Exploring the association between socio-economic and environmental factors and food consumption in Iran: insights from time series data

Abstract

Background

The nutrition transition—a global shift toward refined, low-fiber diets—is driven by urbanization and economic development, with significant implications for food security. In Iran, where economic volatility (e.g., inflation, sanctions and GDP fluctuations) has altered dietary habits, this study aims to quantify the association of socio-economic and environmental factors with household food consumption patterns. Understanding these dynamics is critical for designing policies that ensure equitable access to healthy diets amid rapid transitions.

Method

We analyzed secondary data from Iran’s annual Household Income and Expenditure Survey (1991–2019; n = 756,232 households), using a three-stage cluster sampling method to ensure national representativeness. Key variables included urbanization rates, gross domestic product (GDP), food production, inflation, and precipitation, with food consumption measured in daily Adult Male Equivalent (AME) units and categorized into six dietary groups (including; grains, vegetables, fruits, dairy, fats, and meats). Time series regression with ARIMA errors (R 4.1.2) identified drivers of consumption trends.

Results

In present study, urbanization was positively associated with meat (r = 0.135, P < 0.001) and vegetable (r = 0.086, P = 0.020) consumptions, however negatively associated with grains (r = -0.317, P = 0.025), fruits (r = -0.037, P < 0.001), and dairy (r = -0.0571, P = 0.012) intake. GDP growth raised fruit (r = 0.00007, P = 0.005) and meat (r = 0.00008, P = 0.030) consumptions while lowering grain reliance (r = -0.0007, P = 0.020). Qualitative insights revealed that urban households prioritized convenience and protein-rich foods, whereas rural diets remained grain-heavy due to cost constraints. Inflation increased grain consumption (staple reliance) (r = 0.025, P = 0.003) but decreased fruit intake (r = -0.009, P = 0.007). Additionally, higher precipitation boosted grain consumption (r = 0.004, P = 0.001).

Conclusion

Iran’s nutrition transition is strongly tied to socio-economic shifts: urbanization and GDP growth favor diversified diets, while inflation exacerbates reliance on cheap staples. Policymakers must prioritize (1) subsidies for nutrient-rich foods, (2) nutrition education, and (3) climate-resilient agriculture to mitigate dietary inequities. These findings offer a template for other economies facing similar transitions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-025-23437-1.

Three key points highlighting the contributions of our study

• Provides the first comprehensive time-series analysis (1991–2019) of Iran’s food consumption patterns,

• Examines the unique interplay of economic instability (e.g., sanctions, inflation) and ecological pressures (e.g., water scarcity) in driving Iran’s nutritional transition

• Offers a comparative perspective on dietary transitions

Introduction

Over the past few decades, countries around the world have experienced a profound nutritional transition, marked by a shift from traditional diets rich in fiber and whole foods to those high in refined sugars, fats, and salt [1–3]. This global phenomenon is particularly evident in the Middle East, where diets have become increasingly dominated by calorie-dense and nutrient-poor foods, contributing to a surge in nutrition-related non-communicable diseases (NCDs) such as obesity, diabetes, and cardiovascular disease. These dietary shifts often occur alongside broader demographic, socioeconomic, and environmental transformations, including urbanization, industrial development, and climate change [4–9].

Gross domestic product (GDP) and urbanization are among the key macro-level drivers of food consumption patterns. In many contexts, increased GDP is associated with higher consumption of nutrient-dense foods, such as fruits, vegetables, seafood, and animal proteins [10]. Simultaneously, urbanization, especially in low- and middle-income countries, has altered food production and distribution systems, often increasing the availability of processed and energy-dense foods while decreasing dietary quality [9, 11–15]. Furthermore, climate change has become an increasingly disruptive force in global food systems, affecting agricultural productivity and food security [16–20].

Within this context, Iran presents a unique and underexplored case. Over the past three decades, the country has faced persistent macroeconomic instability, including prolonged periods of economic sanctions, chronic inflation, and unstable GDP growth [21–23]. These economic pressures have coincided with environmental stressors, such as water scarcity, desertification, and reduced precipitation, driven largely by climate change [24, 25]. In addition, urbanization and population growth have formed consumption preferences, accelerating the transition toward diets rich in meats, oils, and discretionary calories, while displacing traditional and nutrient-rich dietary components [8, 26–28].

Unlike in many other middle-income countries, Iran's nutritional transition has occurred without the stabilizing effects of sustained economic growth, challenging conventional assumptions about the relationship between GDP and diet quality [7, 10, 29]. The drying of inland lakes, declines in agricultural productivity, and estimated GDP contractions of 30–50% since 2011 have compounded the nation’s food security challenges [15–18]. Yet, most existing studies tend to generalize dietary transitions across countries, with few offering in-depth analyses of how economic shocks, inflation, and sanctions intersect with global trends in the Iranian context [19, 20].

An analysis of regional trends reveals that urbanization and rising incomes in Turkey have accelerated a shift toward Westernized diets, while inflation and recurrent droughts undermine food security [30, 31]. In Pakistan, low-income reliance on cereals, compounded by climate-induced agricultural disruptions, exacerbates malnutrition [32]. Post-conflict economic instability and water scarcity in Iraq have diminished dietary diversity, pushing households toward low-cost staples [33]. Meanwhile, Central Asia’s post-Soviet economic transition and increasing desertification have replaced traditional livestock-based diets with processed foods, whereas Afghanistan’s protracted instability and droughts have entrenched dependence on food aid [34, 35]. These patterns underscore common challenges—economic volatility, environmental stressors, and rapid urbanization—that collectively transform nutritional landscapes. Such insights are critical for informing Iran’s food security policies, given its analogous socio-economic and climatic pressures.

This study contributes to the literature by addressing several critical gaps. It aims to conduct a comprehensive time-series analysis of food consumption patterns in Iran from 1991 to 2019, incorporating macroeconomic indicators such as GDP and inflation, demographic factors like urbanization, and environmental influences including climate change. No similar study has been conducted in Iran, and with the sample size used, it may not have been conducted anywhere in the world. It also explores the complex interaction between economic instability and environmental stressors in shaping Iran’s distinctive nutritional trajectory. In addition, the study offers a comparative perspective by examining how Iran’s dietary transition aligns with or diverges from those observed in neighboring or economically similar countries, such as Iraq, Pakistan, and Turkey.

By integrating macroeconomic and ecological dimensions, this study presents a novel framework for understanding how volatile political economies and climate-related challenges influence dietary transitions. The findings aim to support policymakers in designing strategies that promote sustainable and healthy diets in resource-constrained settings.

Food group data

This study utilized comprehensive data from the Households Income and Expenditure Survey (HIES) from 1991 to 2019. The HIES is conducted annually by the Statistical Centre of Iran (SCI) and targets all private and institutional households residing in both urban and rural regions across the country. A stratified three-stage cluster sampling method was applied to select participants [36]. Over the full period from 1991 to 2019, the total sample size comprised 756,232 households. Ethical approval for secondary data analysis was obtained from the Ethics Committee of Mashhad University of Medical Sciences (Code: IR.MUMS.MEDICAL.REC.1400.480), ensuring compliance with national guidelines for anonymized data use.

The HIES food expenditure dataset encompassed the quantity and cost of 194 different food items consumed by households during the previous month. These included purchased foods, as well as those received as gifts, donations, or produced domestically by household members. Based on our previous studies, the aforementioned food data were converted into six food groups, including grains, vegetables, fruits, dairy, fats, and meats [37–39], as defined in detail in Table 1. The data were standardized to daily intake values.

Table 1.

Category of food groups

Food groups	Food items
Grains	Bread, cereal, noodles, pasta, rice, barley, starch, popcorn, cornflakes, wheat germ, bulgur
Vegetables	Potatoes, cabbage, cauliflower, brussels sprouts, broccoli, mixed vegetables, spinach, lettuce, cucumbers, eggplant, green peas, green beans, green peppers, turnips, corn, squash, celery, mushrooms, onions, garlic, tomatoes, carrots, olives
Fruits	Pears, apricots, cherries, apples, raisins or grapes, bananas, cantaloupe, watermelon, oranges, grapefruit, kiwi, grapefruits, strawberries, peaches, nectarines, tangerines, mulberries, plums, persimmons, pomegranates, lemons, pineapples, fresh figs, natural fruit juices, dried figs, dried dates, dried mulberries, other dried fruits
Dairy	Milk, yogurt, flavored milk, cream, cheese, ice cream, dough
Fats, oils, sugars, sweets	Hydrogenated fats, animal fats, mayonnaise, vegetable oils, chocolates, candies, tamarisk, honey, artificial juice, jam, jelly, cookies, cakes, confections
Meat, poultry, fish, eggs, legumes, and nuts	Processed meats, red meats, organ meats, poultry, fish, shrimp, eggs, beans, peas, lima beans, broad beans, lentils, soy, peanuts, almonds, pistachios, hazelnuts, roasted seeds, walnuts

We have included equations to illustrate the calculation of food groups for better transparency, as follows:

Grains group = bread + cereal + noodles + pasta + rice + barley + starch + popcorn + cornflakes + wheat germ + bulgur.

Vegetables group = potatoes + cabbage + cauliflower + brussels sprouts + broccoli + mixed vegetables + spinach + lettuce + cucumbers + eggplant + green peas + green beans + green peppers + turnips + corn + squash + celery + mushrooms + onions + garlic + tomatoes + carrots + olives.

Fruits group = pears + apricots + cherries + apples + raisins or grapes + bananas + cantaloupe + watermelon + oranges + grapefruit + kiwi + grapefruits + strawberries + peaches + nectarines + tangerines + mulberries + plums + persimmons + pomegranates + lemons + pineapples + fresh figs + natural fruit juices + dried figs + dried dates + dried mulberries + other dried fruits.

Dairy group = milk + yogurt + flavored milk + cream + cheese + ice cream + dough.

Fats, oils, sugars, sweets = hydrogenated fats + animal fats + mayonnaise + vegetable oils + chocolates + candies + tamarisk + honey + artificial juice + jam, jelly + cookies + cakes + confections.

Meat, poultry, fish, eggs, legumes, and nuts = processed meats + red meats + organ meats + poultry + fish + shrimp + eggs + beans + peas + lima beans + broad beans + lentils + soy + peanuts + almonds + pistachios + hazelnuts + roasted seeds + walnuts.

In present study trained and experienced interviewers conducted face-to-face interviews with the heads of households and asked them a series of questions about their household for the purposes of the study (reference [40] provides more comprehensive and detailed information).

Because food intake was assessed at the household level, interpreting the results becomes challenging and total intake figures needed to be adjusted for individual consumption. To do this, the total household food consumption was divided by the number of household members. However, due to individual variations in dietary needs based on age, gender, body size, and physical activity, food consumption is not equal among members [41]. For instance, a household with young children is likely to have lower overall energy requirements than one composed solely of adults, as children have lower caloric needs. Therefore, instead of using a simple per capita method, Adult Male Equivalent (AME) units were calculated for each individual [41]. AME reflects the energy requirement of an individual based on age and sex, relative to that of a reference adult male aged 18–30 years with moderate physical activity, as defined by the FAO and WHO guidelines [42].This approach provides a more accurate estimation of each person's contribution to overall household food consumption compared to per capita assessments [43]. In this research, AME values were assigned to all household members based on age and sex, and the sum of these values was used to estimate individual food intake. Each food item’s total amount was divided by the household’s AME sum to calculate intake per AME.

Here are the equation formulas;

1. Total Household AME Calculation:

   Total AMEhousehold = ${\sum }_{\mathrm{i}=1}^{\mathrm{n}}\mathrm{AMEi}$

   Where:

   AMEi = Adult Male Equivalent of member individual (based on age and sex).

   i = individual.

   n = number of household members.

2. Equivalent Daily Intake per AME (for each food item):

   Food intake per AME = $\frac{\mathrm{Total}\mathrm{houshold}\mathrm{food}\mathrm{item}\mathrm{amount}}{\mathrm{Total}{\mathrm{AME}}_{\mathrm{houshold}}}$

This converts household-level data to an equivalent intake per adult male.

Also, since these values reflect food items that were purchased, gifted, or produced, they differ from actual intake values, as some of the food typically goes to waste and is not actually consumed. To account for food waste, we applied FAO’s estimated loss percentages for each food group, specifically at the consumption level of the food supply chain [44].

Adjustment for Food Waste Using FAO Estimates:

Adjusted Food Intake per AME = Food intake per AME × (1- waste percentage).

Waste Percentage is the FAO estimate for each food group (e.g., 0.10 for 10% waste).

It is important to note that although efforts were made to bring the purchased food quantities closer to actual intake by applying the above adjustments, these values should not be considered exact intake values. What is obtained is essentially a proxy for actual intake. Therefore, throughout this text, any reference to "intake amount" refers to this proxy of intake per adult male equivalent.

Finally, NUTRITIONIST IV software which is customized for the Iranian food composition database, was employed to analyze energy and nutrient intakes, including energy, macronutrients and micronutrients [45, 46]. Households reporting energy consumption levels greater or lesser than three standard deviations from the average were excluded from further analysis. Furthermore, Energy and nutrient contents of food items were analyzed using the USDA food composition table (FCT), and for traditional Iranian foods that were not provided by the USDA FCT, the Iranian FCT was used. Iranian FCT was added to Nutritionist IV, therefore for both foods which either supported or don't support USDA FCT, energy and nutrient were calculated.

Statistical analysis

In this study, time series regression was used to model the determinants of food consumption. The resulting model is a regression with ARIMA errors and provides all the advantages of regression with the powerful time series features of an ARIMA model. The data were analyzed in R version 4.1.2 using the forecast package. A value of P < 0.05 was considered statistically significant.

Model identification

The general form of a multiple regression model is

$${\mathrm{Y}}_t=b_0+b_1{X}_{1t}+\cdots +b_k{X}_{\mathit{kt}}+N_t,$$ 1

where ${\text{Y}}_t$ is the response variable, $X_{1t},\cdots ,X_{\mathit{kt}}$ are the k explanatory variables, the coefficients $b_1,b_2,\cdots ,b_k$ are model parameters, and $N_t$ is the error term. One key assumption in the regression model is that $N_t$ is “white noise", meaning it is an uncorrelated series [47]. However, when fitting a regression model to time series data, autocorrelation in the residuals is commonly found. Ignoring these autocorrelations causes problems, such as “spurious regression”, where the p-values associated with the coefficients become too small, leading some predictor variables to appear significant when they are not [48]. To address this issue, a regression model with ARIMA errors was introduced, where this model allows the errors to contain autocorrelation.

Let $N_t$ in Eq. (1) be an ARMA (p, q) model, then it can be rewritten as:

$$N_t={\varnothing }_1{N}_{t-1}+{\varnothing }_2{N}_{t-2}+\cdots +{\varnothing }_p{N}_{t-\text{p}}-{\theta }_1{e}_{t-1}-{\theta }_2{e}_{t-2}-\cdots -{\theta }_q{e}_{t-q}+e_t,$$ 2

where, the coefficients ${\varnothing }_1,{\varnothing }_2,\cdots ,{\varnothing }_{p}and{\theta }_1,{\theta }_2,\cdots ,{\theta }_q$ are the model parameters.

The Autoregression with Moving Average, introduced by Box and Jenkins (1970), is denoted as ARMA (p, q). In these models, p is the order of the autoregressive (AR) process, and q is the order of the moving average (MA) process [49]. If a non-stationary model is required for the errors $N_t$, the model can be specified by first differencing all variables and then fitting a regression model with an ARMA (p, q) errors. A regression model in differences with ARMA errors is equivalent to a regression model with ARIMA (p, d, q) errors. In this model, p is the order of the autoregressive (AR) process, d is the order of the difference, and q is the order of the moving average (MA) process [49].

To identify the models, the following approach was used: First, the regression model was fitted with an AR(1) or AR(2) as a proxy model for the errors, $N_t$. If the regression errors appeared nonstationary and differencing seemed appropriate, the response variable and all explanatory variables were differenced. Then, the model was refitted using the same proxy model for $N_t$, this time with the differenced variables. If the errors appeared stationary, an appropriate ARMA model was identified for the error series, $N_t$. The orders of q and p were determined from the large spikes at lags in the ACF and PACF plots, respectively. Next, the entire model was refitted using the new ARMA model for the errors. Several models were tested, and the best model for $N_t$ was chosen as the one with the minimum Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. Finally, it was verified that the residual series $e_t$ resembled white noise.

Results

Figures 1, 2, 3, 4, 5 and 6 illustrate the annual trends for each food consumption variable from 1991 to 2019, alongside projections extending to 2030. These figures include a red dashed line representing the actual historical data of food consumption (including grains, vegetables, fruits, dairy products, fats, and meat) in grams per day from 1991 to 2019; a green dashed line representing the fitted values for the same historical period from the regression model with ARIMA errors, which are reported in Table 2 for the corresponding food consumption variables; a solid blue line representing the forecast from 2020 to 2030 based on the model. Additionally, these figures incorporate prediction intervals to convey the uncertainty inherent in our forecasts. Specifically, the dark shaded regions denote 80% prediction intervals, while the light shaded regions denote 95% prediction intervals. These intervals define a range of values within which each future value is likely to fall, with the corresponding probability.

Fig. 1.

The trend of changes in grains consumption during the years 1991 to 2019 and forecasting up to 2030. Grain consumption has shown a gradual decline since 1991 (60 g/day), reaching its lowest level in 2012 (≈50 gr/day). and remaining relatively stable since 2019. According to our results, these fluctuations could be attributed to changes in inflation (r = 0.025, P = 0.003), GDP (r = −0.0007, P = 0.020), urbanization (r = −0.317, P = 0.025), and precipitation (r = 0.004, P = 0.001). It is anticipated that by 2030, grain consumption will decrease slightly, stabilizing at around 52 g per day

Fig. 2.

The trend of changes in vegetable consumption from 1991 to 2019, with a forecast up to 2030. Vegetable consumption has been on the rise since 1991, peaking in 2008 (≈5.5 gr/day), and then stabilizing with minor fluctuations through 2019. Based on our results, urbanization (r = 0.086, P = 0.020) may be the reason of these fluctuations. Projections suggest that vegetable consumption will remain constant at approximately 5.2 g per day until 2030

Fig. 3.

The trend of changes in fruits consumption during the years 1991 to 2019 forecasting up to 2030. Fruit consumption has experienced notable fluctuations over the study period, with alternating phases of decline and growth. The lowest fruit consumption occurred in 1996 (≈2.6 gr/day), while the highest was recorded in 2017 (≈3.7 gr/day). The variation in fruit consumption may be related to inflation (r = −0.009, P = 0.007) and urbanization (r = −0.037, P < 0.001). Future trends are expected to stabilize at 3.4 g per day through 2030 (Fig. 3)

Fig. 4.

The trend of changes in dairy consumption the years 1991 to 2019 and forecasting up to 2030. Dairy consumption shows a general declining trend, from around 5.9 g/day in 1991 to approximately 4.8 g/day in 2000, followed by fluctuations until 2019. These fluctuations could be attributed to urbanization (r = −0.0571, P = 0.012). The forecast indicates a stabilization around 5.0 g/day, with uncertainty reflected in the estimates

Fig. 5.

The trend of changes in fats consumption during the years 1991 to 2019 forecasting up to 2030. The consumption of fats, oils, sugar, and sweets exhibited a consistent upward trend throughout the study period. It is predicted that fat consumption will continue to increase, reaching about 24 g per day by 2030

Fig. 6.

The trend of changes in meats consumption during the years 1991 to 2019 forecasting up to 2030. From 1991 to around 2001, meat intake remained around 7.5–8.0 g/day, followed by a sharp rise starting in 2002, reaching about 12.5 g/day by 2012. This higher level of intake remained relatively stable through 2019. Based on our results, GDP (r = 0.00008, P = 0.030) and urbanization (r = 0.135, P < 0.001) played significant roles in the fluctuations. It is projected to remain between 10 and 12.5 g per day until 2030

Table 2.

Summary of parameter estimations for regression models with ARIMA errors

Response Variable	Model	Explanatory Variable	Parameter Estimation	Standard errors	Z-Value	P-Value
Grains	Regression with ARIMA (1, 0, 2) errors
		Inflation	0.025	0.008	2.996*	0.003
		GDP	−0.0007	0.0003	−2.322*	0.020
		Food production	0.018	0.012	1.479	0.139
		Urbanization	−0.317	0.141	−2.238*	0.025
		Precipitation	0.004	0.001	3.241*	0.001
Vegetables	Regression with ARIMA (0,0,1) errors
		Inflation	−0.009	0.005	−1.797	0.072
		GDP	0.0001	0.0002	0.638	0.524
		Food production	−0.006	0.009	−0.730	0.466
		Urbanization	0.086	0.037	2.328*	0.020
		Precipitation	−0.0002	0.0006	−0.401	0.689
Fruits	Regression with ARIMA (0,1,2) errors
		Inflation	−0.009	0.003	−2.674*	0.007
		GDP	0.00007	0.00003	2.805*	0.005
		Food production	0.006	0.00465	1.323	0.186
		Urbanization	−0.037	0.00781	−4.790*	< 0.001
		Precipitation	−0.0005	0.00051	−1.038	0.299
Dairy	Regression with ARIMA (0,0,1) errors
		Inflation	0.0032	0.003	0.865	0.387
		GDP	0.00007	0.0001	0.389	0.697
		Food production	0.0006	0.005	0.116	0.907
		Urbanization	−0.0571	0.022	−2.490*	0.012
		Precipitation	0.00009	0.0004	0.211	0.832
Fats	Regression with ARIMA (2,0,0) errors
		Inflation	0.0028	0.012	0.230	0.818
		GDP	0.0001	0.0002	0.588	0.557
		Food production	0.0098	0.014	0.678	0.498
		Urbanization	0.178	0.137	1.299	0.194
		Precipitation	−0.001	0.001	−0.948	0.343
Meat	Regression with ARIMA (2,1,0) errors
		Inflation	0.003	0.005	0.545	0.585
		GDP	0.00008	0.00003	2.164*	0.030
		Food production	0.011	0.009	1.251	0.210
		Urbanization	0.135	0.012	10.831*	< 0.001
		Precipitation	−0.001	0.0007	−1.313	0.188

^* P-Value < 0.05

To forecast each food consumption using a regression model with ARIMA errors, both the regression and ARIMA parts in the food consumption model were forecasted separately and then combined. Since future predictors were unknown, they were modeled independently and projected through 2030. Subsequently, these predictor forecasts were incorporated into the regression framework of the food consumption model to generate food consumption projections up to 2030.

The Grain consumption has shown a gradual decline since 1991 (60 g/day), reaching its lowest level in 2012 (≈50 gr/day), and remaining relatively stable since 2019. It is anticipated that by 2030, grain consumption will decrease slightly, stabilizing at around 52 g per day (Fig. 1).

The Vegetable consumption has been on the rise since 1991, peaking in 2008 (≈5.5 gr/day), and then stabilizing with minor fluctuations through 2019. Projections suggest that vegetable consumption will remain constant at approximately 5.2 g per day until 2030 (Fig. 2).

The Fruit consumption has experienced notable fluctuations over the study period, with alternating phases of decline and growth. The lowest fruit consumption occurred in 1996 (≈2.6 gr/day), while the highest was recorded in 2017 (≈3.7 gr/day). Future trends are expected to stabilize at 3.4 g per day through 2030 (Fig. 3).

The Dairy consumption shows a general declining trend, from around 5.9 g/day in 1991 to approximately 4.8 g/day in 2000, followed by fluctuations until 2019. The forecast indicates a stabilization around 5.0 g/day, with uncertainty reflected in the estimates (Fig. 4).

The consumption of fats, oils, sugar, and sweets exhibited a consistent upward trend throughout the study period. It is predicted that fat consumption will continue to increase, reaching about 24 g per day by 2030 (Fig. 5).

From 1991 to around 2001, meat intake remained around 7.5–8.0 g/day, followed by a sharp rise starting in 2002, reaching about 12.5 g/day by 2012. This higher level of intake remained relatively stable through 2019. It is projected to remain between 10 and 12.5 g per day until 2030 (Fig. 6).

Further comparison and explanation of Figs. 1, 2, 3, 4, 5, and 6 are provided in the Supplementary Section.

Table 2 showed the associations between socio-economic and environmental factors—including urbanization, GDP, inflation, food production, and precipitation (as independent variables)—and the consumption of various food groups, including grains, vegetables, fruits, dairy, fats, and meats (as response or dependent variables). According Table 2. urbanization was positively associated with meat (r = 0.135, P < 0.001) and vegetable (r = 0.086, P = 0.020) consumptions, however was negatively associated with grains (r = −0.317, P = 0.025), fruits (r = −0.037, P < 0.001), and dairy (r = −0.0571, P = 0.012) intake. GDP growth raised fruit (r = 0.00007, P = 0.005) and meat (r = 0.00008, P = 0.030) consumptions while lowering grain reliance (r = −0.0007, P = 0.020). Inflation increased grain consumption (staple reliance) (r = 0.025, P = 0.003) but decreased fruit intake (r = −0.009, P = 0.007). Additionally, higher precipitation boosted grain consumption (r = 0.004, P = 0.001). Explanations for these significant associations were provided in the Discussion section.

$$\mathit{G}\mathit{r}\mathit{a}\mathit{i}\mathit{n}{\mathit{s}}_t=75.30+0.025inflatio{n}_t-0.0007GD{P}_t+0.018Foodproductio{n}_t-0.317Urbanizatio{n}_t+0.004Precipitatio{n}_t+N_t,Where{N}_t=0.623N_{t-1}+e_t+0.450e_{t-1}+0.999e_{t-2}$$

$$\mathit{V}\mathit{e}\mathit{g}\mathit{e}\mathit{t}\mathit{a}\mathit{b}\mathit{l}\mathit{e}{\mathit{s}}_t==-0.783-0.009inflation_t^{}+0.0001GD{P}_t-0.006Foodproductio{n}_t+0.086Urbanizatio{n}_t-0.0002Precipitatio{n}_t+N_t,Where{N}_t=e_t+0.591e_{t-1}$$

$$\mathit{F}\mathit{r}\mathit{u}\mathit{i}\mathit{t}{\mathit{s}}_t=-0.009inflatio{n}_t+0.00007GD{P}_t+0.006Foodproductio{n}_t-0.037Urbanizatio{n}_t-0.0005Precipitatio{n}_t+N_t,Where{N}_t=e_t+N_{t-1}-0.021e_{t-1}-0.196e_{t-2}$$

$$\mathbf{Dairy}=8.596+0.0032inflatio{n}_{\mathit{t}}+0.00007GD{P}_{\mathit{t}}+0.0006Foodproductio{n}_{\mathit{t}}-0.0571Urbanizatio{n}_{\mathit{t}}+0.00009Precipitatio{n}_{\mathit{t}}+N_{\mathit{t}},Where{N}_{\mathit{t}}=e_{\mathit{t}}+0.581e_{\mathit{t}-\mathit{1}}$$

$${\mathbf{Fats}}_t=9.017+0.002inflatio{n}_{\mathit{t}}+0.0001GD{P}_{\mathit{t}}+0.009Foodproductio{n}_{\mathit{t}}+0.178Urbanizatio{n}_t-0.001Precipitatio{n}_t+N_t,Where{N}_t=1.313N_{\mathit{t}-\mathit{1}}-0.455N_{\mathit{t}-\mathit{2}}+e_t$$

$${\mathbf{Meat}}_t=0.003inflatio{n}_{\mathit{t}}+0.00008GD{P}_{\mathit{t}}+0.011Foodproductio{n}_t+0.135Urbanizatio{n}_t-0.001Precipitatio{n}_t+N_{\mathit{t}},Where{N}_t=1.211N_{\mathit{t}-\mathit{1}}+0.193N_{\mathit{t}-\mathit{2}}-0.404N_{\mathit{t}-\mathit{3}}+e_t$$

Model diagnostics

Model diagnostics focus on evaluating the goodness of fit of the specified and estimated models. As shown in Table 3, the p-values from the Ljung-Box Q test (LBQ) exceeded 0.05 for all models, indicating no evidence of autocorrelation in the residuals. Similarly, the P-values for the normality tests were greater than 0.05, supporting the assumption of normality for the residuals.

Table 3.

Model diagnostics

Response Variable	MAPE	RMSE	AICc	BIC	Ljung–Box Q test		Jarque Bera Test
					${\mathrm{\chi }}^2$	P-Value	${\mathrm{\chi }}^2$	P-Value
Grains	0.016	0.502	81.66	83.11	3.774	0.286	1.434	0.488
Vegetables	0.0001	0.218	17.58	21.31	6.271	0.280	2.354	0.308
Fruits	0.014	0.154	−0.400	2.68	1.539	0.819	0.673	0.714
Dairy	0.001	0.149	−4.36	−0.62	7.164	0.208	1.331	0.514
Fats	0.045	0.550	77.30	80.13	1.451	0.835	0.452	0.797
Meat	0.007	0.328	42.16	45.23	4.371	0.358	0.071	0.965

Abbreviations: MAPE Mean absolute percentage error, RMSE Root mean square error, AICc corrected Akaike Information Criterion, BIC Bayesian Information Criterion

Discussion

The present study examined the association between socio-economic and environmental factors, including; inflation, GDP, food production, urbanization, and precipitation with food consumption trends among the Iranian population. The results indicated a positive association between the consumption of meats and vegetables with urbanization. Conversely, there was an inverse association between the consumption of grains (bread, cereal, rice, and pasta), fruits, and dairy with urbanization. Additionally, GDP was positively correlated with the consumption of fruits, and meats, while it was negatively correlated with the consumption of grains (bread, cereal, rice, and pasta). Inflation was associated with increased consumption of grains (bread, cereals, rice, and pasta) and decreased consumption of fruits. Furthermore, a positive relationship was observed between precipitation and the consumption of grains (bread, cereal, rice, and pasta).

Urbanization, the migration from rural to urban areas, has profound effects on dietary habits, food accessibility, and health globally. Currently, over half the world’s population resides in urban areas, a figure projected to reach 6 billion by 2041 [50]. This shift has been accompanied by a "nutrition transition," marked by increased consumption of animal-source foods, refined grains, processed foods, and sugars, with reduced intake of traditional staples like cereals and dairy [51].

Furthermore, urbanization does not always lead to improved nutritional pattern. Historical studies, such as those in Great Britain, show urban diets were often poorer than rural ones, characterized by higher tea, coffee, and sugar consumption and lower intakes of grains, dairy, and vegetables [52]. Recent patterns in China align with this trend, as urban diets shifted towards fruits, vegetables, meat, and seafood, although our findings suggest a reduction in dairy and fruits alongside increased meat and vegetable consumption [53, 54].

Urbanization influences diets through changes in income, prices, and agricultural practices. The loss of farmland and pollution reduce food availability, particularly for fruits and dairy [54]. In wealthier Asian countries, urban living significantly reduces grain consumption while increasing demand for meat, fruits, and vegetables [55]. Food purchasing behavior also varies: rural diets depend on subsistence farming and budgets, while urban residents have broader access to processed and convenience foods [55–59]. Economic opportunities in urban areas, especially for women, have fueled demand for fast foods and ready-to-eat meals [60, 61].

As a result of study conducted by Hejazi et.al, food insecurity in both urban and rural households worsened, with the percentage of urban households affected rising from 8.84% in 2017 to 11.2% in 2019, and rural households from 25.17% to 29.2%. Furthermore, research shows that, in comparison to urban populations, rural residents in Iran generally consume lower amounts of fruits, vegetables, and meat, while their diets include higher intakes of bread, legumes, and sugar [62, 63]. In a recent study by Darroudi et al., rural males and females consumed significantly more fats and oils (3.72 g/day, P = 0.01, and 5.2 g/day, P < 0.001, respectively) than their urban counterparts. However, our findings did not reveal any significant association between urbanization and fat consumption. Furthermore, Darroudi et al. reported that both suburban females and males consumed significantly less fruit compared to urban individuals (−47.41 g/day, P < 0.001; and −60.42 g/day, P = 0.001, respectively), but there was no significant difference between urban and rural areas. In contrast, our study found that urbanization was negatively associated with the consumption of fruits, dairy products, and grains, while showing a positive association with meat and vegetables. These discrepancies may be attributed to differences in data sources. While Darroudi et al. used data from Iran’s Food and Nutrition Surveillance System (FNS), our analysis was based on data from the Household Income and Expenditure Survey (HIES) and we did not separate suburban from urban population. Additionally, the study by Darroudi et al. categorized participants into three subgroups, including suburban, urban, and rural, whereas our study considered only the overall level of urbanization. Therefore, this difference distinguished these two studies.

Gross domestic product (GDP) is widely recognized as an indicator of socioeconomic status and has been linked to various health outcomes and dietary patterns. Agriculture significantly contributes to the global economy, accounting for over 20% of GDP. Moreover, higher income levels, associated with rising GDP, even allow households to afford a greater variety of foods. Muhammad et al. [64] demonstrated that global increases in income tend to raise fruit consumption, though the impact varies across demographic categories. For example, younger men might benefit more from lower fruit prices, while older women allocate higher incomes toward healthier foods. These findings align with evidence suggesting that women are more likely than men to use additional income to purchase healthier food options [65, 66]. However, vegetable consumption does not always show a significant increase in income, possibly due to concerns about pesticide exposure and other contaminants, as suggested by our findings. Additionally, regional and cultural differences are important factors affecting the quantity of fruits and vegetables consumed in different areas [67].

Interestingly, GDP per capita registered its highest value in 2019, corresponding to peak per capita consumption of fruits and meat, while its lowest value in 1992 coincided with significantly reduced consumption of these foods. This directly proportional relationship between GDP and meat and fruit consumption has been consistently reported in the literature [68]. For example, Nguyen Thanh Binh et al. [10] and Popescu et al. [69] found that in higher-GDP countries, diets were more animal-based and included higher fruit intake, which was consistent with our results. This factors collectively create an environment conducive to dietary diversification. In the current study, we found that GDP was positively associated with fruit and meat consumption while inversely associated with grain consumption. These findings are consistent with those of Mario Siervo et al., who observed a direct relationship between per capita GDP and fruit consumption and an inverse relationship with grain consumption [70].

Despite the benefits of economic growth on diet quality, achieving recommended dietary intakes remains a challenge, particularly in low- and middle-income countries (LMICs). The WHO recommends consuming at least 400 g of fruits and vegetables daily to prevent non-communicable diseases (NCDs). However, as shown in the PURE study, individuals in low-income countries consume far fewer servings of fruits and vegetables than their higher-income counterparts, with only a small proportion meeting the WHO recommendations [10, 26]. Economic growth often leads to a"nutrition transition,"characterized by reduced reliance on staple grains and increased consumption of higher-protein and higher-fat foods [71, 72]. While this shift diversifies diets and improves nutrient intake, it can also contribute to rising rates of obesity and diet-related NCDs if not carefully managed. Public health efforts should emphasize sustainable and balanced dietary patterns to optimize the benefits of economic growth while mitigating health risks associated with dietary transitions [19, 73, 74].

Additionally, previous studies have shown that economic sanctions negatively impact the GDP growth of the targeted country. Based on figures from the World Bank, Iran’s GDP per capita dropped significantly from $8,329 in 2012, when international sanctions were first imposed, to $2,746 by 2020. Aside from a notable 6.8% growth in 2016 following the nuclear agreement, the country’s economy either stagnated or contracted throughout the decade [75]. As a result, this period is often referred to as Iran’s “lost decade” [75]. In line with our results, Saeediankia et al. demonstrated inflation and sanctions have negative effects on fruit consumption while the per capita GDP led to an increase in it [76]. Also, they predicted that sanctions account for a decline of about 15 gr in daily meat consumption for an adult person [76]. Mokari-Yamchi et al. claimed that among Iranian population, grains have the highest budget share and quantity, while our study showed negative association between GDP and grain consumption [77]. In addition, red meat and vegetables have the highest and lowest unit prices, respectively [77]. However, no prior study has assessed the association between GDP and food group consumption. Therefore, the aforementioned statement could be interpreted as indicating that higher GDP is associated with increased consumption of fruits and meat.

Food price inflation exhibits a similar pattern globally, impacting real purchasing power directly. As food prices rise, real purchasing power decreases. Meanwhile, high food price inflation affects not only a country’s macroeconomic stability but also places a significant burden on small-scale farmers and low-income consumers in developing nations, where a substantial share of household income is spent on food [78]. Allais et al. have suggested that the full impact of increasing food prices on nutrient consumption is not fully clear due to the complex interdependencies among food commodities [79]. In China, similar to the global trend, increasing fuel prices impact food price inflation both directly, by raising production costs for farmers, and indirectly, through the development of grain-based fuel production technologies [80]. Piesse et al. demonstrated that biofuels contribute to increased grain demand by diverting grains from markets to biofuel production, thus driving up grain prices [80]. Furthermore, current food price inflation has been attributed to severe weather events, rising labor wages, and cyclical supply shortages of vegetables [81].

According to a USDA report, "the Consumer Price Index (CPI) for 'all food' increased 4% in 2007, the largest annual jump since 1990" [82]. This rapid inflation in stable food and farm commodity prices was attributed to several factors, including high corn demand for ethanol production, low grain and oilseed supplies due to poor harvests, increased US exports driven by a weak dollar, changing eating habits influenced by rapidly emerging economies, and rising input costs. Furthermore, increases in energy costs led to higher transportation, processing, and retail expenses [83]. In South Africa, individuals in the very low socioeconomic status category spend approximately a third of their total food expenditure on staple starch foods such as maize meal and bread. These foods are rich in carbohydrates but have limited micronutrients and incomplete amino acids (typically ranging from 1 to 5 g per 100 g cooked). Food consumption studies conducted in South Africa consistently report maize as the most frequently consumed food, accounting for 78% of total population consumption, while consumption of fruits and vegetables remains limited [84, 85].

Food affordability is essential globally because food plays a crucial role in health and survival. Unfortunately, increased food prices can compromise the health status of many people. In developing countries, individuals living in poverty or with low incomes often spend less on balanced diets. Satiety, energy intake, and limited education all influence food choices, resulting in households in these countries relying heavily on starches for their main energy requirements and consuming very fruits and vegetables [86]. Economic conditions and food prices are significant factors influencing food choices [87], with changes in inflation rates expected to affect fruit consumption [88]. Furthermore, the habit of consuming certain foods, like fruits, can be fostered through early education; therefore, educational policies should include nutritional knowledge, as suggested by some authors [89].

In 2010 systematic review of the impact of food price on consumption, the results demonstrated price elasticity estimates ranging from 0.27 to 0.81 (absolute values of mean price elasticity estimate [95% CI])), with the price elasticities 0.7 [0.41–0.98] and 0.6 [0.43–0.77], for fruits and grains, respectively [90]. Nevertheless, in times of inflation, prices of goods and services, including food, increase. Consumers might shift their consumption patterns towards more basic and affordable staples like grains (such as rice, wheat, or corn) when the prices of other food items, like meat or dairy, rise significantly. This can lead to an increase in grain consumption. Additionally, factors such as stockpiling behavior, the substitution effect, government subsidies and support programs, and cultural and dietary shifts play vital roles in reinforcing the positive association between inflation and grain consumption. These elements collectively drive consumers to adjust their purchasing habits, often increasing their reliance on grains as they adapt to rising prices and economic uncertainties [91].

Under the broader framework of general sanctions theory, comprehensive sanctions are seen as significant disruptors of a country’s economic stability. These sanctions often lead to extensive trade disruptions, shortages of essential goods, and escalating inflation. Such economic challenges directly impact food availability and affordability, leading to notable shifts in dietary patterns across the population [92]. According the world bank [93], the Annual Iran's inflation rate increased to 30.6% in 2020, as well as Iran’s food market has a 25% import dependency ratio. Following the re-imposition of sanctions in 2018, the country saw a significant rise in food prices due to restrictions on international financial transactions.

The highest inflation rates were observed in vegetables, meats, and fruits, while less nutritious foods like white bread and oils had the lowest inflation rates [94]. In line with previous findings, our study showed that inflation was negatively associated with fruit consumption, while it was positively associated with grain consumption. The cost of a healthy diet for an average Iranian family of 3.3 people is 341,866,008 IRR (US$2,849), which is 3.6 times higher than the amount families spent on food the previous year (94,505,000 IRR or US$788) [94]. A healthy diet, rich in nutrients, is more expensive than an unhealthy one, with meats and fruits being the most costly food groups, while fats are the least expensive. Due to the current economic situation and government subsidies on certain foods like white bread, Iranians are increasingly turning to energy-dense, lower-cost foods such as fats, sugars, and refined grains [94]. Similarly, the present study demonstrated that inflation led to increased consumption of grains among Iranian citizens. For instance, the annual cost of grains and fruits for a family of 3.3 members was 45,303,654.0 IRR (US$377.53) and 79,683,697.5 IRR (US$664.03), respectively, which can support our results. Additionally, this shift toward an unhealthy diet is likely to lead to a rise in obesity and related diseases, including cardiovascular diseases, diabetes, and cancer. If food price inflation continues, the ongoing hidden hunger could escalate into a food crisis [94].

Changes in precipitation patterns due to climate change pose numerous challenges to food supply and food security [95]. In the study by Liu et al., three periods of precipitation transition were identified: abundant precipitation (Stage I, from 1988 to 2004), decreased precipitation (Stage II, from 2005 to 2015), and drought recovery (Stage III, from 2016 to 2018). Zhilin Liu et al. found a significant positive correlation between precipitation and food production (r = 0.535, P < 0.01), with the correlation coefficient between precipitation and grain yield higher than that between precipitation and meat and milk production. These results suggest that higher production levels lead to increased intake of grain-based foods. Consistent with these findings, the present study demonstrated that consumption of grain groups including bread, cereal, rice, and pasta increased with precipitation [95].

As climate change continues to alter precipitation patterns, previous studies have identified a downward trend in annual per capita fruit consumption with each 1 °C increase in temperature [69, 96]. This suggests that temperature changes influence consumption habits. Nevertheless, we did not see any significant association between changing in precipitation and fruit consumption in present study.

Since precipitation and its vital role in agricultural output, research has often highlighted a positive association between grain consumption and precipitation [97]. In fact, increased water availability from precipitation supports higher crop yields, leading to greater grain production, more accessibility, Affordability, and availability, which can encourage higher consumption levels [97]. Also, in regions with adequate precipitation, there may be less reliance on irrigation, linking to more sustainable grain farming and greater price stability, particularly in areas with limited access to irrigation. These factors can also impact on food preferences and diets, which tend to be more grain-centric due to affordability. For instants, in countries like china, sufficient precipitation has been led to steady or increased grain productions, which is correlated with enhancing food security and consumption of grains such as rice and wheat [97]. On the other hand, regarding region and types of grain this association might be different. Consist with aforementioned result, in Iran (based on our results), higher precipitation similarly correlates with increased grain consumption.

Iran receives less than one-third of the global average annual precipitation, approximately 250 mm, and much of the country is characterized by a harsh, arid climate. Over recent decades, the rise in temperature in Iran has exceeded the global average, with the rate of temperature change being nearly twice that of the global mean and showing significant fluctuations over time [98]. Regarding this phenomenon, several studies have examined the relationship between climate variables, such as temperature and precipitation, and agricultural production, without considering their impact on food consumption [99–102]. For instance, in Iran, a 1% decrease in average precipitation has been associated with a reduction in wheat production ranging from −5.24% to −46.12%, and in rice production from −0.01% to − 28.78%. Conversely, a 1% increase in average rainfall is expected to lead to a 30.28% rise in wheat production and approximately a 42.36% increase in rice production, with a 90% probability [103]. Meanwhile, higher levels of precipitation are associated with increased grain production, which may explain the rise in grain consumption.

Policy implications

The findings of this study carry significant implications for policymakers addressing food security, public health, and sustainable development in Iran and similar economies. Urbanization’s association with increased meat and vegetable consumption—alongside decreased intake of grains, fruits, and dairy—highlights the need for interventions to counteract the nutritional downsides of urban dietary shifts [104]. Public health campaigns should prioritize education on balanced diets, particularly in cities, where processed and convenience foods dominate [105]. Subsidies for fruits, vegetables, and dairy could make these foods more accessible, while urban farming initiatives might help reconnect populations with traditional, nutrient-rich staples [106].

Economic growth, while linked to greater dietary diversity, also risks exacerbating inequalities in food access [107]. As GDP rises, so does demand for animal products and fruits, but without targeted policies, lower-income groups may be left behind. Policymakers could consider subsidizing nutrient-dense foods for vulnerable populations or introducing tax incentives to encourage private-sector investment in affordable, healthy options [69]. Agricultural policies should also evolve to support crop diversification, reducing over-reliance on grains while ensuring farmers’ livelihoods [108].

Inflation remains a critical challenge, pushing households toward cheaper, calorie-dense staples like grains while reducing fruit consumption—a trend that threatens long-term health outcomes [109]. Temporary price controls or targeted food vouchers could help mitigate these effects during economic downturns [110]. Additionally, reducing food waste through improved storage and distribution infrastructure could stabilize prices and ensure more consistent access to perishable goods like fruits and dairy.

Climate variability further complicates food security, with precipitation levels directly affecting grain production and consumption [111]. To build resilience, investments in drought-resistant crops and water-efficient farming techniques are essential [95]. Diversifying food sources beyond rainfall-dependent grains could buffer against climate shocks, while early warning systems might help anticipate and manage supply disruptions.

Ultimately, addressing these interconnected challenges requires collaboration across sectors [108]. Health and agricultural ministries must work together to align farm subsidies with nutritional goals, while partnerships with the private sector could improve food distribution in underserved urban areas. Lessons from Iran’s experience—particularly the links between precipitation, grain security, and inflation—could also inform policies in other regions undergoing rapid urbanization and climate stress [95]. Moving forward, pilot programs testing these interventions in high-risk communities could provide valuable evidence for scaling up effective strategies.

Strength and limitation

One of the key strengths of this research is its comprehensive analysis of multiple macroeconomic and environmental drivers of dietary change. By examining inflation, GDP, urbanization, and precipitation together, the study provides a more complete understanding of how these interconnected factors influence food choices. The long-term perspective is another notable strength, as the analysis captures dietary shifts across several decades of economic transformation in Iran. This extended timeframe helps reveal meaningful trends that might be missed in shorter studies.

The findings align well with global evidence on nutrition transitions, particularly the well-documented shift toward more animal-based diets with urbanization and economic growth. This consistency with international research enhances the credibility of the results and suggests they may be relevant for other developing economies undergoing similar transitions. The study's focus on policy implications is particularly valuable, as it translates complex economic relationships into concrete recommendations for improving food security and public health.

However, several limitations should be considered when interpreting the results. The reliance on national-level data means the analysis may overlook important regional variations or socioeconomic disparities in food access and consumption. Moreover, our study failure to examine specific subgroups of the population, such as, pregnant women and children. While the study identifies significant associations between economic factors and dietary patterns, the observational nature of the research means we cannot establish definitive causal relationships. Other unmeasured factors, such as cultural preferences, food import policies, or specific government subsidies, may also influence the observed patterns but were not accounted for in the analysis.

The climate component of the study, while innovative, is limited by focusing primarily on precipitation. Additional environmental factors like temperature extremes or drought frequency might provide a more complete picture of climate impacts on food production and consumption. Furthermore, the study does not examine behavioral aspects of food choice or supply chain dynamics that could modify how economic factors affect actual consumption patterns.

These limitations point to valuable directions for future research. More granular data at regional or household levels could reveal important disparities masked by national averages. Incorporating qualitative methods could help uncover the cultural and behavioral dimensions of dietary change. Expanding the climate analysis to include additional environmental indicators would strengthen our understanding of food system vulnerabilities. Finally, evaluating the actual impact of proposed policy interventions would be an important next step in translating these findings into effective action.

Conclusion

The findings of this study revealed the complex dynamics through which macroeconomic and environmental factors collectively influence dietary patterns in Iran. Our analysis demonstrated that urbanization is a powerful driver of dietary change, promoting increased consumption of meats and vegetables while reducing intake of traditional staples like grains, fruits, and dairy products. These shifts mirror global nutrition transition patterns but are uniquely mediated by Iran's economic conditions and environmental constraints.

The study identified several critical policy opportunities that could help shape healthier dietary outcomes. To address the negative impact of inflation on fruit consumption, targeted subsidy programs could be implemented specifically for fresh fruits and vegetables in urban markets, coupled with nutrition education initiatives in schools and community centers. The decline in dairy intake associated with urbanization might be mitigated through urban food policy interventions, including incentives for dairy retailers in underserved neighborhoods and public awareness campaigns about the nutritional benefits of dairy products.

Economic policies should recognize the dual role of GDP growth, which while associated with greater dietary diversity, may also accelerate nutrition transition-related health risks. Strategic food pricing policies and agricultural support programs could help maintain access to traditional healthy foods even as incomes rise. The demonstrated relationship between precipitation and grain consumption suggests that investments in water-efficient agricultural technologies and climate-resilient crop varieties could help stabilize staple food supplies in the face of climate variability.

These findings collectively suggested that effective nutrition policy in Iran must adopt an integrated approach that simultaneously addresses economic, urban, and environmental dimensions of food security. Such an approach should combine targeted social protection measures with urban food environment regulations and climate-smart agricultural investments. Future research should focus on evaluating the effectiveness of these integrated interventions, with particular attention to their implementation feasibility within Iran's unique economic context and their potential to reduce socioeconomic disparities in dietary quality.

This more nuanced understanding of the interacting drivers of dietary change provides a stronger foundation for developing policies that can promote nutrition security amid Iran's ongoing socioeconomic transformation while preserving the nutritional benefits of traditional food systems.

Abbreviations

GDP

Gross domestic products

HIES

Households Income and Expenditure Survey

AMEs

Adult Male Equivalent units

NCDs

Non-communicable diseases

DBM

Double burden of malnutrition

UPFs

Ultra-processed foods

SCI

Statistical Centre of Iran

FCT

Food composition table

FFQ

Food frequency questionnaire

CCF

Cross-correlation function

ACF

Autocorrelation

PACF

Partial autocorrelation

AIC

Akaike Information Criterion

BIC

Bayesian Information Criterion

LBQ

Ljung-Box Q test

J-B

Jarque-Bera test

OLS

Ordinary least squares

ASF

Animal-source foods

CPI

Consumer Price Index

Authors’ contributions

The authors hereby appreciate the great contributions of the study participants. Overall, SRS and PR supervised the project and approved the final version of the manuscript to be submitted. SRS and PR designed the research. SRS, PR and NS analyzed and interpreted the data; SRS, PR and KI critically reviewed the manuscript; MMN, PR, KI, and SRS drafted the initial manuscript. All authors read and approved the final manuscript.

Funding

This study does not have any Funding support.

Data availability

Datasets used in the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Mashhad University of Medical Sciences, Mashhad, Iran (Code: IR.MUMS.MEDICAL.REC.1400.480). All participants provided written informed consent before enrollment, and the research protocol adhered to the ethical guidelines outlined by the committee and the principles of the Declaration of Helsinki (2013, Fortaleza) https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects.

Consent for publication

Not applicable’ for that section.

Competing interests

The authors declare no competing interests.