Nutrition Transition Patterns in the Context of Global Food Systems Transformation

Nutrition Transition Patterns in the Context of Global Food Systems Transformation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Analysis Nutrition Transition Patterns in the Context of Global Food Systems Transformation Bianca Carducci, Yixin Chen, Hanqi Luo, Patrick Webb, Jess Fanzo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4657489/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The idea of nutrition transition was critical to conceptualizing patterns of the global burden of malnutrition in line with demographic and epidemiological transitions of the 20 th century. However, earlier typologies are less reflective of the nuances which characterize today’s nutrition reality. This analysis presents a new paradigm for conceptualizing nutrition transition that better aligns with food system transitions and with evolving population dynamics. Examining seven nutrition indicators (under-5 stunting, wasting and overweight, adult overweight, anemia in women of reproductive age, adult diabetes and adult raised blood pressure) with national-level estimates between 2013-2023, we explore how food system typologies cluster across 108 countries and identify three distinct population-level patterns which describe a triple burden of malnutrition that manifests differently across the globe. The findings suggest that while Cluster 1 encompasses a large proportion of less industrialized countries from a food systems perspective, Clusters 2 and 3 represent a mosaic of typologies experiencing similar nutrition burdens in middle- and higher-income nations. Therefore, the forces shaping global and local food systems are dynamic and interactive, resulting in outcomes that are less linear and distinct than previous concepts allowed for. Scientific community and society/Developing world Health sciences/Risk factors Figures Figure 1 Figure 2 Introduction Over the last century, prevailing frameworks have depicted three kinds of population transitions– demographic, epidemiologic, and health. In the early 1920s, Frank Notestein described the demographic transition theory as a window of opportunity for the accelerated growth and increased longevity of populations due to lower rates of early mortality and fertility (1) . Although the remarkable reversal from high mortality and fertility has been observed in modern industrialized populations, such as North America and Europe, this same transition has become evident in middle-income countries and is expected to expand into low-income countries (2) . Building on that idea, Omran postulated that an epidemiological transition would also occur relating to three burdens of disease and mortality patterns, including receding mortality and morbidity from infectious diseases and an increased occurrence of chronic and non-communicable diseases (NCDs). This change is likely linked to social and economic factors, including national urbanization, policy liberalization and globalization, agricultural productivity, and income growth (3) . While debated in the literature as an extension of the second or a distinct third form of transition, the so-called nutrition transition adds two additional patterns (4, 5) by characterizing five patterns of population health focusing on dietary intake, physical activity and nutritional status (3); namely, (1) Collecting Food; (2) Famine; (3) Receding Famine; (4) Nutrition-related Non-Communicable Disease; (5) Behavioral Change. While these inter-linked population-wide patterns were important for conceptualizing the global burden of malnutrition and aligning nutrition outcomes with demographic and epidemiological transitions in the 20 th century, most of today’s population is concentrated in pattern 3 (receding famine) or pattern 4 (degenerative diseases). In addition, these patterns are less reflective of the nuance that manifests within countries, regions, households or individuals, which contribute to today’s nutrition reality. Many countries are experiencing multiple simultaneous, rather than sequential burdens, in which populations are dealing with both undernourishment and diet-related NCDs, for example, India, Indonesia and South Africa (6-8) . In other words, the idea of nutrition transition has become more complex and at the same time, over-simplified. The forces shaping global and local food systems are dynamic and interactive, resulting in outcomes that are less linear and distinct than previous concepts allowed for (9) . At the same time, homogenization of agriculture has created simplicity. Currently there are more than 6,000 plant species that are cultivated for food, but fewer than 200 make substantial contributions to global food output, and only nine accounting for 66% of total crop production as of 2014 (10) . Therefore, diets have become increasingly convergent and hence similar (11) and less optimal with overall declines in undernourishment and gains in diet-related NCDs everywhere (12) . As the world in the aggregate has become wealthier, there is greater inequity within countries and regions and, specifically, more nutrition inequity (13) . Here, we make the case for an updated approach to conceptualizing the nutrition transition that better aligns with current food systems changes and evolving population dynamics. The aim is to propose a new way of understanding of food systems dynamics to better support appropriate policy choices leading to universal access to healthy and sustainable diets. Online Methods Data Sources To quantify global patterns of nutrition, we drew on secondary data from national nutrition surveys and global estimates as well as food system typology information. A total of seven nutrition indicators were chosen to represent the nutritional status of countries in our study; four nutrition indicators used in the World Health Assembly comprehensive nutrition targets of 2012, and three additional indicators taken from the Global Nutrition Report. These were, collectively: 1. The prevalence of childhood stunting. Childhood stunting refers to children under 5 years who are shorter than the child growth median of children at the same age (less than 2 Standard Deviations (SD)); 2. The prevalence of anemia in women of reproductive age. Anemia refers to iron deficiency anemia which is defined as hemoglobin level < 120 g/L; 3. The prevalence of childhood overweight. Childhood overweight is identified as children under 5 years with more than 2 SD greater than child growth median; 4. The prevalence of childhood wasting. Childhood wasting refers to children who weigh 2 SD below the child growth median for their length; 5. The prevalence of raised blood pressure. Raised blood pressure refers to adults aged 18 and over with systolic and/or diastolic blood pressure ≥ 140/90mmHg; 6. The prevalence of adult diabetes. Adult diabetes is defined as adults aged 18 and older with fasting glucose ≥ 7.0mmol/L, on medication for raised blood glucose or with a history of diagnosis of diabetes; 7. The prevalence of adult overweight. Adult overweight is defined as adults whose body mass index (BMI) is greater or equal to 25 kg/m 2 . We compiled the latest country-level nutrition indicators data from publicly available databases. The definitions, data source, and summary statistics of seven nutrition indicators is presented in Supplementary Table 1. To conceptualize food systems, Marshall and colleagues (14) developed food system typologies by ranking and scoring countries based on four purposefully selected indicators: agriculture value added per worker (in constant 2010 USD), number of supermarkets per 100,000 inhabitants, urban population as a percent of total population and percent of dietary energy from cereals, roots, and tubers. To construct the typology, countries were first ranked from highest to lowest on each indicator, under the hypothesis that higher values were associated with more industrialized food systems, and lower values more traditional food systems. The ranking was inverted in the case of the share of dietary energy from cereals, roots, and tubers, which is theorized to be lower in more industrialized food systems. A score for each country was assigned based on the sum of its ranks on each of the four indicators. Using the distribution of scores, five quintiles were formed, with the lowest quintile representing the most industrialized food system type and the highest quintile representing the most traditional food system type. These types are exhaustive and mutually exclusive. The 155 countries included represent 97% of the global population, 93% of global land area, and 97% of global gross domestic product. In addition to food system typologies, we further classified each country by 2022 World Bank country income level (15) . Since the initial construction, indicators have been updated annually through the Food Systems Dashboard (16) . To be included in this analysis, countries were required to have prevalence data for all seven nutrition indicators collected after 2013 and 2021 food system typology information. Analytical approach We first conducted pairwise correlation testing of seven nutrition indicators to understand the co-existence of various forms of malnutrition. We estimated the median, minimum, maximum of the prevalence of nutrition indicators by food system typology. In order to categorize countries by common nutrition status, we conducted the k-means clustering based on the seven nutrition indicators. The Elbow method, Silhouette method, and Gap statistics method were used to determine the optimal number of clusters (Supplementary Figs. 1 and 2). Countries were then grouped by that optimal number. The standardized mean of the nutrition indicators by clusters were calculated and presented. In addition, the distribution of food system typologies for each cluster was presented. Results One hundred and eight countries with all seven nutrition indicators data collected in the past 10 years, alongside food system typology data, were included. The correlation among the seven nutrition indicators was estimated (Supplementary Table 2). Results show that almost all correlations between two indicators are significant, except for the ones between hypertension and anemia, diabetes, or wasting, as well as between wasting and diabetes. The median, minimum and maximum of the prevalence of seven nutrition indicators were calculated by five food system typologies (Table 1 ). The prevalence of stunting, wasting, and women’s anemia consistently decreased as the typology shifted from rural and traditional to industrial and consolidated; whereas, the prevalence of adult diabetes, raised blood pressure and adult and childhood overweight exhibited an increase from rural and traditional to modernizing and formalizing, but slightly decreased within the industrial and consolidated typology. Table 1 Descriptive Statistics of Nutrition Indicators by Food System Typology Cells are expressed as medians (min, max). Food System Typology Rural and Traditional (n = 31) Informal and Expanding (n = 29) Emerging and Diversifying (n = 23) Modernizing and Formalizing (n = 18) Industrial and Consolidated (n = 7) Nutrition Indicator Stunting, % 30.4 (12.7, 56.5) 18.7 (6.9, 43.6) 8.6 (3.4, 22.8) 6.8 (1.6, 21.9) 2.4 (1.2, 9.5) Wasting, % 6.5 (1.1, 16.3) 5.1 (0.8, 18.7) 2.7 (0.4, 9.1) 2.2 (0.3, 9.7) 0.4 (0.1, 1.7) Childhood Overweight, % 3.3 (0.8, 6.9) 4.6 (1.3, 18.8) 7.7 (3.3, 19.0) 8.4 (3.8, 13.4) 5.4 (3.1, 12.6) Anemia, % 42.4 (17.2, 59.0) 32.8 (7.4, 53.0) 23.0 (10.6, 52.4) 22.6 (8.7, 37.7) 13.5 (11.7, 21.7) Diabetes, % 5.2 (1.1, 18.9) 6.6 (1.9, 30.8) 9.1 (4.4, 17.7) 9.1 (5.9, 24.9) 6.8 (3.6, 10.7) Raised Blood Pressure, % 36.0 (25.7, 41.5) 37.9 (28.3, 46.8) 38.1 (20.7, 56.4) 41.3 (26.2, 49.1) 31.6 (26.7, 47.5) Adult Overweight, % 25.5 (20.0, 38.7) 34.4 (18.3, 77.6) 56.0 (30.6, 67.9) 60.3 (42.5, 73.4) 59.5 (30.3, 67.9) The analysis found three distinct clusters. Nutrition indicators, food typologies, and cluster information are summarized in Supplementary Table 3. The standardized mean of each nutrition indicator by cluster is presented in Fig. 1 . Cluster 1 countries exhibit the highest prevalence of child stunting and wasting, and anemia in women of reproductive age, median adult raised blood pressure, as well as the lowest prevalence of childhood and adult overweight, alongside adult diabetes. Cluster 2 countries have the lowest prevalence of childhood stunting, median childhood wasting and anemia, the highest prevalence of adult raised blood pressure, childhood and adult overweight and adult diabetes. Cluster 3 countries have the lowest prevalence of childhood wasting, anemia amongst women and adult raised blood pressure; whereas the other four indicators were within the median. The distribution of food systems by cluster and the countries by cluster are presented in Fig. 2 and Table 2 respectively. For cluster 1 (n = 48), the majority of countries are classified as rural and traditional (65%) and informal and expanding (38%). Two countries, Gabon and Maldives, were classified as emerging and diversifying and one country, Malaysia, as modernizing and formalizing. In cluster 2 (n = 40), 40% and 33% of countries were classified as emerging and diversifying and modernizing and formalizing, respectively. Finally, cluster 3 (n = 20) was fairly distributed and exhibited all typologies including, informal and expanding (30%), emerging and diversifying (25%), modernizing and formalizing (20%) and industrial and consolidated (20%). One country was included as rural and traditional typology. Global mapping of clusters by food system typologies are presented in Supplementary Fig. 3B. Table 2. Clustering of Countries and Food System Typology Cluster Food System Typology Countries Cluster 1 Rural and Traditional Afghanistan, Burundi, Benin, Burkina Faso, Bangladesh, Cambodia, Central African Republic, Chad, Congo Democratic Republic, Ethiopia, Ghana, Guinea, Guinea-Bissau, Kenya, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Myanmar, Nepal, Niger, Nigeria, Sierra Leone, Sudan, Tanzania, Togo, Zambia Cluster 1 Informal and Expanding Angola, Cameroon, Congo Republic, Cote d’Ivoire, Djibouti, Haiti, India, Indonesia, Mauritania, Pakistan, Philippines, Senegal, Sri Lanka, The Gambia, Uganda Cluster 1 Emerging and Diversifying Gabon, Maldives Cluster 1 Modernizing and Formalizing Malaysia Cluster 2 Informal and Expanding Azerbaijan, Egypt Arab Republic, Eswatini, Kyrgyz Republic, Namibia, Samoa, Tajikistan, Uzbekistan Cluster 2 Emerging and Diversifying Algeria, Armenia, Belize, Fiji, Georgia, Guyana, Iraq, Jamaica, Kazakhstan, Lebanon, Panama, Paraguay, Serbia, South Africa, Suriname, Tunisia Cluster 2 Modernizing and Formalizing Albania, Brazil, Bulgaria, Costa Rica, Dominican Republic, Jordan, Kuwait, Latvia, North Macedonia Mongolia, Montenegro, Oman, Saudi Arabia Cluster 2 Industrialized and Consolidated Argentina, Estonia, Uruguay Cluster 3 Rural and Traditional Rwanda Cluster 3 Informal and Expanding Bolivia, Guatemala, Honduras, Morocco, Thailand, Vietnam Cluster 3 Emerging and Diversifying China, Ecuador, El Salvador, Mexico, Peru Cluster 3 Modernizing and Formalizing Chile, Colombia, Iran Islamic Republic, Portugal Cluster 3 Industrialized and Consolidated Belgium, Germany, Korea, Rep., United States Discussion Using a systems lens, this analysis identified three distinct population-level patterns in which a triple burden of malnutrition, or the co-existence of undernutrition, overnutrition and a micronutrient deficiency, exists globally linked to various forms of food system transition. The seven key nutrition indicators changed differently as the typology shifted: the prevalence of stunting, wasting, and female wasting consistently decreased from rural and traditional to industrial and consolidated, while the prevalence of adult diabetes, raised blood pressure, and adult and childhood overweight initially increased from rural and traditional to modernizing and formalizing, before slightly decreasing within the industrial and consolidated typology. Cluster 1 represents nations characterized by the highest prevalence of undernutrition and enduring food insecurity. These issues stem from entrenched poverty and socio-political tensions. In Cluster 2, we observe emerging economies where rapid economic advancement has led to a recent shift towards overweight and obesity, with persistent pockets of undernourishment. However, inadequate governance structures hinder the effective prevention and management of this transition. Cluster 3 denotes countries that began grappling with multiple burdens of malnutrition in the 1990s, as highlighted by Popkin (17) . Consequently, these nations have instituted policies and initiatives aimed at addressing NCDs, with varying success. Importantly, the findings suggest that while Cluster 1 encompasses a large proportion of less industrialized countries from a food systems perspective, Clusters 2 and 3 are a mosaic of typologies experiencing similar nutrition burdens. This suggests that food systems are likely one of many systems contributing to nutrition transition and therefore, policy measures and public health programs beyond promotion of food security and healthy diets must be considered to halt these alarming trends. Various macro- and micro- drivers shape current trends and future trajectories of nutrition and health outcomes, including food systems that produce and provide diets (18) . Some of these forces and drivers are exogenous to food systems that suggest and spur world transition, development, or fragility. The forces vary – some being political and economical, others being technology and innovation, and others due to the behaviors and incentives of people – both consumers and the 1.2 billion who work in food systems (19) . Disruptions and shocks to these forces are impacting food systems, some due to conflict in certain areas of the world, more recently because of the pandemic, but increasingly due to climate-related extreme weather events (20, 21) . Globalization has changed and is now more nuanced with different actors and population dynamics. Our findings are consistent with other global studies on spatio-temporal trends of malnutrition. Since 1990, the number of people with hypertension worldwide has doubled, with most of the increase occurring in low-income and middle-income regions (22) . Likewise, diabetes prevalence has increased globally by 129.7% from 1990 to 2017, with the greatest burden in China, India, United States, Indonesia and Mexico (23) . At the same time, childhood undernourishment, especially stunting, has seen declines since the 90s, but progress has been uneven across regions (24, 25) . In contrast, relatively little progress has been made on child wasting, childhood obesity and anemia in women of reproductive age (24, 26) . However, global discourse has recently shifted away from discussing malnutrition in two distinct silos (essentially over- versus under- nutrition). Increasing importance has been placed on viewing undernutrition and overnutrition as interconnected issues sharing common pathophysiological processes that together contribute to the triple burden of malnutrition. While the current study utilized a population-level approach to assess the multiple burdens, there is a lack of consensus in the literature on the definition of the double or triple burden of malnutrition (27) . Previous studies have often measured co-existing stunting and overweight within the same individual, or within the same household (i.e., stunted children and overweight mothers) (28, 29) . A recent systematic review by Kosaka and Umezaki suggests the national prevalence of double burden households varied from 0.0 to 26.8% by country and year; however, few studies were directly comparable, because of differences in the combinations of undernourished and overweight persons, age ranges, nutritional indicators and cut-off points (30) . In addition, fewer studies measure the co-existence of other metrics of under- and over-nutrition, such as micronutrients deficiencies and risk factors of NCDs (31, 32) and current evidence is largely based on women of reproductive age and children under 5-years of age, excluding populations such as school-aged children, adult males and elderly, which limits their representativeness. From a monitoring perspective then, the World Health Assembly nutrition targets and routine national surveys must be expanded to measure and report both child and adult under- and over-nutrition within the same household or individual, in order to accurately assess and improve surveillance of malnutrition and address inequities. At the same time, improving the granularity of data on subnational food system typologies is critical to ensure national averages do not mask the nuance that exists within a country. These data are critical to informing priority setting, including the spectrum of practical, targeted and measurable multi-duty actions for nutrition that exist across food, health, water and social protection systems (33) . These actions include maternal nutrition and antenatal care, promotion of optimal nutrition in early life, as well as school food policies and programs and regulations on food marketing (34) . Countries that prioritize the implementation and scale-up of evidence-based, direct and indirect nutrition policies and programs stand to make great improvements in human capital development and economic productivity, as these interventions generally have very high cost-benefit ratios. All in all, our collective effort is required if we are to end all forms of malnutrition. For this goal to be achieved, active engagement within and across a range of sectors and systems is required to result in better outcomes and simultaneously support the achievement of sectoral goals. 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(2023) A multilevel analysis of the triple burden of malnutrition in Indonesia: trends and determinants from repeated cross-sectional surveys. BMC Public Health 23, 1836. Sahiledengle B, Mwanri L, Petrucka P et al. (2024) Co-existence of maternal overweight/obesity, child undernutrition, and anaemia among mother-child pairs in Ethiopia. PLOS Glob Public Health 4, e0002831. Escher NA, Andrade GC, Ghosh-Jerath S et al. (2024) The effect of nutrition-specific and nutrition-sensitive interventions on the double burden of malnutrition in low-income and middle-income countries: a systematic review. Lancet Glob Health 12, e419-e432. Hawkes C, Ruel MT, Salm L et al. (2020) Double-duty actions: seizing programme and policy opportunities to address malnutrition in all its forms. The Lancet 395, 142–155. Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4657489","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Analysis","associatedPublications":[],"authors":[{"id":327700574,"identity":"208d2d50-38a3-4aaf-bdde-c812bb2f57ab","order_by":0,"name":"Bianca Carducci","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACAwYGxgcJFRJyDAzMDQyMDRJQQfxamA0enLEwBmolXgub5MO2isQGiBYGwlrMpZsfSCSwSaT3tze2Sd3cYSHPwN68TQKfFss5xwwMEngkcmecOdgmnXtGwrCB51gZXi0GN3IYEhIkJHI3SCQCtbRJAL2TY0ZQy4EEA4l0A6gW+wb5NwS1MDYArUmAaUlskODBr8VyRpoxQ8IBCUOgX5qtgX5JbuNJK7bAp8VcIvn5z5//6uT525sP3s7dUWfbz3544w18WjABG2nKR8EoGAWjYBRgAwAE6kfc/lvnHAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3815-3048","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Bianca","middleName":"","lastName":"Carducci","suffix":""},{"id":327700575,"identity":"31bcdc55-cb35-4873-9cc1-f5f40a2c2bf4","order_by":1,"name":"Yixin Chen","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Yixin","middleName":"","lastName":"Chen","suffix":""},{"id":327700576,"identity":"0d252a52-b910-4803-87a7-1f807b0638ce","order_by":2,"name":"Hanqi Luo","email":"","orcid":"https://orcid.org/0000-0001-6253-5818","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Hanqi","middleName":"","lastName":"Luo","suffix":""},{"id":327700577,"identity":"fd8ea997-254b-497b-a76a-d1f32c168341","order_by":3,"name":"Patrick Webb","email":"","orcid":"https://orcid.org/0000-0002-9857-3354","institution":"Friedman School of Nutrition Science and Policy","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Webb","suffix":""},{"id":327700578,"identity":"a98873ea-47be-4dbb-9caf-8c9e0754b6e1","order_by":4,"name":"Jess Fanzo","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Jess","middleName":"","lastName":"Fanzo","suffix":""}],"badges":[],"createdAt":"2024-06-29 04:40:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4657489/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4657489/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60710574,"identity":"5a115b1a-28ca-4df2-aceb-0cbdf612c15a","added_by":"auto","created_at":"2024-07-19 20:06:24","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":219373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNutrition Indicator Standardized Mean by Cluster\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4657489/v1/02ee8d3599ac6add58c51764.jpeg"},{"id":60710577,"identity":"48ceb259-9d27-4c3e-9b33-dd5690d2a124","added_by":"auto","created_at":"2024-07-19 20:06:25","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":246740,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercent of Food System Typology, by Cluster\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4657489/v1/8a85846379c39f142284361d.jpeg"},{"id":79449854,"identity":"2dceb18d-5c77-43ac-958f-eeb4259aa7bd","added_by":"auto","created_at":"2025-03-28 14:44:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1032984,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4657489/v1/36e69945-fe11-427d-9c85-38506c6652a0.pdf"},{"id":60710575,"identity":"2bc17f99-770a-41f0-8a68-87183e511c23","added_by":"auto","created_at":"2024-07-19 20:06:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3803388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryFileNutritionTransitionFinal.docx","url":"https://assets-eu.researchsquare.com/files/rs-4657489/v1/9fcb2b04c6282feb6b3e8c2f.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Nutrition Transition Patterns in the Context of Global Food Systems Transformation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOver the last century, prevailing frameworks have depicted three kinds of population transitions\u0026ndash; demographic, epidemiologic, and health. In the early 1920s, Frank Notestein described the demographic transition theory as a window of opportunity for the accelerated growth and increased longevity of populations due to lower rates of early mortality and fertility \u003csup\u003e(1)\u003c/sup\u003e. Although the remarkable reversal from high mortality and fertility has been observed in modern industrialized populations, such as North America and Europe, this same transition has become evident in middle-income countries and is expected to expand into low-income countries \u003csup\u003e(2)\u003c/sup\u003e. Building on that idea, Omran postulated that an epidemiological transition would also occur relating to three burdens of disease and mortality patterns, including receding mortality and morbidity from infectious diseases and an increased occurrence of chronic and non-communicable diseases (NCDs). This change is likely linked to social and economic factors, including national urbanization, policy liberalization and globalization, agricultural productivity, and income growth \u003csup\u003e(3)\u003c/sup\u003e. While debated in the literature as an extension of the second or a distinct third form of transition, the so-called nutrition transition adds two additional patterns \u003csup\u003e(4, 5)\u003c/sup\u003e by characterizing five patterns of population health focusing on \u0026nbsp;dietary intake, physical activity and nutritional status (3); namely, \u0026nbsp;(1) Collecting Food; (2) Famine; (3) Receding Famine; (4) Nutrition-related Non-Communicable Disease; (5) Behavioral Change.\u003c/p\u003e\n\u003cp\u003eWhile these inter-linked population-wide patterns were important for conceptualizing the global burden of malnutrition and\u0026nbsp;aligning nutrition outcomes with demographic and epidemiological transitions\u0026nbsp;in the 20\u003csup\u003eth\u003c/sup\u003e century, most of today\u0026rsquo;s population is concentrated in pattern 3 (receding famine) or pattern 4 (degenerative diseases). In addition, these patterns are less reflective of the nuance that manifests within countries, regions, households or individuals, which contribute to today\u0026rsquo;s nutrition reality. Many countries are experiencing multiple simultaneous, rather than sequential burdens, in which populations are dealing with both undernourishment and diet-related NCDs, for example, India, Indonesia and South Africa \u003csup\u003e(6-8)\u003c/sup\u003e. In other words, the idea of nutrition transition has become more complex and at the same time, over-simplified. The forces shaping global and local food systems are dynamic and interactive, resulting in outcomes that are less linear and distinct than previous concepts allowed for \u003csup\u003e(9)\u003c/sup\u003e. At the same time, homogenization of agriculture has created simplicity. Currently there are more than 6,000 plant species that are cultivated for food, but fewer than 200 make substantial contributions to global food output, and only nine accounting for 66% of total crop production as of 2014 \u003csup\u003e(10)\u003c/sup\u003e. Therefore, diets have become increasingly convergent and hence similar \u003csup\u003e(11)\u003c/sup\u003e and less optimal with overall declines in undernourishment and gains in diet-related NCDs everywhere \u003csup\u003e(12)\u003c/sup\u003e. As the world in the aggregate has become wealthier, there is greater inequity within countries and regions and, specifically, more nutrition inequity \u003csup\u003e(13)\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we make the case for an updated approach to conceptualizing the nutrition transition that better aligns with current food systems changes and evolving population dynamics. The aim is to propose a new way of understanding of food systems dynamics to better support appropriate policy choices leading to universal access to healthy and sustainable diets.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Online Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eTo quantify global patterns of nutrition, we drew on secondary data from national nutrition surveys and global estimates as well as food system typology information. A total of seven nutrition indicators were chosen to represent the nutritional status of countries in our study; four nutrition indicators used in the World Health Assembly comprehensive nutrition targets of 2012, and three additional indicators taken from the Global Nutrition Report. These were, collectively: 1. The prevalence of childhood stunting. Childhood stunting refers to children under 5 years who are shorter than the child growth median of children at the same age (less than 2 Standard Deviations (SD)); 2. The prevalence of anemia in women of reproductive age. Anemia refers to iron deficiency anemia which is defined as hemoglobin level\u0026thinsp;\u0026lt;\u0026thinsp;120 g/L; 3. The prevalence of childhood overweight. Childhood overweight is identified as children under 5 years with more than 2 SD greater than child growth median; 4. The prevalence of childhood wasting. Childhood wasting refers to children who weigh 2 SD below the child growth median for their length; 5. The prevalence of raised blood pressure. Raised blood pressure refers to adults aged 18 and over with systolic and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140/90mmHg; 6. The prevalence of adult diabetes. Adult diabetes is defined as adults aged 18 and older with fasting glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0mmol/L, on medication for raised blood glucose or with a history of diagnosis of diabetes; 7. The prevalence of adult overweight. Adult overweight is defined as adults whose body mass index (BMI) is greater or equal to 25 kg/m\u003csup\u003e2\u003c/sup\u003e. We compiled the latest country-level nutrition indicators data from publicly available databases. The definitions, data source, and summary statistics of seven nutrition indicators is presented in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eTo conceptualize food systems, Marshall and colleagues \u003csup\u003e(14)\u003c/sup\u003e developed food system typologies by ranking and scoring countries based on four purposefully selected indicators: agriculture value added per worker (in constant 2010 USD), number of supermarkets per 100,000 inhabitants, urban population as a percent of total population and percent of dietary energy from cereals, roots, and tubers. To construct the typology, countries were first ranked from highest to lowest on each indicator, under the hypothesis that higher values were associated with more industrialized food systems, and lower values more traditional food systems. The ranking was inverted in the case of the share of dietary energy from cereals, roots, and tubers, which is theorized to be lower in more industrialized food systems. A score for each country was assigned based on the sum of its ranks on each of the four indicators. Using the distribution of scores, five quintiles were formed, with the lowest quintile representing the most industrialized food system type and the highest quintile representing the most traditional food system type. These types are exhaustive and mutually exclusive. The 155 countries included represent 97% of the global population, 93% of global land area, and 97% of global gross domestic product. In addition to food system typologies, we further classified each country by 2022 World Bank country income level \u003csup\u003e(15)\u003c/sup\u003e. Since the initial construction, indicators have been updated annually through the Food Systems Dashboard \u003csup\u003e(16)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo be included in this analysis, countries were required to have prevalence data for all seven nutrition indicators collected after 2013 and 2021 food system typology information.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnalytical approach\u003c/h2\u003e \u003cp\u003eWe first conducted pairwise correlation testing of seven nutrition indicators to understand the co-existence of various forms of malnutrition. We estimated the median, minimum, maximum of the prevalence of nutrition indicators by food system typology. In order to categorize countries by common nutrition status, we conducted the k-means clustering based on the seven nutrition indicators. The Elbow method, Silhouette method, and Gap statistics method were used to determine the optimal number of clusters (Supplementary Figs.\u0026nbsp;1 and 2). Countries were then grouped by that optimal number. The standardized mean of the nutrition indicators by clusters were calculated and presented. In addition, the distribution of food system typologies for each cluster was presented.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOne hundred and eight countries with all seven nutrition indicators data collected in the past 10 years, alongside food system typology data, were included. The correlation among the seven nutrition indicators was estimated (Supplementary Table 2). Results show that almost all correlations between two indicators are significant, except for the ones between hypertension and anemia, diabetes, or wasting, as well as between wasting and diabetes. The median, minimum and maximum of the prevalence of seven nutrition indicators were calculated by five food system typologies (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The prevalence of stunting, wasting, and women\u0026rsquo;s anemia consistently decreased as the typology shifted from rural and traditional to industrial and consolidated; whereas, the prevalence of adult diabetes, raised blood pressure and adult and childhood overweight exhibited an increase from rural and traditional to modernizing and formalizing, but slightly decreased within the industrial and consolidated typology.\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive Statistics of Nutrition Indicators by Food System Typology\u003c/strong\u003e Cells are expressed as medians (min, max).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eFood System Typology\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRural and Traditional\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInformal and Expanding\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEmerging and Diversifying\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModernizing and Formalizing\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndustrial and Consolidated\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNutrition Indicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStunting, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.4 (12.7, 56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.7 (6.9, 43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.6 (3.4, 22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.8 (1.6, 21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.4 (1.2, 9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWasting, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5 (1.1, 16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.1 (0.8, 18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.7 (0.4, 9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.2 (0.3, 9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4 (0.1, 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChildhood Overweight, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.3 (0.8, 6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.6 (1.3, 18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.7 (3.3, 19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.4 (3.8, 13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.4 (3.1, 12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnemia, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.4 (17.2, 59.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.8 (7.4, 53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.0 (10.6, 52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.6 (8.7, 37.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.5 (11.7, 21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.2 (1.1, 18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.6 (1.9, 30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1 (4.4, 17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1 (5.9, 24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.8 (3.6, 10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRaised Blood Pressure, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.0 (25.7, 41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.9 (28.3, 46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.1 (20.7, 56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.3 (26.2, 49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.6 (26.7, 47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdult Overweight, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.5 (20.0, 38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.4 (18.3, 77.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.0 (30.6, 67.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.3 (42.5, 73.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.5 (30.3, 67.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis found three distinct clusters. Nutrition indicators, food typologies, and cluster information are summarized in Supplementary Table 3. The standardized mean of each nutrition indicator by cluster is presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Cluster 1 countries exhibit the highest prevalence of child stunting and wasting, and anemia in women of reproductive age, median adult raised blood pressure, as well as the lowest prevalence of childhood and adult overweight, alongside adult diabetes. Cluster 2 countries have the lowest prevalence of childhood stunting, median childhood wasting and anemia, the highest prevalence of adult raised blood pressure, childhood and adult overweight and adult diabetes. Cluster 3 countries have the lowest prevalence of childhood wasting, anemia amongst women and adult raised blood pressure; whereas the other four indicators were within the median.\u003c/p\u003e\n\u003cp\u003eThe distribution of food systems by cluster and the countries by cluster are presented in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e respectively. For cluster 1 (n\u0026thinsp;=\u0026thinsp;48), the majority of countries are classified as rural and traditional (65%) and informal and expanding (38%). Two countries, Gabon and Maldives, were classified as emerging and diversifying and one country, Malaysia, as modernizing and formalizing. In cluster 2 (n\u0026thinsp;=\u0026thinsp;40), 40% and 33% of countries were classified as emerging and diversifying and modernizing and formalizing, respectively. Finally, cluster 3 (n\u0026thinsp;=\u0026thinsp;20) was fairly distributed and exhibited all typologies including, informal and expanding (30%), emerging and diversifying (25%), modernizing and formalizing (20%) and industrial and consolidated (20%). One country was included as rural and traditional typology. Global mapping of clusters by food system typologies are presented in Supplementary Fig. 3B.\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"text-align: inherit;\"\u003eTable 2. Clustering of Countries and Food System Typology\u003c/span\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCluster\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFood System Typology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountries\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural and Traditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfghanistan, Burundi, Benin, Burkina Faso, Bangladesh, Cambodia, Central African Republic, Chad, Congo Democratic Republic, Ethiopia, Ghana, Guinea, Guinea-Bissau, Kenya, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Myanmar, Nepal, Niger, Nigeria, Sierra Leone, Sudan, Tanzania, Togo, Zambia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformal and Expanding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAngola, Cameroon, Congo Republic, Cote d\u0026rsquo;Ivoire, Djibouti, Haiti, India, Indonesia, Mauritania, Pakistan, Philippines, Senegal, Sri Lanka, The Gambia, Uganda\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmerging and Diversifying\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGabon, Maldives\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModernizing and Formalizing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformal and Expanding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAzerbaijan, Egypt Arab Republic, Eswatini, Kyrgyz Republic, Namibia, Samoa, Tajikistan, Uzbekistan\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmerging and Diversifying\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlgeria, Armenia, Belize, Fiji, Georgia, Guyana, Iraq, Jamaica, Kazakhstan, Lebanon, Panama, Paraguay, Serbia, South Africa, Suriname, Tunisia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModernizing and Formalizing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbania, Brazil, Bulgaria, Costa Rica, Dominican Republic, Jordan, Kuwait, Latvia, North Macedonia Mongolia, Montenegro, Oman, Saudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustrialized and Consolidated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArgentina, Estonia, Uruguay\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural and Traditional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRwanda\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformal and Expanding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBolivia, Guatemala, Honduras, Morocco, Thailand, Vietnam\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmerging and Diversifying\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina, Ecuador, El Salvador, Mexico, Peru\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModernizing and Formalizing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChile, Colombia, Iran Islamic Republic, Portugal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCluster 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustrialized and Consolidated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelgium, Germany, Korea, Rep., United States\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing a systems lens, this analysis identified three distinct population-level patterns in which a triple burden of malnutrition, or the co-existence of undernutrition, overnutrition and a micronutrient deficiency, exists globally linked to various forms of food system transition. The seven key nutrition indicators changed differently as the typology shifted: the prevalence of stunting, wasting, and female wasting consistently decreased from rural and traditional to industrial and consolidated, while the prevalence of adult diabetes, raised blood pressure, and adult and childhood overweight initially increased from rural and traditional to modernizing and formalizing, before slightly decreasing within the industrial and consolidated typology. Cluster 1 represents nations characterized by the highest prevalence of undernutrition and enduring food insecurity. These issues stem from entrenched poverty and socio-political tensions. In Cluster 2, we observe emerging economies where rapid economic advancement has led to a recent shift towards overweight and obesity, with persistent pockets of undernourishment. However, inadequate governance structures hinder the effective prevention and management of this transition. Cluster 3 denotes countries that began grappling with multiple burdens of malnutrition in the 1990s, as highlighted by Popkin \u003csup\u003e(17)\u003c/sup\u003e. Consequently, these nations have instituted policies and initiatives aimed at addressing NCDs, with varying success. Importantly, the findings suggest that while Cluster 1 encompasses a large proportion of less industrialized countries from a food systems perspective, Clusters 2 and 3 are a mosaic of typologies experiencing similar nutrition burdens. This suggests that food systems are likely one of many systems contributing to nutrition transition and therefore, policy measures and public health programs beyond promotion of food security and healthy diets must be considered to halt these alarming trends.\u003c/p\u003e \u003cp\u003eVarious macro- and micro- drivers shape current trends and future trajectories of nutrition and health outcomes, including food systems that produce and provide diets \u003csup\u003e(18)\u003c/sup\u003e. Some of these forces and drivers are exogenous to food systems that suggest and spur world transition, development, or fragility. The forces vary \u0026ndash; some being political and economical, others being technology and innovation, and others due to the behaviors and incentives of people \u0026ndash; both consumers and the 1.2\u0026nbsp;billion who work in food systems \u003csup\u003e(19)\u003c/sup\u003e. Disruptions and shocks to these forces are impacting food systems, some due to conflict in certain areas of the world, more recently because of the pandemic, but increasingly due to climate-related extreme weather events \u003csup\u003e(20, 21)\u003c/sup\u003e. Globalization has changed and is now more nuanced with different actors and population dynamics.\u003c/p\u003e \u003cp\u003eOur findings are consistent with other global studies on spatio-temporal trends of malnutrition. Since 1990, the number of people with hypertension worldwide has doubled, with most of the increase occurring in low-income and middle-income regions \u003csup\u003e(22)\u003c/sup\u003e. Likewise, diabetes prevalence has increased globally by 129.7% from 1990 to 2017, with the greatest burden in China, India, United States, Indonesia and Mexico \u003csup\u003e(23)\u003c/sup\u003e. At the same time, childhood undernourishment, especially stunting, has seen declines since the 90s, but progress has been uneven across regions \u003csup\u003e(24, 25)\u003c/sup\u003e. In contrast, relatively little progress has been made on child wasting, childhood obesity and anemia in women of reproductive age \u003csup\u003e(24, 26)\u003c/sup\u003e. However, global discourse has recently shifted away from discussing malnutrition in two distinct silos (essentially over- versus under- nutrition). Increasing importance has been placed on viewing undernutrition and overnutrition as interconnected issues sharing common pathophysiological processes that together contribute to the triple burden of malnutrition.\u003c/p\u003e \u003cp\u003eWhile the current study utilized a population-level approach to assess the multiple burdens, there is a lack of consensus in the literature on the definition of the double or triple burden of malnutrition \u003csup\u003e(27)\u003c/sup\u003e. Previous studies have often measured co-existing stunting and overweight within the same individual, or within the same household (i.e., stunted children and overweight mothers) \u003csup\u003e(28, 29)\u003c/sup\u003e. A recent systematic review by Kosaka and Umezaki suggests the national prevalence of double burden households varied from 0.0 to 26.8% by country and year; however, few studies were directly comparable, because of differences in the combinations of undernourished and overweight persons, age ranges, nutritional indicators and cut-off points \u003csup\u003e(30)\u003c/sup\u003e. In addition, fewer studies measure the co-existence of other metrics of under- and over-nutrition, such as micronutrients deficiencies and risk factors of NCDs \u003csup\u003e(31, 32)\u003c/sup\u003e and current evidence is largely based on women of reproductive age and children under 5-years of age, excluding populations such as school-aged children, adult males and elderly, which limits their representativeness.\u003c/p\u003e \u003cp\u003eFrom a monitoring perspective then, the World Health Assembly nutrition targets and routine national surveys must be expanded to measure and report both child and adult under- and over-nutrition within the same household or individual, in order to accurately assess and improve surveillance of malnutrition and address inequities. At the same time, improving the granularity of data on subnational food system typologies is critical to ensure national averages do not mask the nuance that exists within a country. These data are critical to informing priority setting, including the spectrum of practical, targeted and measurable multi-duty actions for nutrition that exist across food, health, water and social protection systems \u003csup\u003e(33)\u003c/sup\u003e. These actions include maternal nutrition and antenatal care, promotion of optimal nutrition in early life, as well as school food policies and programs and regulations on food marketing \u003csup\u003e(34)\u003c/sup\u003e. Countries that prioritize the implementation and scale-up of evidence-based, direct and indirect nutrition policies and programs stand to make great improvements in human capital development and economic productivity, as these interventions generally have very high cost-benefit ratios.\u003c/p\u003e \u003cp\u003eAll in all, our collective effort is required if we are to end all forms of malnutrition. For this goal to be achieved, active engagement within and across a range of sectors and systems is required to result in better outcomes and simultaneously support the achievement of sectoral goals. More investments should be directed towards low- and middle- income countries, where nutrition transition poses the greatest threat to public health due to rapidly changing food systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eBC, HL, PW and JF conceptualized the study. YC and HL conducted analyses. BC, YC, HL, PW and JF wrote the first draft. All authors approved the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eNone to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNotestein FW (1945) Population \u0026mdash; The Long View. 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The Lancet 395, 142\u0026ndash;155.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4657489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4657489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe idea of nutrition transition was critical to conceptualizing patterns of the global burden of malnutrition in line with demographic and epidemiological transitions of the 20\u003csup\u003eth\u003c/sup\u003e century. However, earlier typologies are less reflective of the nuances which characterize today’s nutrition reality. This analysis presents a new paradigm for conceptualizing nutrition transition that better aligns with food system transitions and with evolving population dynamics. Examining seven nutrition indicators (under-5 stunting, wasting and overweight, adult overweight, anemia in women of reproductive age, adult diabetes and adult raised blood pressure) with national-level estimates between 2013-2023, we explore how food system typologies cluster across 108 countries and identify three distinct population-level patterns which describe a triple burden of malnutrition that manifests differently across the globe. The findings suggest that while Cluster 1 encompasses a large proportion of less industrialized countries from a food systems perspective, Clusters 2 and 3 represent a mosaic of typologies experiencing similar nutrition burdens in middle- and higher-income nations. Therefore, the forces shaping global and local food systems are dynamic and interactive, resulting in outcomes that are less linear and distinct than previous concepts allowed for.\u003c/p\u003e","manuscriptTitle":"Nutrition Transition Patterns in the Context of Global Food Systems Transformation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 20:06:18","doi":"10.21203/rs.3.rs-4657489/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a3d9179d-a215-4816-83c8-5aba67555dd2","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34683364,"name":"Scientific community and society/Developing world"},{"id":34683365,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-03-28T14:36:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 20:06:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4657489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4657489","identity":"rs-4657489","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}