars such as ultra-processed foods6,7. There is evidence that federal food assistance programs designed to combat hunger may play a role in the food insecurity-obesity paradox4, as excessive consumption of energy-dense foods provided by these programs may induce weight gain7. Additionally, food insecurity is associated with lower dietary diversity, with an emphasis on high-calorie foods such as rice, oils, and fats rather than nutrient-dense options like eggs, vegetables, fruits, legumes, nuts, seeds, and dairy products8,9 and higher risk of obesity10.
Obesity, or excessive body fat, is an important modifiable risk factor for noncommunicable diseases11,12. Because of its simplicity and applicability, body mass index (BMI) is the most commonly used population-level anthropometric and adiposity measure in adults of both sexes in observational and clinical studies13. Previous studies have examined the association between food insecurity and obesity prevalence using BMI6. However, in some cases, BMI may not be able to distinguish between lean body mass and fat mass and has limited ability to indicate overall body fat distribution14. Research suggests that while increased body fat is associated with an increased risk of death, higher muscle mass may be associated with a lower risk15. In fact, a high proportion of visceral fat tissue in the body has been linked to metabolic disorders16. In addition, there is evidence that central obesity, even within the normal weight range, may be associated with a higher risk of metabolic abnormalities17. Therefore, it is suggested that novel measures of body fat distribution may correlate more strongly with the health risks associated with obesity and may be valuable in predicting chronic diseases and premature death18. The body roundness index (BRI)19 and a body shape index (ABSI)20 are the two new indices for estimating body fat content and body fat distribution, respectively. BRI and ABSI have been suggested to be superior to BMI in predicting the risks of cardiovascular disorders and premature death18,20–22.
Therefore, the aim of the current study was to examine the possible association between food insecurity and body fat levels as assessed by the two new adiposity measures in nationally representative data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2020. By incorporating waist circumference (WC) as opposed to BMI, BRI and ABSI provide more accurate proportions of fat, central adiposity, and human body composition, as well as associated health risks and mortality in the population, and offer promising alternatives to BMI21. By displaying a clearer picture of the risk factors of food insecure individuals living in a high-income country, more tailored public health guides, practices and programs can be adopted to manage health concerns associated with food insecurity and ultimately lead to better health outcomes in food insecure individuals.
Methods
Data source and study population
The present cross-sectional study was conducted based on a nationally representative data of the NHANES from 2007 to 2020, available for public use at https://wwwn.cdc.gov/nchs/nhanes. NHANES is a multi-stage, cluster-sampled survey on noninstitutionalized US populations that are released in 2-year cycles. Participants were asked to complete interviewer-administered questionnaires. Standardized physical examinations including anthropometric measurements were also conducted for the participants at a mobile examination center. NHANES protocols were designed in accordance with the U.S. Health and Human Services regulations for the protection of human subjects in research23. The survey protocols, procedures, questionnaires, consent documents, and participant materials were reviewed and approved by the Office of Management and Budget and the NCHS Ethics Review Board23. Moreover, written informed consents were obtained from all participants23. Since the data for physical activity status of the participants in the year cycles before 2007–2008 were not as consistent as the data from 2007 onwards, the time frame of the present study was limited to the years 2007–2020. Considering the aim of the current study, the study participants were restricted to adults aged 20 years and older. The current research was a complete-case study since cases without complete data on any of the study’s variables were removed from the study. Pregnant women were not included in the current study. In addition, participants with special considerations for weight, height or WC measurements (i.e., exceeding capacity, by clothing, using medical appliances, etc.) were not included in the analyses. The flow diagram of the inclusion and exclusion criteria for the study participants, along with the final sample size and losses, is shown in Fig. 1.
Fig. 1.
Flow diagram of inclusion and exclusion criteria of the study participants.
Variables
Food security
Household food security data were collected using U.S. Food Security Survey Module (US FSSM) questionnaire, which consists of an 18-item scale24. In the present study, the focus was on the responses of 10 items related to the adults; in brief the questions were about (1) to be worried run out of food, (2) food didn’t last, (3) couldn’t afford balanced meals, (4) cut size or skip meals, (5) how often cut size/skip meals, (6) eat less than should, (7) hungry, but didn’t eat, (8) lost weight, no money for food, (9) not eat whole day, (10) how often not eat for day (See Supplementary Data 1)25. Based on the number of affirmative responses for those questions, the adult food security was divided into 4 categories. Food security was categorized according to the USDA’s recommendations, which is based on the number of affirmative responses to the US FSSM. (See Supplementary Data 2). 0,1–2, 3–5 and 6–10 affirmative responses were, respectively, labeled as having full, marginal, low and very low food security26. In the present study, those who did not belong to the full food secure group, including marginal to very low food secure groups with 1 or more affirmative responses to the questions, were considered to be in a state of food insecurity27.
BMI, BRI and ABSI
Physical examination including measurement of weight, height and WC were conducted for all participants and were collected by trained health technicians. BMI was calculated by weight (kg)/squared height (m) and was classified as follows: underweight (< 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2) and obesity (≥ 30 kg/m2).
Obesity was defined as BMI ≥ 30 kg/m2 in this study.
BRI and ABSI were calculated as follows28:
 |
BRI and ABSI do not yet have a standardized operationalizations in the literature; thus, in this study we categorized them based on quartiles. The range of the BRI quartiles were as follows: [1.05, 3.83], (3.83, 5.12], (5.12, 6.72] and (6.72, 23.5], and range of the ABSI quartiles were as follows: [0.0576, 0.0783], (0.0783, 0.0816], (0.0816, 0.0848] and (0.0848, 0.111]. Then, the highest 25% of values for BRI and ABSI were considered as high BRI and ABSI.
Covariates
Information on sociodemographic characteristics including age, sex, race/ethnicity, educational level and family income to poverty ratio (IPR) were ascertained by standardized questionnaires available at the data repository link. Education levels were classified into two categories (up to high school, college or higher). The ethnicity/race were categorized as Hispanic, non-Hispanic White, non-Hispanic Black, and other (including multi-racial and Mexican-American). These categories are based on the evidence of higher prevalence of obesity and food insecurity in some subpopulations in the US29. Marital status was categorized into two groups (married/living with partner and not-married). The ratio of family income to poverty (IPR) was calculated based on instructions of the Department of Health and Human Services (HHS). In NHANES, the IPR ranges from 0 to 5. In this spectrum, 1 is equivalent to 100% of the federal poverty level. The index was then divided into three categories (i.e., ≤ 130%, 130% < IPR ≤ 185%, > 185%). Higher values of IPR represent a higher family income status30,31. Smoking status was defined by two questions including “smoked at least 100 cigarettes in life” and “if they smoke now”. Accordingly, participants were categorized as never smoker (never smoked or not smoked at least 100 cigarettes in life), former smoker (smoked at least 100 cigarettes in life but not smoke now) and current smoker (smoked at least 100 cigarettes in life and smoke now)32. Alcohol intake was evaluated based on the following question: “the number of alcoholic drinks per day over the past 12 months”. According to the CDC guidelines, one drink or less per day for women and two drinks or less per day for men are considered as moderate drinking. Those who consumed more than the aforementioned amounts were classified as heavy drinkers, while participants who did not drink at all were labeled as non-drinkers33.
The average energy intake of the participants was calculated by using the dietary data obtained from 24-hour dietary recalls for 2 days by interviewers. The physical activity questionnaire of the NHANES was based on the Global Physical Activity Questionnaire34. Based on NHANES guidelines, total metabolic equivalent (MET) score of physical activity for the participants was calculated by summing up the scores obtained from 5 categories of activities (vigorous recreational activities (MET-score = 8), vigorous occupational activities (MET-score = 8), moderate recreational activities (MET-score = 4), moderate occupational activities (MET-score = 4), walking / bicycling for transportation (MET-score = 4)) as follows:
Physical activity (MET-score) = sum of [days / week of each category of activities × the duration of each category of activities (minutes) × the related MET-score for each category of activities]
The final MET-scores were then classified into 4 groups as follows: inactive (MET min/week < 250), somewhat active (250 ≤ MET min/week < 500), active (500 ≤ MET < 1000) and very active (1000 ≤ MET)35.
Statistical analyses
Continuous and categorical variables indicating the general characteristics of the study participants were reported as mean ± standard deviation (SD) and number (percentage), respectively. To compare categorical and continuous variables across four categories of food security (full food security, marginal food security, low food security, very low food security), chi-square and analysis of variance (ANOVA) tests were, respectively, used. Multivariable binary logistic regression analyses were used to evaluate the association between different stages of food security and obesity, BRI > 6.72 and ABSI > 0.0848 as compared to those who had full food security. The point values of 0 and 1 were assigned to the lowest 75% and the highest 25% of BRI and ABSI values, respectively. Sex (male/female), age (year), alcohol consumption (non-drinker, moderate-drinker, heavy-drinker), smoking status (never smoker, former smoker, current smoker), educational levels (high school or less, college degree or more), race/ethnicity (non-Hispanic White, non-Hispanic Black, other Hispanic, others), marital status (married/living with partner, not-married), IPR (≤ 130%, 130% < IPR ≤ 185%, > 185%), physical activity status (inactive, moderately active, active, very active) and energy intake (continuous) were considered as potential confounders for inclusion in the analyses. Two models were tested in this study. There was no specific interaction in Model 1. Moreover, because there was evidence of sex differences in the association between food insecurity and obesity and between BRI and ABSI with cardiometabolic risks, we examined the possibility of sex effect modification in the association between different levels of food security and BMI, BRI, and ABSI in Model 220,21,36. Furthermore, in this research, age subgroup analysis was conducted in line with previously available studies examining food insecurity in different age groups using the NHANES database37,38. In the current study, age subgroups were divided into young adults (20–39 years), middle-aged adults (40–64 years), and old adults (65 + years). R software version 4.1.1 was used for all statistical analyses. Statistical significance was defined as P-value < 0.05.
Results
Demographic characteristics
A total of 23,692 participants aged 20 years or older (47% women) with complete data were eligible and included for the analyses. Table 1 presents the demographic characteristics and anthropometric indexes of the participants stratified by food security categories. The prevalence of full, marginal, low and very low food security among the US adults in the present study was 68.0%, 12.3%, 10.7%, and 9.0%, respectively. Age, race/ethnicity, education level, marital status, IPR, smoking status and alcohol intake were significantly associated with food security. Those participants who belonged to the very low food security category tended to be youngest (mean ± SD: 44.49 ± 15.32 years), having highest energy intake (2139.54 ± 1040.72 kcals) and highest WC (101.67 ± 18.04 cm) and BMI values (30.25 ± 7.75 kg/m2) in comparison to other categories of food security. Participants in the very low food security group had a higher prevalence of non-Hispanic White individuals. The association between different stages of food security and odds of having obesity is indicated in Table 2. As compared to those who had full food security, participants who had marginal, low and very low food security had a 28% (95%CI: 1.18, 1.39), 40% (95%CI: 1.28, 1.53), and 43% (95%CI: 1.30, 1.57) higher odds of having obesity, respectively (model 1). Additional analyses suggested a potential effect modification by sex (p < 0.001), where we found a significant positive association in women, but no association was found in men (model 2).
Table 1.
Characteristics of the study participants stratified by food security status.
| Characteristic | Full food security (n = 16110, 68.0%) |
Marginal food security (n = 2919, 12.3%) |
Low food security (n = 2541, 10.7%) |
Very low food security (n = 2122, 9.0%) |
P-value* |
|---|---|---|---|---|---|
| Age (years) | 50.58 (17.33) | 45.01 (16.23) | 45.28 (15.86) | 44.49 (15.32) | < 0.001 |
| Energy intake (kcal/day) | 2097.13 (827.69) | 2119.03 (920.17) | 2096.13 (921.81) | 2139.54 (1040.72) | 0.13 |
| Weight (kg) | 82.47 (20.94) | 83.52 (21.59) | 84.43 (22.17) | 85.57 (23.22) | < 0.001 |
| Height (cm) | 168.61 (9.83) | 167.26 (9.90) | 167.10 (9.52) | 168.12 (9.87) | < 0.001 |
| BMI (kg/m2) | 28.91 (6.54) | 29.80 (7.08) | 30.23 (7.47) | 30.25 (7.75) | < 0.001 |
| WC (cm) | 99.34 (16.11) | 100.47 (16.86) | 101.29 (17.30) | 101.67 (18.04) | < 0.001 |
| BRI (unit) | 5.38 (2.24) | 5.68 (2.46) | 5.82 (2.55) | 5.80 (2.64) | < 0.001 |
| ABSI (unit)) | 0.0817 (0.00481) | 0.0813 (0.00476) | 0.0813 (0.00472) | 0.0814 (0.00493) | < 0.001 |
| Sex | |||||
| Male | 8606 (68.7) | 1492 (11.9) | 1317 (10.5) | 1117 (8.9) | 0.08 |
| Female | 7504 (67.2) | 1496 (12.8) | 1224 (11.0) | 1005 (9.0) | |
| Age categories (years) | |||||
| 20–29 | 2369 (59.4) | 651 (16.3) | 529 (13.3) | 436 (10.9) | < 0.001 |
| 30–39 | 2558 (62.7) | 571 (14.0) | 501 (12.3) | 447 (11.0) | |
| 40–49 | 2753 (66.1) | 535 (12.8) | 458 (11.0) | 418 (10.0) | |
| 50–64 | 4407 (68.0) | 761 (11.7) | 727 (11.2) | 583 (9.0) | |
| >=65 | 4023 (80.7) | 401 (8.0) | 326 (6.5) | 238 (4.8) | |
| Marital status | |||||
| Married/Living with partner | 10,262 (63.7) | 1608 (55.1) | 1295 (51.0) | 972 (45.8) | < 0.001 |
| Not married | 5848 (36.3) | 1311 (44.9) | 1246 (49.0) | 1150 (54.2) | |
| Alcohol intake | |||||
| Non-drinker | 3072 (63.8) | 638 (13.3) | 598 (12.4) | 504 (10.5) | < 0.001 |
| Moderate-drinker | 12,786 (69.4) | 2211 (12.0) | 1882 (10.2) | 1547 (8.4) | |
| Heavy-drinker | 252 (55.5) | 70 (15.4) | 61 (13.4) | 71 (15.6) | |
| Smoking Status | |||||
| Never smoker | 8704 (72.5) | 1368 (11.4) | 1152 (9.6) | 781 (6.5) | < 0.001 |
| Current smoker | 2768 (51.2) | 870 (16.1) | 837 (15.5) | 927 (17.2) | |
| Former smoker | 4638 (73.8) | 681 (10.8) | 552 (8.8) | 414 (6.6) | |
| Education level | |||||
| High-school or less | 5785 (56.2) | 1621 (15.8) | 1618 (15.7) | 1267 (12.3) | < 0.001 |
| College degree or more | 10,325 (77.0) | 1298 (9.7) | 923 (6.9) | 855 (6.4) | |
| Race/Ethnicity | |||||
| Non-Hispanic white | 8056 (76.3) | 896 (8.5) | 786 (7.4) | 827 (7.8) | < 0.001 |
| Non-Hispanic black | 3113 (60.6) | 795 (15.5) | 652 (12.7) | 577 (11.2) | |
| Other Hispanic | 1374 (58.6) | 364 (15.5) | 360 (15.4) | 245 (10.5) | |
| Other | 3567 (63.2) | 864 (15.3) | 743 (13.2) | 473 (8.4) | |
| IPR categories | |||||
| <=130% | 2909 (41.5) | 1308 (18.7) | 1428 (20.4) | 1358 (19.4) | < 0.001 |
| 130%< IPR < = 185% | 1745 (56.1) | 544 (17.5) | 468 (15.1) | 351 (11.3) | |
| > 185% | 11,456 (84.4) | 1067 (7.9) | 645 (4.7) | 413 (3.0) | |
| Physical activity status | |||||
| Inactive | 4532 (66.2) | 881 (12.9) | 805 (11.8) | 629 (9.2) | 0.13 |
| Somewhat active | 1022 (70.4) | 167 (11.5) | 157 (10.8) | 106 (7.3) | |
| Active | 1697 (73.7) | 253 (11.0) | 215 (9.3) | 139 (6.0) | |
| Very active | 8859 (67.7) | 1618 (12.4) | 1364 (10.4) | 1248 (9.5) | |
| BMI categories | |||||
| Underweight | 202 (61.6) | 44 (13.4) | 51 (15.5) | 31 (9.5) | < 0.001 |
| Normal weight | 4489 (71.0) | 709 (11.2) | 574 (9.1) | 550 (8.7) | |
| Overweight | 5515 (70.6) | 928 (11.9) | 780 (10.0) | 588 (7.5) | |
| Obese | 5904 (64.0) | 1238 (13.4) | 1136 (12.3) | 953 (10.3) | |
| BRI categories | |||||
| Q1= [1.05,3.83] | 4153 (70.1) | 682 (11.5) | 574 (9.7) | 514 (8.7) | < 0.001 |
| Q2= (3.83,5.12] | 4215 (71.2) | 695 (11.7) | 553 (9.3) | 460 (7.8) | |
| Q3= (5.12,6.72] | 4078 (68.9) | 719 (12.1) | 640 (10.8) | 486 (8.2) | |
| Q4= (6.72,23.5] | 3664 (61.9) | 823 (13.9) | 774 (13.1) | 662 (11.2) | |
| ABSI categories | |||||
| Q1=[0.0576,0.0783] | 3919 (66.2) | 785 (13.3) | 667 (11.3) | 552 (9.3) | 0.01 |
| Q2=(0.0783,0.0816] | 4007 (67.7) | 731 (12.3) | 664 (11.2) | 521 (8.8) | |
| Q3=(0.0816,0.0848] | 4070 (68.7) | 726 (12.3) | 609 (10.3) | 518 (8.7) | |
| Q4=(0.0848,0.111] | 4114 (69.5) | 677 (11.4) | 601 (10.1) | 531 (9.0) | |
Data are presented as mean ± SD continuous variables or frequency (n (%)) for categorical variables.
*Obtained by chi-square test for categorical variables and ANOVA test for continuous variables.
Abbreviations: BMI: body mass index; BRI: body roundness index; ABSI: body shape index; IPR: income to poverty ratio.
Table 2.
The association between different stages of food security and odds of having obesity in the study participants.
| Characteristics | Adjusted* OR | 95% CI | P-value | |
|---|---|---|---|---|
| Lower | Upper | |||
| Model 1 | ||||
| Full food security | reference | |||
| Marginal food security | 1.28 | 1.18 | 1.39 | < 0.001 |
| Low food security | 1.40 | 1.28 | 1.53 | < 0.001 |
| Very low food security | 1.43 | 1.30 | 1.57 | < 0.001 |
| Model 2: with interaction effect between sex and different stages of food security | ||||
| Males*marginal food security | 1.07 | 0.83 | 1.38 | 0.12 |
| Males*low food security | 1.04 | 0.79 | 1.36 | 0.56 |
| Males*very low food security | 1.14 | 0.85 | 1.54 | 0.27 |
| Females*marginal food security | 1.54 | 1.37 | 1.73 | < 0.001 |
| Females*low food security | 1.91 | 1.69 | 2.16 | < 0.001 |
| Females*very low food security | 1.81 | 1.57 | 2.07 | < 0.001 |
OR: odds ratio; CI: confidence interval.
*Models adjusted for age, energy intake, smoking status, alcohol intake, education level, race/ethnicity, IPR, physical activity and marital status.
BMI categories: non-obese (< 29.9 kg/m2) and obesity (≥ 30 kg/m2).
Tables 3 and 4 present the association between food security categories and odds of having high BRI and ABSI values, respectively. Marginal, low and very low food security were, respectively, associated with a 39%, 50%, and 60% higher odds of high BRI in the main analysis incorporating all participants (model 1). Sex-specific analyses indicated similar positive associations in women. In men, only very low food security was associated with 42% higher odds of having high BRI (model 2). For ABSI, marginal, low and very low food security were associated with a 16%, 13%, and 27% higher odds of high ABSI, respectively (model 1). The results were similar to the main analyses in women. In men, marginal and low food security were not associated with the odds of high ABSI, but very low food security was associated with 20% higher odds of having high ABSI (model 2).
Table 3.
The association between different stages of food security and odds of having high values of body roundness index in the study participants.
| Characteristics | Adjusted* OR |
95% CI | P-value | |
|---|---|---|---|---|
| Lower | Upper | |||
| Model 1 | ||||
| Full food security | reference | |||
| Marginal food security | 1.39 | 1.26 | 1.52 | < 0.001 |
| Low food security | 1.50 | 1.36 | 1.66 | < 0.001 |
| Very low food security | 1.60 | 1.43 | 1.78 | < 0.001 |
| Model 2: with interaction effect between sex and different stages of food security | ||||
| Males*marginal food security | 1.09 | 0.83 | 1.45 | 0.12 |
| Males*low food security | 1.19 | 0.99 | 1.43 | 0.08 |
| Males*very low food security | 1.42 | 1.17 | 1.74 | < 0.001 |
| Females*marginal food security | 1.69 | 1.50 | 1.92 | < 0.001 |
| Females*low food security | 1.85 | 1.62 | 2.11 | < 0.001 |
| Females*very low food security | 1.82 | 1.57 | 2.10 | 0.01 |
OR: odds ratio; CI: confidence interval.
*Models adjusted for age, energy intake, smoking status, alcohol intake, education level, race/ethnicity, IPR, physical activity and marital status.
BRI Categories: [1.05, 6.72] and (6.72, 23.5].
Table 4.
The association between different stages of food security and odds of having high values of a body shape index in the study participants.
| Characteristics | Adjusted* OR |
95% CI | p-value | |
|---|---|---|---|---|
| Lower | Upper | |||
| Model 1 | ||||
| Full food security | reference | |||
| Marginal food security | 1.16 | 1.04 | 1.30 | 0.01 |
| Low food security | 1.13 | 0.01 | 1.27 | 0.04 |
| Very low food security | 1.27 | 1.12 | 1.44 | < 0.001 |
| Model 2: with interaction effect between sex and different stages of food security | ||||
| Males*marginal food security | 1.01 | 0.87 | 1.16 | 0.94 |
| Males*low food security | 1.02 | 0.87 | 1.18 | 0.84 |
| Males*very low food security | 1.20 | 1.02 | 1.40 | 0.03 |
| Females*marginal food security | 1.39 | 1.15 | 1.67 | 0.002 |
| Females*low food security | 1.29 | 1.06 | 1.56 | 0.03 |
| Females*very low food security | 1.38 | 1.14 | 1.68 | 0.02 |
OR: odds ratio; CI: confidence interval.
*Models adjusted for age, energy intake, smoking status, alcohol intake, education level, race/ethnicity, IPR, physical activity and marital status.
ABSI categories: [0.0576, 0.0848] and (0.0848, 0.111].
Table 5 shows the association between food security stages and odds of having obesity, high BRI and ABSI values in different age subgroups. For BMI and BRI, the significant positive association between different stages of food security persisted in all age subgroups. Nevertheless, the concurrent increase in odds of obesity with increased severity of food insecurity was not observed in middle-aged adults. On the other hand, the increased odds of having high BRI in young and middle-aged adults were fairly in proportion to increased intensity of food insecurity. Among older adults, the odds of high BRI were elevated in all categories of food insecurity; However, the elevated odds of high BRI decreased from 1.73 in marginal food security to 1.54 in low and 1.48 in very low food security stages. No association was found between food security and ABSI in participants older than 65 years. In addition, the odds of having high ABSI raised in accordance to the severity of food insecurity from 1.24 in marginal to 1.43 in low and 1.55 in very low food security stage in middle-aged adults.
Table 5.
The association between food security categories and odds of having obesity, having a high body roundness index and high a body shape index in age subgroups of study participants.
| Parameter | Adjusted* OR |
BMI | P-value | Adjusted* OR |
BRI | P-value | Adjusted* OR |
ABSI | P-value | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Characteristics | 95% CI | 95% CI | 95% CI | |||||||||
| Lower | Upper | Lower | Upper | Lower | Upper | |||||||
| Stages of Food Security in different Age Subgroups | ||||||||||||
| 20 < = Age < = 39 | ||||||||||||
| Full | reference | |||||||||||
| Marginal | 1.25 | 1.10 | 1.42 | < 0.001 | 1.21 | 1.03 | 1.42 | 0.02 | 1.29 | 1.02 | 1.62 | 0.03 |
| Low | 1.51 | 1.32 | 1.73 | < 0.001 | 1.51 | 1.28 | 1.78 | < 0.001 | 1.14 | 0.87 | 1.46 | 0.33 |
| Very low | 1.54 | 1.33 | 1.78 | < 0.001 | 1.75 | 1.48 | 2.08 | < 0.001 | 1.32 | 1.01 | 1.71 | 0.03 |
| 40 < = Age < = 64 | ||||||||||||
| Marginal | 1.32 | 1.17 | 1.49 | < 0.001 | 1.56 | 1.38 | 1.78 | < 0.001 | 1.24 | 1.08 | 1.42 | 0.002 |
| Low | 1.34 | 1.19 | 1.52 | < 0.001 | 1.73 | 1.52 | 1.97 | < 0.001 | 1.43 | 1.25 | 1.64 | < 0.001 |
| Very low | 1.32 | 1.16 | 1.51 | < 0.001 | 1.71 | 1.49 | 1.97 | < 0.001 | 1.55 | 1.34 | 1.80 | < 0.001 |
| Age > = 65 | ||||||||||||
| Marginal | 1.34 | 1.08 | 1.65 | < 0.001 | 1.73 | 1.40 | 2.14 | < 0.001 | 1.15 | 0.93 | 1.41 | 0.20 |
| Low | 1.38 | 1.10 | 1.73 | < 0.001 | 1.54 | 1.22 | 1.94 | < 0.001 | 0.99 | 0.79 | 1.24 | 0.91 |
| Very low | 1.47 | 1.30 | 1.92 | < 0.001 | 1.48 | 1.12 | 1.94 | 0.005 | 1.28 | 0.98 | 1.68 | 0.07 |
| BMI categories: non-obese (≤ 29.9 kg/m2) and obesity (≥ 30 kg/m2) | BRI Categories: [1.05, 6.72] and (6.72, 23.5] | ABSI categories: [0.0576, 0.0848] and (0.0848, 0.111] | ||||||||||
OR: odds ratio; CI: confidence interval.
*Adjusted for age, energy intake, smoking status, alcohol intake, education level, race/ethnicity, IPR, physical activity and marital status.
Discussion
Using data from a nationally representative sample of US adults, a cross-sectional study was conducted to examine the relationship between food insecurity and the odds of obesity as well as high BRI and ABSI values. The prevalence of marginal, low and very low food security among US adults was 12.3%, 10.7%, and 9.0%, respectively. A positive association between food insecurity and obesity, has been found where odds of having obesity increased proportionally with increasing severity of food insecurity (from marginal to very low) particularly in young and older adults. A similar pattern was found for high BRI and ABSI, where odds of having high BRI and ABSI increased with increasing severity of food insecurity. The BMI analysis pointed to a strong possible sex difference in the association, showing that food insecurity was linked to increased odds of obesity in women, but not in men.
The results we obtained about the association between food insecurity and obesity aligned with the findings of another research carried out, worldwide. A cross-sectional study of 2551 UK adults in 2020 indicated that participants who had food insecurity had a 33% higher odds of overweight compared to those who had food security39. Other cross-sectional studies in Canada40, Netherlands41 and Iran42 also suggested a positive association between food insecurity and overweight or obesity. A recent meta-analysis of cross-sectional study indicated a positive association between food insecurity and obesity in children and adults43. With regard to the US population, studies conducted among old children44, adolescents45, and young adults36 suggested a positive association between food insecurity and odds of obesity in the US. However, compared to previous research in the US, this study is the largest conducted using data from a representative sample of US adults. The largest previous study conducted in the US was a cross-sectional study on 7741 adults in the Adult California Health Interview Survey, indicating that the prevalence of obesity was 30% higher among Supplemental Nutrition Assistance Program participants than in non-participants and it was independent of food insecurity or socio-economic status46. They stated that people who receive nutrition assistance programs tend to drink more soda and use their monthly benefits to buy cheaper, high-calorie foods rather than improving their eating habits46.
The BMI analyses in the current study revealed that there may be a sex difference, as we observed a positive association between food insecurity and obesity in women, but not in men.
A previous study of 4,667 US participants in the NHANES 2011–2016 who were young adults (18–35 years old) found a similar sex difference, with a positive association between food insecurity and obesity in women but not in men36. The observed sex difference could be explained by the fact that gender norms place women in the private sphere of the household; women are typically responsible for selecting and preparing food for the family, making them more aware and capable of dealing with food shortages at home47,48. Women in food-insecure households often resort to risky mechanisms to ensure their children are well-nourished. These strategies result in women consuming fewer dairy products, fruits and vegetables, grains, meats, and protein while eating more carbohydrates. As a result, these women receive lower dietary quality scores compared to their children48,49. The consumption of diets low in nutrients but high in carbohydrates, such as breads and cereals, sugar and sugar-sweetened beverages among mothers from food-insecure households may have contributed to an increased risk of being overweight50. In contrast, traditional gender roles envision men as the breadwinners of the family, so they must be tough and emotionally stable and are less likely to use coping strategies to feed their family48. Furthermore, food insecurity is associated with lower dietary diversity9, which in turn may be associated with greater risk of obesity10. Research suggests that the association between food insecurity and some adverse health outcomes, such as depression, may be partly driven by dietary diversity51. Furthermore, obesity could be caused by the stress associated with food insecurity52, as women are more likely than men to eat more when stressed53,54, particularly foods high in fat and sweets55. However, the Adult California Health Interview Survey found the opposite results; the association between food insecurity and obesity was stronger in men than in women46. Nevertheless, the potential sex difference in the association between food insecurity and obesity, as well as possible underlying mechanisms, should be further investigated in future research56.
On the other hand, we investigated the association between food insecurity and high BRI and ABSI in the US adults. Previous research has mainly focused on the association between food insecurity and traditional measures of overall and abdominal obesity such as WC and BMI43. A previous longitudinal evaluation from the Pacific Islands Families Study of 1376 Pacific Island mothers found that boys born into food insecure households had more visceral fat at age 14 than boys born into food secure households57. However, we are not aware of any previous study on the association between food insecurity and BRI and ABSI. In some situations, BMI may not be appropriate to account for the percentages of total and visceral fat. BRI is a novel alternative adiposity measure that is intended to consider this limitation19. It is suggested that BRI may be slightly superior to BMI and other traditional obesity measures such as waist or hip circumferences in predicting total and visceral fat mass19. ABSI is also a novel indicator of body fat distribution which integrates WC, BMI and height to compute a more appropriate indicator of body fat distribution20. Studies have indicated that both ABSI and BRI have a good ability in predicting the risk of multiple unfavorable health outcomes18,20–22,58–60.
There are some potential mechanisms to explain the positive association between food insecurity and obesity61. Evidence from studies of food-insecure populations in the US suggests that food insecurity can cause reductions in energy expenditure and increases in energy intake through various mechanisms; both are associated with weight gain61. Food insecurity can cause depression and sleep disturbances, fatigue and reduced psychomotor readiness for physical activity, resulting in food-insecure adults being less active and thereby reducing energy expenditure62,63. Furthermore, prolonged periods without food intake and unpredictable eating patterns during food insecurity may reduce dietary thermogenesis and energy expenditure, particularly in women64. Alternatively, stress over access to enough food during food insecurity causes increases in cortisol levels and appetite for fats and sugars65. Food-insecure adults are less likely to consume fiber and protein sources and are more likely to consume cheaper foods, including refined carbohydrates such as shelf-stable meals, sugar-sweetened beverages and ultra-processed foods leading to higher energy intake7,66. Additionally, because it is adaptive to have larger fat stores as a safety net against energy shortages, people who experience food insecurity tend to gain weight61. Some other mechanisms such as lack of access to healthy foods and access to healthcare, low physical activity level and marital status may partially explain the association between food insecurity and obesity6,56,61.
We also examined the association between various stages of food security and obesity, high BRI, and high ABSI across different age subgroups. The results of the present study further showed that even marginal food security significantly elevated odds of obesity in all age subgroups including young, middle-aged and older adults. Contrary to the results of the present study, a study of 18–35 years young US adults in the NHANES 2011–2016, showed a positive significant association between food insecurity and BMI in female young adults only36. In another 2011–2012 NHANES study of older adults aged ≥ 60 years, food insecurity was inversely associated with overweight or obesity in men only67. We are not aware of any study that examines the association between food insecurity and obesity in different age subgroups of adults. The difference in the results of the different studies examining the association between food insecurity and BMI in NHANES databases, could be due to the variety of year-cycles included for each study, differences in the categories considered food insecure, and different ranges of age in each adult subgroup. Regarding food insecurity and ABSI and BRI in different age groups, we could not identify any relevant studies to discuss.
Strengths and limitations of the study
The present study is the largest study to investigate the association between food insecurity and obesity in the US adults. We used reliable data from a nationally representative sample of the US adults, and used valid and reliable methods to gather information on food insecurity and adiposity. We also used novel alternative measures of total and visceral fat content (BRI) and body fat distribution (ABSI) that have been addressed in the previous research. We also considered some potential confounders such as education status, alcohol drinking and smoking status in the analyses that have not been included in the previous research that used NHANES data43.
The main limitation of the study is its cross-sectional design that limits determining the temporal sequence of food insecurity and obesity. Thus, prospective cohort studies are needed to present more confident results. In addition, NHANES used self-reported data to assess food insecurity which, in turn, may lead to misclassification of food insecurity in the study participants68; however, some biases are reduced by having validated data collection instruments. Although NHANES uses various types of quality control monitoring procedures to ensure the collection of high-quality data during the survey, biases including response bias and interviewer bias may occur in confounding variables, which were mostly collected through questionnaires. Despite adjusting several confounding variables in this study, they may potentially generate residual confusion. Moreover, residual confusion that might be caused by the variable IPR, can’t be ruled out in our study. In addition, some potential confounding factors such as the number of children were largely missing from the dataset and were therefore not taken into account in the analyses.
Conclusion
To conclude, this large-scale cross-sectional study on US adults indicated that food insecurity in the US adults is associated with a higher odds of having obesity by BMI, as well as high BRI and ABSI. Age-stratified analyses showed persistent positive associations between different stages of food security and high BMI and BRI. We found a potential sex difference in the association between food insecurity and obesity, high BRI and ABSI, though, it was stronger for BMI. Following previous research showing a positive link between food insecurity and general obesity as assessed by BMI, the findings of the present study suggest that food insecurity might be associated with a higher odds of central obesity and high levels of visceral fat content. These findings need to be confirmed in the future research.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Author contributions
Conceptualization was done by PA and MR. Data were cured by MR. MR, PA, FG and MSY designed the methodology. FG performed statistical analysis. LA, MSY and AJ validated the study. LA supervised the study. The original draft was written by MR and AJ. AJ and LA reviewed and edited the manuscript.
Data availability
The datasets generated and/or analyzed during the current study are available in the NHANES repository: https://www.cdc.gov/nchs/nhanes/.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Mir Saeed Yekaninejad, Email: yekaninejad@yahoo.com.
Leila Azadbakht, Email: Azadbakhtleila@gmail.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during the current study are available in the NHANES repository: https://www.cdc.gov/nchs/nhanes/.