Abstract
Introduction
Food insecurity (FI) and obesity are associated with cardiovascular risk markers such as hypertension, yet it remains unclear whether their coexistence increases hypertension risk beyond their independent contributions. We hypothesized that women experiencing both FI and OWT/OB (‘coexisting burden’) would have higher odds of hypertension, compared to those experiencing only OWT/OB, only FI, or neither. We aimed to examine the association of FI and overweight/obesity (OWT/OB) with hypertension and systolic blood pressure (SBP) among Guatemalan women of reproductive age (15–49 years).
Materials and methods
Data came from 1,538 women in Guatemala’s nationally representative 2018–2019 Health and Nutrition Epidemiological Surveillance System Survey (SIVESNU). A four-category exposure combined binary FI (assessed using the 8-item Food Insecurity Experience Scale) and OWT/OB status. SBP was measured and hypertension categorized using standard criteria. Multinomial logistic and linear regressions assessed associations with hypertension status and SBP, adjusting for covariates.
Results
Participants had a mean age of 30 years; 76% experienced FI, 60% OWT/OB, 44% had both (‘coexisting burden’), and 32% had hypertension. Contrary to our hypothesis, associations were stronger among women with only OWT/OB (OR = 2.69, 95% CI: 1.44–5.02) than among those with coexisting burden (OR = 1.83, 95% CI: 1.01–3.31), compared to women with neither condition. No associations were found with pre-hypertension.
Conclusion
Overall, OWT/OB, with or without FI, was associated with higher odds of hypertension, underscoring the need for community- and person-level interventions supporting healthier body weight.
Keywords: food insecurity, Guatemala, hypertension, Latin America, multinomial logistic regression, obesity, women
1. Introduction
Within the United Nations’ Sustainable Developments Goal of Zero Hunger, specific targets aim to end hunger, achieve food security and end all forms of malnutrition by 2030 (1). Food insecurity (FI) is defined as “lacking physical and economic access to a sufficient and nutritious diet” (2, 3). As of 2023, an estimated 29% of the global population (2.33 billion) were experiencing moderate or severe FI, with a similar prevalence (28%) reported in Latin America and the Caribbean (1). Notably, FI has consistently been more prevalent among women than men, both globally and across all regions (1). At the same time, obesity rates have been rising at an alarming pace over the last decade, particularly in low- and middle-income countries where undernutrition and micronutrient deficiencies remain highly prevalent (1). Latin America and the Caribbean have the highest obesity prevalence, affecting nearly 30% of the adult population (1).
Obesity, a form of malnutrition, may result from excessive caloric intake due to inexpensive and energy-dense foods, as well as stress-induced metabolic changes (4–7). Its coexistence with FI has historically been described as paradoxical (the FI-obesity paradox), since FI was traditionally associated with undernutrition (8). While coping behaviors in response to FI—such as emotional eating (9) or irregular meal patterns (10, 11)—may influence body weight, current evidence suggests that, rather than reflecting a direct causal relationship, FI and obesity are more likely consequences of shared social disadvantages (7, 8). A complex interplay of social, economic, and psychological factors therefore underlies both FI and malnutrition (including obesity) (5, 8, 11–14). The short- and long-term health impacts of this interplay are not yet fully understood, highlighting the need for further investigation (7, 8, 14, 15). For example, while the separate influence of FI (16, 17) and obesity (18, 19) on cardiovascular risk markers such as hypertension are well-documented (20, 21), it is unclear whether their combined impact exacerbates hypertension risk beyond their independent contributions, particularly when measured clinically (16).
Despite growing evidence that FI and obesity go hand in hand, research on this coexisting burden and its implications for population health remains scarce in Latin American countries (19, 22). In Guatemala, a low-middle-income country in Central America, half of the population experiences food insecurity, malnutrition and poverty (23, 24). These conditions contribute to the growing burden of noncommunicable diseases (NCDs), the leading cause of death in the country (24). Recent reports have shown that nearly one-third of the population are affected by hypertension (24). Moreover, undernutrition (25), obesity (26), anemia (27) and hypertension (28) are highly prevalent among Guatemalan women of reproductive age (15–49 years old) (WRA), with reported socioeconomic and ethnic disparities in all forms of malnutrition (25–27). Nutritional inequalities among WRA increase their risk of impaired fertility, maternal health complications, chronic diseases, and ultimately higher mortality (29, 30). One study suggested that WRA with both FI and obesity faced an increased risk of excessive weight gain during pregnancy and postpartum (22). However, the impact of the coexisting burden of FI and obesity on other health outcomes among WRA in Latin America remains unexplored. Understanding the implications of this coexisting burden on cardiovascular disease risk among vulnerable populations like WRA has the potential to inform targeted interventions.
In this study, we aimed to estimate the joint association of FI and overweight/obesity (OWT/OB) with hypertension among Guatemalan WRA (15–49 years old) using nationally representative data. We hypothesized that women experiencing both FI and OWT/OB (‘coexisting burden’) would have higher odds of hypertension, compared to those experiencing only OWT/OB, only FI, or neither.
2. Materials and methods
2.1. Study design and population
This cross-sectional study aimed to estimate the joint association of FI and overweight/obesity (OWT/OB) with hypertension among Guatemalan WRA. We utilized data from WRA who participated in Guatemala’s 2018–2019 Health and Nutrition Epidemiological Surveillance System Survey (SIVESNU). SIVESNU is a semi-annual nationally representative survey designed to monitor health and nutrition in Guatemala (31). As part of the original SIVESNU sampling framework—designed to support surveillance objectives across women and young children—households were eligible for recruitment if they had at least one WRA (15–49 years) and one child under 5 years of age. SIVESNU adheres to a multistage sampling design to ensure national representativeness of eligible households. Details about the survey design are provided elsewhere (31). The 2018–2019 cycle is the most recent publicly available dataset as of writing.
2.1.1. Analytic sample
For the present study, our analytic sample was restricted to non-pregnant women with complete information on exposure, outcome, and covariates. Out of 1755 WRA who participated in the 2018–2019 SIVESNU cycle, we excluded pregnant women (N = 95) and those with missing pregnancy status (n = 4), leaving 1,656 women eligible for analysis. We further excluded 118 participants with missing data on key variables (exposure, outcome and/or covariates), leaving 1,538 women in the analytic sample (93% of the eligible sample, see Supplementary Figure 1). Compared to those excluded, women included in the analysis were more physically active, had higher socioeconomic position (SEP), and had fewer children at home. However, included and excluded participants were similar in terms of hypertension status, urbanicity, and education level (see Supplementary Table 1).
2.2. Exposure: FI and body mass index
FI was assessed using the Food Insecurity Experience Scale (FIES) (32), a shortened scale largely based on the Latin American and Caribbean Food Security Scale (ELCSA) (33, 34). The FIES is an 8-item experience-based tool, with cross-cultural comparability across countries and population subgroups (35), and was validated by the Food and Agricultural Organization of the United Nations (32, 36). In SIVESNU, the FIES was administered at the household level using a 12-month recall period, with responses provided by the head of household. Response options for each item were ‘no’ (0) or ‘yes’ (1). We summed the 8 items and then classified the scale according to Cafiero et al. (36) as: food secure (score = 0), mild FI (score = 1–3), moderate FI (score = 4–6), severe FI (score = 7–8). See Supplementary Table 2 for the FIES questions.
Trained professionals measured each woman’s standing height using a portable stadiometer (ShorrBoard, Olney, Maryland) and weight using a digital floor scale (Seca 874 Digital Floor Scale, Olney, Maryland). Body mass index (weight in kg/height in meters squared, BMI) was categorized as less than overweight (<25.0 kg/m2), overweight (OWT, 25.0- < 30 kg/m2) or obese (OB ≥ 30.0 kg/m2).
We then constructed a variable that cross-classified FI and BMI categories to evaluate whether the combined exposure was associated with greater hypertension risk beyond the independent contribution of each condition. To obtain adequate sample sizes across cross-classified exposure and outcome categories, we dichotomized FI and BMI status. We defined FI as having at least mild FI (score > = 1) and high BMI status as being overweight or obese (BMI > =25.0 kg/m2). The final 4-category exposure variable was (1) not FI and not OWT/OB, (2) only FI, (3) only OWT/OB, (4) FI and OWT/OB (‘coexisting burden’).
2.3. Outcome: blood pressure
Participants provided information on hypertension diagnosis and use of antihypertensive medications. Trained clinicians measured blood pressure (BP) using MDF Lenus Digital Blood Pressure Monitor (MDF Instruments, Los Angeles, California) with an arm cuff placed on the left arm after the participant sat for at least 5 min. Three BP measurements were taken at one-min intervals, and the second and third measurements were averaged (31). BP was classified according to the 2017 American College of Cardiology/ American Heart Association (ACC/AHA) guidelines (37). Pre-hypertension was defined as SBP of 120–129 mm Hg or diastolic BP < 80 mm Hg. Hypertension was defined as SBP ≥ 130 mm Hg, diastolic BP ≥ 80 mm Hg, or currently taking antihypertensive medications. If blood pressure was unmeasured but the response was affirmative to antihypertensive medications, then we classified it as hypertension; otherwise, it was considered missing and excluded from the analysis.
2.4. Confounders
Confounders were selected a priori based on literature review and directed acyclic graphs (DAGs). Age, lactating status, and physical activity were included as confounders because they are known to be related to FI and obesity status (9, 11, 12, 22, 38), as well as with hypertension risk (16, 18, 19, 39–41), due to their influence on nutritional demands, metabolic changes, and chronic stress. These variables were not considered mediators in the causal pathway of interest.
We categorized age into four groups: 15–19 years, 20–29 years, 30–39 years, 40–49 years. Lactating status was included as a binary variable (lactating vs. not lactating). Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ) (42). Participants were asked separately about the frequency and duration of moderate and vigorous physical activity (MVPA) across work, sports, transportation (walking/cycling) and recreational domains. We calculated total MVPA (minutes per week) using standard GPAQ methods: (43) (MVPA = moderate activity minutes per week + [2 × vigorous intensity minutes per week]). We then categorized MVPA into tertiles (low, middle, and high). For linear regression models with SBP as the outcome, we additionally included ‘use of anti-hypertensive medication’ as a confounder as it directly suppresses blood pressure and could influence body weight.
Household-level confounders included number of adults, number of children, urbanicity, and socioeconomic position (SEP), because they are known to influence FI and dietary patterns (thus nutritional status/obesity) (9, 11, 12, 22, 38) and are also positively associated with hypertension through pathways related to stress (16, 18, 19, 39–41). We included separate variables for household number of adults and children, as they may contribute differently to household resource allocation and dietary patterns. We also included a binary measure of urbanicity determined based on the Guatemala’s National Institute of Statistics definition of urban areas as “cities, towns, villages, or populated places that have the category of a neighborhood or condominium, and those with more than 2,000 inhabitants, provided that 51% or more of the households have electric lighting and piped water (tap) within their residences (homes)” (44). Since household income information was not collected in SIVESNU, we derived an SEP index using 12 variables related to socioeconomic status including assets ownership (television, refrigerator, washing machine, landline phone, computer, microwave, vehicle), healthcare access (insurance, ever seen a dentist), home status (ownership vs. rental), number of children living in the household, and home environment (separate sleeping and cooking spaces) (28, 45). See Supplementary Table 3 for variable description and loadings. We applied principal components analysis (PCA), identifying a single factor that retained 9 binary variables and explained 47% of total variance. We then estimated the SEP index score by summing the 9 retained variables (unweighted), and categorized the score into tertiles (lower SEP, score 0–1; middle SEP, score 2–3; higher SEP, score 4–9).
2.5. Statistical methods
Descriptive analyses (unweighted frequencies and percentages) contrasted participant characteristics across the exposure and the outcome. Multinomial logistic regression was used to examine the relative odds of having pre-hypertension or hypertension (compared to normotension). Linear regression was used to model systolic blood pressure (SBP).
We fit a sequence of progressively adjusted models for each outcome. Model 1 was unadjusted. Model 2 was adjusted for individual-level confounders (age, physical activity, lactating status). Model 3 added SEP, number of children and adults in the household, and urban/rural residence. For linear regression models with continuous SBP as the outcome, all models were additionally adjusted for use of anti-hypertensive medication (N = 59, 3.8%) because it directly lowers measured blood pressure. Anti-hypertensive medication use was not included in multinomial logistic regression models, as medication use was part of the hypertension definition and adjustment could have resulted in over-adjustment.
All regression analyses incorporated SIVESNU’s complex survey design using variables that accounted for the primary sampling unit (conglomerado) and the sampling weight (pesomujer). A two-sided p-value <0.05 was considered statistically significant. All analysis were performed using SAS 9.4® (Statistical Analysis System, RRID: SCR_008567).
2.5.1. Sensitivity analyses
We conducted two sensitivity analyses. First, we repeated the multinomial regression models using different hypertension cut-offs (46, 47) to test whether our conclusions hold across other hypertension guidelines (Supplementary Table 4). Second, we refined the cross-classification exposure by decomposing FI into mild, moderate, and severe, resulting in an 8-category exposure variable (Supplementary Table 5).
3. Results
3.1. Descriptive results
The mean age of the sample was 30.2 years (SD 9.3) (Table 1). Nearly 40% of the sample had high BP (7% pre-hypertension, 32% hypertension). Mean systolic blood pressure was 114.1 mm Hg (SD 13.6). Overall, 74% of women in the sample experienced FI and 60% had OWT/OB. Forty-four percent experienced coexisting burden of FI and OWT/OB, whereas 16% had only OWT/OB and 30% only FI. Women with coexisting burden were more likely to be older, live in a rural area, and middle SEP. Those experiencing only OWT/OB were disproportionately urban and of higher SEP, whereas those with only FI were disproportionately younger, resided in rural areas, and had lower SEP. Participants with high BP (pre-hypertensive and hypertensive) were older and predominantly lived in rural areas compared to normotensive individuals (Supplementary Table 6).
Table 1.
Characteristics of the sample, stratified by food insecurity and overweight/obese status (N = 1,538)a.
| Food insecurity (FI) and overweight/obese (OWT/OB) | |||||
|---|---|---|---|---|---|
| Total (N = 1,538) | Neither (N = 144, 9%) | Only FI (N = 467, 30%) | Only OWT/OB (N = 253, 16%) | Coexisting burden (FI and OWT/OB) (n = 674, 44%) | |
| Hypertension | |||||
| Normal | 946 (61%) | 107 (74%) | 358 (70%) | 132 (52%) | 349 (52%) |
| Pre-hypertension | 100 (7%) | 7 (1%) | 28 (1%) | 19 (1%) | 46 (1%) |
| Hypertension | 492 (32%) | 30 (25%) | 81 (29%) | 102 (47%) | 279 (47%) |
| SBP (mean ± SD) | 114.1 ± 13.6 | 108.5 ± 11.6 | 110.2 ± 13.3 | 115.5 ± 11.6 | 117.4 ± 13.9 |
| Food insecurity | |||||
| None | 397 (26%) | 144 (100%) | - | 253 (100%) | - |
| Mild | 612 (40%) | - | 244 (52%) | - | 368 (55%) |
| Moderate | 364 (23%) | - | 145 (31%) | - | 219 (32%) |
| Severe | 165 (11%) | - | 78 (17%) | - | 87 (13%) |
| BMI (mean ± SD) | 27.0 ± 5.5 | 22.0 ± 2.1 | 22.0 ± 2.0 | 30.0 ± 4.0 | 30.4 ± 4.6 |
| Age (mean ± SD) | 30.2 ± 9.3 | 26.2 ± 8.6 | 26.1 ± 9.0 | 31.1 ± 8.6 | 33.6 ± 8.5 |
| Age group | |||||
| 15–19 years | 240 (16%) | 37 (26%) | 139 (30%) | 26 (10%) | 38 (6%) |
| 20–29 years | 511 (33%) | 65 (45%) | 176 (38%) | 88 (35%) | 182 (27%) |
| 30–39 years | 480 (31%) | 27 (19%) | 97 (21%) | 88 (35%) | 268 (40%) |
| 40–49 years | 307 (20%) | 15 (10%) | 55 (12%) | 51 (20%) | 186 (28%) |
| Physical activity (tertiles)b | |||||
| Low | 562 (37%) | 72 (50%) | 165 (35%) | 87 (34%) | 238 (36%) |
| Middle | 497 (32%) | 32 (22%) | 161 (34%) | 86 (34%) | 218 (32%) |
| High | 479 (31%) | 40 (28%) | 141 (31%) | 80 (32%) | 218 (32%) |
| Lactating status | |||||
| No | 1,292 (84%) | 116 (81%) | 371 (79%) | 217 (86%) | 588 (87%) |
| Yes | 246 (16%) | 28 (19%) | 96 (21%) | 36 (14%) | 86 (13%) |
| Socioeconomic position (tertiles)c | |||||
| Lower | 533 (35%) | 42 (29%) | 216 (46%) | 39 (15%) | 236 (35%) |
| Middle | 526 (34%) | 41 (28%) | 158 (34%) | 72 (28%) | 255 (38%) |
| Higher | 479 (31%) | 61 (43%) | 93 (20%) | 142 (57%) | 183 (27%) |
| N children in household | |||||
| 0 children | 171 (11%) | 19 (13%) | 40 (9%) | 30 (12%) | 82 (12%) |
| 1–2 children | 790 (51%) | 86 (60%) | 230 (49%) | 151 (60%) | 323 (48%) |
| ≥3 children | 577 (38%) | 39 (27%) | 197 (42%) | 72 (28%) | 269 (40%) |
| N adults in household | |||||
| 1–2 adults | 841 (55%) | 75 (52%) | 254 (54%) | 154 (61%) | 358 (53%) |
| 3–4 adults | 479 (31%) | 50 (35%) | 245 (31%) | 69 (27%) | 215 (32%) |
| ≥5 adults | 218 (14%) | 19 (13%) | 68 (15%) | 30 (12%) | 101 (15%) |
| Urban/rural | |||||
| Urban | 583 (38%) | 64 (44%) | 137 (29%) | 133 (53%) | 249 (27%) |
| Rural | 955 (62%) | 80 (56%) | 330 (71%) | 120 (47%) | 425 (63%) |
FI, food insecurity; OWT/OB, overweight/obese; SBP, systolic blood pressure; SD, standard deviation. aResults presented in N (%), except for SBP and age presented as mean ± SD. bPhysical activity includes minutes/week of moderate and vigorous (multiplied by 2 to standardize to moderate) sports activity, as well as bicycle activity. Total minutes were summed and divided in tertiles as low (0–60), middle (61–210) and high (211–4,200). cSEP was derived from nine variables that proxied household and individual assets/socio-economic position. See Supplementary Table 3.
3.2. Primary adjusted analysis
Table 2 displays adjusted regression analysis results for three-category hypertension and continuous systolic blood pressure. Statistically significant (p < 0.05) positive associations with hypertension were observed among those with ‘only OWT/OB’ and ‘coexisting burden,’ compared to women with neither FI nor OWT/OB. Although estimates were slightly attenuated after adjusting for confounders (Model 2 and Model 3), the associations remained consistent and statistically significant on both groups.
Table 2.
Adjusted association between food insecurity and OWT/OB status, and outcomes for hypertension and systolic blood pressure (N = 1,538)a.
| Pre-hypertensive vs. normotensiveb | Hypertensive vs. normotensive | SBP (mm Hg, continuous) | ||||
|---|---|---|---|---|---|---|
| OR (95% CI) | P-value | OR (95% CI) | p-value | β Coeff. (95% CI) | p-value | |
| Neither | Referent | Referent | Referent | |||
| Model 1: Unadjusted | ||||||
| Only FI | 1.25 (0.36, 4.26) | 0.72 | 0.86 (0.51, 1.45) | 0.57 | 0.94 (−1.70, 3.57), | 0.48 |
| Only OWT/OB | 2.89 (0.81, 10.39) | 0.10 | 3.23 (1.76, 5.95) | <0.001 | 5.94 (2.94, 8.94) | <0.001 |
| Coexisting burden | 2.02 (0.65, 6.24) | 0.22 | 2.89 (1.66, 5.03) | <0.001 | 7.16 (4.45, 9.87) | <0.001 |
| Model 2: Adjusted for individual-level covariatesc | ||||||
| Only FI | 1.31 (0.36, 4.72) | 0.68 | 0.86 (0.49, 1.52) | 0.60 | 0.94 (−1.77, 3.65) | 0.49 |
| Only OWT/OB | 2.66 (0.73, 9.67) | 0.14 | 2.54 (1.37, 4.70) | 0.003 | 4.15 (1.22, 7.07) | 0.01 |
| Coexisting burden | 1.70 (0.56, 5.14) | 0.35 | 1.92 (1.07, 3.42) | 0.03 | 4.59 (1.98, 7.18) | 0.001 |
| Model 3: Adjusted for Model 2 covariates + household-level covariatesd | ||||||
| Only FI | 1.21 (0.32, 4.67) | 0.78 | 0.79 (0.44, 1.43) | 0.44 | 0.29 (−2.45, 3.02) | 0.84 |
| Only OWT/OB | 2.79 (0.76, 10.32) | 0.12 | 2.69 (1.44, 5.02) | 0.002 | 4.63 (1.79, 7.51) | 0.002 |
| Coexisting burden | 1.62 (0.50, 5.20) | 0.42 | 1.83 (1.01, 3.31) | 0.05 | 4.12 (1.47, 6.77) | 0.003 |
CI, confidence interval; FI, food insecurity; OR, odds ratio; OWT/OB, overweight/obesity. aAll regression analysis accounted for survey weights. A two-sided p-value ≤0.05 is considered statistically significant. bOutcome 1 (hypertension) was modeled with multinomial logistic regression. Outcome 2 (SBP) was modeled using linear regression. Hypertension status was defined according to AHA/ACC 2017 guidelines (pre-hypertension 120–129 and DBP <80, hypertension SBP≥130 or DBP≥80). cIndividual-level covariates: age group (15–19y, 20–29y, 30–39y, 40–49y), physical activity (tertiles of min/week), lactating status (yes/no). When modeling outcome 2 (SBP), the adjustment list also included hypertension medication (takes oral prescription medicine to control hypertension). dHousehold-level covariates: socio-economic position (tertiles), number of children in the household (0, 1–2, ≥3), number of adults in the household (1–2, 3–4, ≥5), urbanicity (urban vs rural).
For example, compared to women who were normotensive and had neither FI nor OWT/OB, the odds of hypertension were 169% higher (model 3 OR = 2.69, 95%CI 1.44–5.02) among those with only OWT/OB, and 83% higher for those with coexisting burden (model 3 OR = 1.83, 95%CI 1.01–3.31). There was no evidence of a conditional association between only FI and hypertension (p > 0.4 for all models), nor between any category of the exposure and pre-hypertension (p > 0.1 for all models).
Results for systolic blood pressure (SBP) aligned with the hypertension results. Compared to the reference group, SBP was about 5 mmHg higher among those with only OWT/OB and 4 mmHg higher among those with the coexisting burden in fully adjusted models (model 3 Beta = 4.63, 95%CI 1.79–7.51; Beta = 4.12, 95%CI 1.47–6.77, respectively). As with hypertension, no association was observed between only FI and SBP (p > 0.4 for all models).
3.3. Sensitivity analysis
When repeating the multinomial regression models using different hypertension cut-offs (46, 47), we obtained similar estimates and same inference, suggesting that our results are not sensitive to the specific definition of hypertension used (Supplementary Table 4). When decomposing FI into mild, moderate, and severe categories within the OWT/OB cross-classification, only OWT/OB and mild coexisting burden remained significantly associated with hypertension and SBP. Moderate coexisting burden was significantly associated with SBP only. Although severe coexisting burden showed a similar estimate magnitude to the mild coexisting burden, the association was not statistically significant, likely due to limited sample size. Additionally, severe FI was associated with hypertension in the opposite direction (i.e., lower odds of hypertension), which could be attributable to small sample size and lower BMI in this subgroup (Supplementary Table 5).
4. Discussion
Overall, among a nationally representative sample of Guatemalan WRA, those who experienced coexisting burden of FI and OWT/OB, or who experienced only OWT/OB, had higher odds of hypertension and higher SBP compared to those with neither FI nor OWT/OB. Our descriptive findings highlight the high burden of chronic disease risk among WRA in Guatemala, and align with prior research reporting socioeconomic conditions and disparities in malnutrition in the country (25, 26). The majority of participants experienced FI (74%), and similarly the majority had OWT/OB (60%); nearly 40% had high blood pressure. The majority of the sample lived in rural areas (62%). Those experiencing the ‘coexisting burden’ of FI and OWT/OB were predominantly of middle SEP and lived in rural areas, as compared to those with only OWT/OB (higher SEP, urban) or only FI (lower SEP, rural).
Our results point to OWT/OB as the key driver of high BP among WRA in Guatemala, with FI as a contributor to high BP in combination with OWT/OB, but not on its own among under- or normal-weight WRA. We conjecture that women experiencing FI without OWT/OB might have a healthier, less-processed diet which may lower risk of hypertension (48). In Guatemala, FI may also reflect reduced overall caloric intake or limited access to processed, sodium-dense foods among some rural and lower-SEP households, which could partially attenuate hypertension risk despite economic hardship (49–51). The lack of an association between FI and hypertension is supported by a systematic review (59), which found no association between FI and clinically measured BP; however, there were no studies that assessed the association according to categories of BMI. Contrary to our hypothesis, we found stronger associations with hypertension and SBP among women with only OWT/OB than among those with the coexisting burden. Given that FI is typically more variable over time than nutritional status (5, 8, 52), it is possible that women with only OWT/OB had more consistent exposure to unhealthy dietary patterns, and other lifestyle factors that could have contributed to hypertension. Of note, a larger proportion of higher SEP was observed among those only OWT/OB (57%) compared to those with coexisting burden (27%), only FI (20%), or neither (47%). In addition, the majority of WRA with only OWT/OB lived in urban areas; this was not the case for the other exposure categories.
Although hypertension is highly prevalent in Latin American countries (53), evidence on its relationship with the food insecurity and obesity paradox remains scarce (7, 53). To our knowledge, this is the first study to examine the joint association of FI and OWT/OB on clinically measured hypertension. Among the few studies that exist, the combination of FI and obesity has been associated with greater postpartum weight gain (38) and worse mental health among women (22). In general, among women who experienced both FI and OWT/OB, higher odds of hypertension may be due to the stress of FI interacting with metabolic factors related to adiposity such as insulin resistance, inflammation, elevated cortisol levels, and other metabolic dysregulations (18). Nevertheless, we did not observe statistically significant associations with pre-hypertension, likely due to small subgroup sizes and limited statistical power.
Our findings highlight the importance of strengthening integrated nutrition and chronic disease prevention efforts in Guatemala, particularly within the framework of the National Food and Nutrition Security Policy 2022–2037 (Política Nacional de Seguridad Alimentaria y Nutricional; POLSAN), which incorporates prevention of overweight, obesity, and other noncommunicable diseases into the national food security agenda (54). Recent national initiatives under POLSAN have emphasized early detection of obesity, nutrition education, physical activity promotion, and multisectoral actions to address the double burden of malnutrition (55). Integrated policies addressing both nutritional vulnerability and chronic disease risk factors may help reduce future cardiovascular disease burden among Guatemalan women. Although the SIVESNU 2018–2019 cycle was the most recent publicly available nationally representative dataset at the time of analysis, recently released summaries from the 2022–2023 SIVESNU cycle suggest that OWT/OB among Guatemalan WRA continue to rise, indicating that the burden observed in our study likely remains highly relevant (56). Future studies should examine whether the joint association between FI, OWT/OB, and hypertension persists in more recent nationally representative data as these datasets become publicly available.
One of the key strengths of our study is its novelty in the Latin American context, addressing a critical gap on the intersection of food insecurity, obesity and hypertension (14). Using a nationally representative sample of WRA—a population highly vulnerable to nutrition and health disparities—including Indigenous and non-Indigenous women from both rural and urban areas, we ensured internal generalizability across different socioeconomic and geographic contexts of Guatemala. Additionally, data were collected using rigorous protocols for clinical measurements on weight and hypertension, minimizing bias associated with self-reported data (57). FI was assessed using the FIES scale, a standardized and validated tool appropriate for the regional context (33). We were also able to account for both individual and household confounders, including SEP which we derived using PCA, a robust and validated methodology used in previous studies in the absence of income data.
Our study is not absent of limitations. As a cross-sectional study, it cannot establish temporal order or causality, particularly given the complexity of the relationship under study. Additionally, information bias may be present as BMI was used to classify obesity instead of more precise measures of adiposity such as bioelectrical impedance analysis or dual-energy X-ray absorptiometry (58). This may have led to misclassification, likely nondifferential, potentially attenuating the observed associations. While there is no gold standard for measuring FI, capturing it at the household level may not fully reflect its severity at the individual level due to intra-household food allocation differences and is subject to recall and social desirability bias (14), which could either overestimate or underestimate the prevalence and its association with hypertension. Residual confounding is also a concern, as neighborhood-level data and other relevant individual factors, such as marital status and dietary intake, were not available; this could bias estimates in either direction depending on the distribution of these unmeasured factors. Finally, SIVESNU sampling and the specific sociocultural and economic context of Guatemala may limit the generalizability of our findings to WRA living in households without young children, older women >49 years old, men, and other populations of high-income countries or with less ethnic diversity. However, given the absence of national estimates on this topic in Guatemala prior to our study, our findings have the potential to inform national policy and program design aimed at addressing the interplay between FI, obesity, and hypertension.
5. Conclusion
With nearly 40% of Guatemalan WRA having high blood pressure, those with OWT/OB, with or without FI, had higher odds of hypertension compared to those without OWT/OB and FI. Although the coexistence of FI and OWT/OB was significantly associated with hypertension, OWT/OB alone showed the strongest associations, suggesting that excess body weight may be the primary driver of hypertension risk in this population. Patterns of FI and OWT/OB differed substantially by SEP status and urbanicity. Socioeconomic disparities in malnutrition and hypertension highlight the need for targeted interventions addressing this coexisting burden and the urgency of policies that improve food access and promote healthier dietary patterns. Future research should explore longitudinal associations to better understand underlying mechanisms.
Acknowledgments
We thank the SIVESNU staff who supported data collection and availability, also providing valuable assistance to this study. We also acknowledge the support and guidance provided by colleagues at INCAP and Drexel University.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. The original surveillance system (SIVESNU) described in this manuscript received funding and/or in-kind office space, laboratory installations, or technical assistance from the Institute of Nutrition of Central America and Panama (INCAP), U.S. Agency for International Development (USAID), Centers for Disease Control and Prevention (CDC), and United Nations Children’s Fund (UNICEF). This work was supported by the Fogarty International Center’s training program at the U.S. National Institute of Health (NIH) under the grant D43TW011971, with no role in the study design, data collection, analysis, interpretation of data, writing of the manuscript, or decision to submit the article for publication. The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the official position of the CDC, INCAP, USAID, UNICEF, or NIH.
Footnotes
Edited by: Ioanna Kontele, University of West Attica, Greece
Reviewed by: C. J. Sonowal, Tata Institute of Social Sciences, India
Marian Botchway, University of Notre Dame, United States
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found at: Health and Nutrition Epidemiological Surveillance System Survey (SIVESNU)/Secretariat of Food Security and Nutrition, Presidency of Guatemala (SIINSAN) at https://portal.siinsan.gob.gt/monitoreo-y-evaluacion/#1643404186375-f4d98353-dd51.
Ethics statement
The studies involving humans were approved by Guatemala’s Ministry for Health National Ethics Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
PA: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Writing – original draft, Writing – review & editing. MK-L: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing – review & editing. FL-S: Project administration, Supervision, Writing – review & editing. MR-Z: Data curation, Funding acquisition, Investigation, Writing – review & editing. KM: Data curation, Investigation, Writing – review & editing. MP: Data curation, Investigation, Writing – review & editing. AA: Conceptualization, Methodology, Project administration, Supervision, Validation, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Correction note
This article has been corrected with minor changes. These changes do not impact the scientific content of the article.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
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Publisher’s note
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2026.1857055/full#supplementary-material
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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
Publicly available datasets were analyzed in this study. This data can be found at: Health and Nutrition Epidemiological Surveillance System Survey (SIVESNU)/Secretariat of Food Security and Nutrition, Presidency of Guatemala (SIINSAN) at https://portal.siinsan.gob.gt/monitoreo-y-evaluacion/#1643404186375-f4d98353-dd51.