metadata: authors: 0: author: Siekmans,Kendra
1: author: Fracassi,Patrizia
2: author: Kato,Tomoko
3: author: Seow,Ti Kian
4: author: Carter,Diana
5: author: Horton,Susan E.
6: author: Dizon,Felipe Jr Fadullon
7: author: Shibata Okamura,Kyoko
count: World
docna: 0: docna: Understanding the Links between Diet
            Quality, Malnutrition, and Economic Costs : An Evidence
            Review for LMICs
docty: Policy Research Working Paper
theme: Urban Infrastructure and Service Delivery,Mitigation,Human Development and Gender,Economic Policy,Food Security,Nutrition,Trade Policy,Fiscal Policy,Environment and Natural Resource Management,Climate change,Urban and Rural Development,Trade,Adaptation,Public Expenditure Policy,Nutrition and Food Security,Urban Development
abstracts: cdata!: Understanding the economic costs
            attributable to unhealthy diets is crucial to inform health
            and agrifood investments in low- and middle-income countries
            experiencing nutrition transition. To review the current
            evidence on the association between diet quality and
            economic costs in low- and middle-income countries, this
            paper first conducted a literature search to identify
            studies that include a dietary exposure, nutrition, or
            health outcome, and a cost estimate. Given the limited
            studies in terms of life stage groups represented, a second
            search was conducted for systematic reviews and
            meta-analyses of observational studies, with effect size
            estimates for the risk of nutrition or health outcomes
            associated with diet quality. Of 21 studies (search 1), most
            were based on the Global Burden of Disease model and
            estimated the fraction of diet-related noncommunicable
            disease outcomes attributable to individual or groups of
            dietary risk factors. The search found 82 systematic reviews
            and meta-analyses (search 2) that estimated the burden of
            malnutrition associated with dietary risk factors. Low
            dietary diversity was associated with increased risk of
            undernutrition and anemia in pregnant women and children.
            Dairy consumption was protective for low birthweight, child
            obesity, and diabetes and hypertension. Low animal source
            food intake increased the risk of anemia and zinc deficiency
            during pregnancy. Unhealthy food consumption, including
            ultra-processed foods and sugar-sweetened beverages,
            increased the risk of overweight/obesity, diabetes, and
            hypertension. Healthy dietary patterns were protective
            during pregnancy for maternal and birth outcomes, and for
            diabetes and hypertension in adults. The results highlight
            gaps in quantifying the contribution of diet quality to
            multiple forms of malnutrition and noncommunicable diseases.
display_title: Understanding the Links between Diet
            Quality, Malnutrition, and Economic Costs : An Evidence
            Review for LMICs
listing_relative_url: /research/2024/04/34295844/understanding-links-between-diet-quality-malnutrition-economic-costs-evidence-review-lmics
topicv3: Food Security m1326439 199,Non-Communicable Disease and Injury
            m1326561 1427,Nutrition m1326779 644
publishtoextweb_dt: 2024-04-04T00:00:00Z
dois: 10.1596/1813-9450-10747
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 65
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model_extractions: Policy Research Working Paper 10747

# Understanding the Links between Diet Quality, Malnutrition, and Economic Costs

An Evidence Review for LMICs _Kendra Siekmans Patrizia Fracassi Tomoko Kato Ti Kian Seow Diana Carter Susan Horton Felipe Dizon Kyoko Shibata Okamura_ Agriculture and Food Global Practice April 2024
Policy Research Working Paper 10747

# **Abstract**

Understanding the economic costs attributable to unhealthy diets is crucial to inform health and agrifood investments in low- and middle-income countries experiencing nutrition transition. To review the current evidence on the association between diet quality and economic costs in low- and middle-income countries, this paper first conducted a literature search to identify studies that include a dietary exposure, nutrition, or health outcome, and a cost estimate. Given the limited studies in terms of life stage groups represented, a second search was conducted for systematic reviews and meta-analyses of observational studies, with effect size estimates for the risk of nutrition or health outcomes associated with diet quality. Of 21 studies (search 1), most were based on the Global Burden of Disease model and estimated the fraction of diet-related noncommunicable disease outcomes attributable to individual or groups of dietary risk factors.

The search found 82 systematic reviews and meta-analyses (search 2) that estimated the burden of malnutrition associated with dietary risk factors. Low dietary diversity was associated with increased risk of undernutrition and anemia in pregnant women and children. Dairy consumption was protective for low birthweight, child obesity, and diabetes and hypertension. Low animal source food intake increased the risk of anemia and zinc deficiency during pregnancy. Unhealthy food consumption, including ultra-processed foods and sugar-sweetened beverages, increased the risk of overweight/obesity, diabetes, and hypertension. Healthy dietary patterns were protective during pregnancy for maternal and birth outcomes, and for diabetes and hypertension in adults. The results highlight gaps in quantifying the contribution of diet quality to multiple forms of malnutrition and noncommunicable diseases.

This paper is a product of the Agriculture and Food Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at kisekmans@nurturedev.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Understanding the Links between Diet Quality, Malnutrition, and Economic Costs: An Evidence Review for LMICs**

Kendra Siekmans<sup>1</sup> , Patrizia Fracassi<sup>2</sup> , Tomoko Kato<sup>3</sup> , Ti Kian Seow<sup>4</sup> , Diana Carter<sup>5</sup> , Susan Horton<sup>6</sup> , Felipe Dizon<sup>7</sup> , and Kyoko Shibata Okamura<sup>8</sup>

**Keywords:** diet, undernutrition, overweight, obesity, non-communicable disease

**JEL classification:** I12 (Health Behavior), I18 (includes public health), Q18 (food policy)

1 Consultant, Food and Nutrition Division, FAO. Corresponding author: ksiekmans@nurturedev.com 2 Food and Nutrition Division, FAO. Patrizia.Fracassi@fao.org 3 Food and Nutrition Division, FAO. Tomoko.Kato@fao.org 4 Food and Nutrition Division, FAO. Tikian.Seow@fao.org 5 Food and Nutrition Division, FAO. Diana.Carter@fao.org 6 University of Waterloo. sehorton@uwaterloo.ca 7 Agriculture and Food, World Bank. fdizon@worldbank.org 8 Health and Nutrition, World Bank. kokamura@worldbank.org

## **Acknowledgments**

The authors would like to thank our colleagues from the ‘Estimating the economic costs of unhealthy diets project’ (under the World Bank ‘Repurposing Agrifood Public Policies and Support for Healthy Diets’ Programmatic Advisory Services and Analytics (PASA)), for their support of related work, including Erkan Ozcelik and Giles HanleyCook from FAO, and Hina Khan Sherwani from the World Bank. We thank Lora Iannotti, Mireya Vilar and Theresa McMenomy for their reviews of the paper. We thank Lynnette Neufeld, Bridget Holmes, Ramani WijesinhaBettoni and participants in a joint World Bank-FAO seminar on September 14, 2023, for helpful comments on earlier drafts. We thank Emily Meier for research assistance.
# Introduction

This paper aims to review and summarize the current evidence on the association between diet quality, malnutrition (in all its forms), and related economic costs in low- and middle-income countries (LMICs). Economic arguments are useful particularly for influencing government policy changes that can support societies to adopt healthier diets. Economic arguments have been widely used to advocate for action against undernutrition and are now increasingly being used to advocate for policies to stem the increase in overweight, obesity, and risk of noncommunicable diseases (NCDs). This follows the shift in concern from undernutrition, initially focused on calorie intake, subsequently on inadequacy of micronutrients, and most recently on overweight and obesity. The term “nutrition transition” has been used to describe big shifts in human diets towards consumption of foods high in fat, salt, sugar, and refined carbohydrates, mostly accompanied by low consumption of fiber, fruits and vegetables.

While a broad array of economic, social and environmental factors affect stunting, the agrifood system is strongly implicated in increases in overweight and obesity and hence NCDs. Very rapid changes in the food system, especially in the food retail environment, have led to higher availability and wider variety of ultra-processed foods (UPF) (Reardon et al. 2021). UPF foods are highly palatable, cheap, ready-to-consume food products that are characteristically energy-dense, fatty, sugary or salty (Monteiro et al. 2013).

Although the nature and pace of the nutrition transition has varied by location and income groups, the lowest income LMICs face severe levels of what is termed the double burden of malnutrition (DBM) as the rapid increases in overweight and obesity are not accompanied by equal reductions in child stunting (Popkin, Corvalan, and Grummer-Strawn 2020). The DBM is defined as the simultaneous manifestation of both undernutrition and overweight and obesity within various contexts, such as within the same country, the same household, or even the same individual (where the individual is both stunted and overweight/obese).

The DBM affects most LMICs and has increased in the poorest LMICs, mainly due to overweight and obesity increases (Popkin, Corvalan, and Grummer-Strawn 2020). Between 1990 and 2022, there has been a transition in many countries from underweight dominance to obesity dominance in the DBM, with growing prevalence of obesity among school-age and adolescent children as well (NCD Risk Factor Collaboration (NCD-RisC) 2024). The work of Barker (2004) on what is termed DOHAD (Developmental Origins and Health and Disease) posits a biological basis for why diets with insufficient appropriate nutrients for pregnant women predispose their children to higher risks of obesity and associated NCDs in adulthood (Wells et al. 2020). Furthermore, with recent global estimates that over half of preschool-aged children and two-thirds of non-pregnant women of reproductive age have at least one of three micronutrient deficiencies (Stevens et al. 2022), most LMICs are dealing in reality with a “triple burden” of malnutrition. Micronutrient deficiencies can occur “at either end of the anthropometric spectrum” as well as in individuals with healthy weights (Mwangome and Prentice 2019). The double and triple burdens of malnutrition render greater complexity for policy interventions (Hawkes et al. 2020).

Earlier economic studies of the cost of malnutrition in LMICs have focused on the effects of undernutrition, while those in high income countries focused on overweight and obesity. The nutrition transition and the DBM have led to a growth in studies of the economic cost of malnutrition in all its forms in LMICs (Schneider et al. 2020). However, associating cost with nutritional status does not directly inform policy, as there are multiple factors which determine nutritional status. Recent work has increased focus on the health and economic consequences of diet since this provides an entry point to examine agrifood policies.

A growing number of economic studies attempt to assess the economic cost of unhealthy diets. Candari and colleagues (2017) reported just six studies from Australia, China, the United Kingdom and the United States that generated evidence on the costs of unhealthy diets, but these varied widely in their definition of unhealthy diets and reported a wide range of cost estimates which were sensitive to the study methodology. The majority of recent studies rely on impacts on health using data from the Global Burden of Disease (GBD) project (Institute for Health Metrics and Evaluation, IHME). These include studies by The Food and Land Use Coalition (2019) and FAO et al. (2023). These estimates face methodological issues discussed elsewhere (Beal et al. 2021; Stanton et al. 2022; Horton et al. 2024).

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To begin, we present a conceptual framework that describes the pathways from unhealthy diets to economic costs. Based on this framework, we define the three components of importance to estimating these costs (dietary exposure, nutrition and health outcomes, and economic impacts on individuals and society) and conduct a scoping literature review to examine the range and characteristics of the evidence on this topic. We assess the adequacy of the evidence by life-stage group with a focus on the most vulnerable periods, namely during pregnancy and lactation, early childhood (from birth to 23 months of age), school age and adolescence and older adulthood (beyond 65 years of age). As a result, we identify the knowledge gaps that remain. We then provide key findings that can inform modeling efforts to estimate the nutrition and health economic costs of unhealthy diets, highlighting limitations in the evidence that need to be considered when interpreting the results, with the aim of informing policy.

# Conceptual framework and definitions

**Figure 1** shows the conceptual framework that is guiding the review, describing the pathways leading from diet quality to various nutrition and health outcomes, that in turn have economic impacts on individuals and their society. There are different pathways for individuals in different stages in their life cycle, since the short- and longterm effects of diet quality may be different at each stage of development (Gernand et al. 2016). We next discuss in turn exposures, outcomes and impacts.

## **_Exposure of interest_**

**First, the starting point and exposure of interest is diet quality which can be defined as ‘healthy’ or ‘unhealthy’.** A ‘ **healthy diet** ’ is defined as one which promotes growth and development and prevents malnutrition in all its forms (FAO and WHO 2019). Dietary patterns tend to be highly contextual and depend on food availability, access, affordability, preferences, cultures and traditions. While there can be many healthful dietary patterns, the principles of what constitutes a healthy diet are universal: i) **adequacy** in amount of energy (calories) and essential nutrients, ii) **diversity** of food groups and within food groups, iii) **balance** of energy from protein, fats and carbohydrates; and iv) **moderation** of unhealthy foods and components (Verger et al. 2023). The State of Food and Agriculture flagship report (SOFA 2023) defines ‘ **unhealthy diets’** as those that do not meet one or more of the principles of healthy diets (adequacy, diversity, balance, and moderation) and are one of the primary drivers of all forms of malnutrition, and related morbidities (FAO et al. 2023).

The WHO dietary recommendations are global reference points for preventing undernutrition, including micronutrient deficiencies, and reducing NCD risks (WHO 2020). WHO recommends exclusive breastfeeding for infants during the first 6 months of their life, followed by continued breastfeeding with appropriate complementary foods for up to 2 years or beyond. The 2023 WHO guidelines for complementary feeding recommend that not breastfed infants 6–23 months can be fed with either animal milk or commercial formula milk up to 11 months while follow-up commercial milk is not recommended. The same guidelines recommend that infants and children 6-23 months should consume a diverse diet including daily consumption of animal source food, fruits and vegetables and frequent consumption of pulses, nuts and seeds, particularly when meat, fish or eggs and vegetables are limited in the diets. The new guidelines also provide specific recommendations on food that should be avoided or consumed in moderation. The new guidelines recommend that infant and young children 6-23 months should not consume foods high in sugar, salt and trans fats and sugar-sweetened beverages and should limit consumption of 100% fruit juice (WHO 2023a).

There are no specific WHO dietary recommendations for other age groups but the universally recognized guidelines for all age groups do emphasize increasing intakes of fruits, vegetables, legumes, nuts and whole grains; limiting energy intake from free sugars and total fats; consuming unsaturated rather than saturated or trans fats; and limiting salt intake. The Global Burden of Disease (GBD) study, which seeks to quantify the contribution of dietrelated risks to NCD burden, aligns its definition of a ‘healthy diet’ with WHO recommendations but includes additional risk factors like any consumption of processed meat and high consumption of red meat (Kumanyika et al. 2020). Characterization of healthy diets based on dietary patterns (rather than individual foods or nutrients) 3
better reflects what people eat. The evidence to date on the health effects of dietary patterns is consistent with the WHO and GBD findings, suggesting a focus on plant-based diets and lower level of food processing (FAO and WHO 2019) **.**

**While there is evidence of diets changing globally over time, there are no harmonized metrics monitoring diet quality or how diets are evolving** (Verger et al. 2023) **. Diets are assessed in a variety of ways, based on the population of interest and type of data available** . For example, breastfeeding practices are measured for infants and children 0-23 months of age. Measures distinguish those who are exclusively breastfed up to six months and continue to be breastfed while receiving complementary food up to 23 months from those who entirely stop breastfeeding at any time and receive instead breast-milk substitutes such as commercial infant formula and/or animal milk. For LMICs, the micronutrient adequacy of the diet is estimated using updated standardized methods for the assessment of minimum diet diversity (MDD) in children 6-23 months of age (WHO and UNICEF 2021) and minimum diet diversity in non-pregnant women of reproductive age (15-49 years, MDD-W) (FAO 2021). However, diet diversity is also assessed among other age groups (Arimond et al. 2021) and pregnant women (e.g. by the most recent Demographic Health Surveys). Since the consensus on standardized metrics for monitoring healthy diets is just emerging (Verger et al. 2023), current evidence focuses on dietary risk factors associated with negative nutrition and health outcomes, such as inadequate consumption of fruits, vegetables, legumes, whole grains, meat, dairy and fiber, or overconsumption of salt, trans-fatty acids, red meat, processed meat and sugarsweetened beverages.

# **_Outcomes of interest_**

**Second, the outcomes of interest are all forms of malnutrition and diet-related co-morbidities such as type II diabetes and hypertension** defined as including undernutrition (such as child wasting, child stunting, child/adult thinness or child/adult micronutrient deficiencies), overweight and obesity, and diet-related NCDs such as heart disease, stroke, diabetes and certain cancers (WHO 2023b). As overweight and obesity increase in poorer populations of LMICs, the coexistence of undernutrition (e.g. stunting) and overweight or obesity in the same individual, one form of the ‘double burden of malnutrition’, is also increasing (Popkin, Corvalan, and GrummerStrawn 2020). The Developmental Origins of Health and Disease (DOHAD) theory proposes a biological basis for why diets with inadequate energy and nutrient levels during early pre-conception and pregnancy predispose offspring to higher risks of obesity and associated NCDs in adulthood (Wells et al. 2020). Coexisting micronutrient deficiencies, estimated to affect over half of preschool-aged children and two-thirds of non-pregnant women of reproductive age (Stevens et al. 2022), contribute to a “triple burden” of malnutrition. Furthermore, co-morbidities associated with overweight and obesity, such as type II diabetes (T2DM) and hypertension are common (Agrawal and Agrawal 2016), including among children (Obita and Alkhatib 2022).

Malnutrition status based on anthropometric measures of weight and height are defined by age group. In children under five, we focus on stunting (height-for-age z-score <-2 SD); wasting (weight-for-height z-score <-2 SD); and overweight (weight-for-height z-score >2 SD) (de Onis 2006). In older children and adolescents, measures of thinness (BMI for age z-score <-2 SD) and obesity (BMI for age >+2 SD) are most relevant (Butte, Garza, and de Onis 2007). Similarly, in individuals 20+ years, BMI-based measures of thinness (BMI <18.5); overweight (BMI 25-29) and obesity (BMI 30+) are used to describe nutrition outcomes of concern (Abarca-Gómez et al. 2017). Micronutrient deficiencies commonly are defined using biochemical measures in children and adults, including, for example iron deficiency (inflammation-adjusted serum ferritin concentration), vitamin A deficiency (inflammation-adjusted serum retinol concentration) and anemia (hemoglobin concentration). Diet-related NCDs are defined using clinical measures, including diabetes (raised fasting blood glucose levels) and hypertension (raised blood pressure) as the two most critical metabolic risk factors for a range of diseases (World Cancer Research Fund International and The NCD Alliance 2014). We chose diabetes and hypertension as the diagnosis was linked to the highest metabolic risk factors associated with raised blood glucose and raised blood pressure aligned with the WHO STEPwise approach to NCD risk factor surveillance in LMICs (WHO, n.d.).

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## **_Impact of interest_**

**Third, the impact of these health and nutrition outcomes is the economic costs to individuals and society.** Economic costs are defined as the direct and indirect impacts of malnutrition on individual and national economic activity, including the costs of preventable mortality, costs of medical care and income lost to ill health, costs of impaired physical growth, and costs and losses linked to impaired cognitive growth (Global Panel 2016).

The measures of economic impact (i.e. cost) include disability adjusted life years (DALYs), the monetary value of losses of goods and services production in a country each year (gross domestic product, GDP), as well as various measures of human capital, such as years of educational attainment and value of future income/wage earnings. Studies on the cost of diet-related NCDs also frequently report the annual per capita health care costs associated with health services (hospitalization, consultations) and treatment of NCDs (medications), as well as indirect health care costs, such as work absenteeism and reduced work productivity.

# Literature search approach

We chose to conduct a scoping review as this type of review is useful for presenting a broad overview of the evidence for a specific topic, examining emerging aspects of research on that topic, clarifying key concepts and identifying gaps (Tricco et al. 2016). The scoping review aimed to assess the extent to which research on the contribution of unhealthy diets to nutrition and health outcomes and their associated economic costs in LMICs has been undertaken. This implied mapping the types of evidence available on which subpopulations (life stage groups) are represented, which diet metrics (or specific dietary factors) are used, and which outcome types and economic costs are considered.

As a first step, we conducted a comprehensive literature search to identify primary research articles that included all three components described in our conceptual framework, namely: a diet metric (or a specific dietary factor), a nutrition or health outcome, and economic cost. These criteria yielded a body of literature that was limited in scope, especially in terms of the life stage groups represented.

For this reason, we conducted a second comprehensive literature search that expanded the inclusion criteria to include systematic review papers that assessed the evidence for the contribution of a diet exposure measure to a nutrition or health outcome described in our conceptual framework and reported an estimate of effect size based on a meta-analysis of observational study data. This was expected to help the mapping of evidence from the first literature search in two ways. First, the biggest gap from the first search was the lack of evidence on the contribution of diet to nutrition and health outcomes for the various life stage groups (beyond adulthood). Second, while there are a reasonable number of studies which provide estimates of the cost of undernutrition and overweight/obesity in LMICs (Horton et al. 2024), there are very few studies that incorporate the contribution of the diet to these outcomes. Since conducting a full literature review on the diet and outcome components would yield a very large body of evidence on these relationships without necessarily providing the desired information on the effects, we chose to limit the second search to studies that consolidated this body of evidence in a systematic way, including calculation of effect sizes. See **Table 1** for a PICO summary of the elements that guided Search 2.

## **Eligibility criteria**

Peer-reviewed journal articles were included if they were: published during 2000-2023 (arbitrary cutoff used to focus on evidence produced with a recent timeframe since dietary patterns are changing rapidly in LMICs), written in English, and involved human participants of any age living in a low- or middle-income country (using the World <u>Bank’s classification</u> in 2023 fiscal year). Articles were excluded if they were case reports, editorials, letters to editors, comments, conference abstracts, short communications, or qualitative studies.

5
Articles were included if they assessed a dietary exposure that was food-based (as opposed to specific nutrients), including breastmilk, food groups or categories (e.g. animal source foods, dairy products, sugar-sweetened beverages) or measures of diet quality (e.g. diet adequacy, diet diversity and different ways of defining healthy or unhealthy dietary patterns, which focus more on balance and moderation). We included studies assessing salt intake (not just sodium in foods) as condiment/seasoning. Articles were excluded if the dietary exposure was a specific nutrient (e.g. omega-3 PUFA, cholesterol, protein), therapeutic diet (gluten-free, DASH diet), uncommon individual food item (e.g. sesame seed), micronutrient supplement (including micronutrient powders, smallquantity lipid nutrient supplements), therapeutic food (e.g. RUTF), food supplement or fortified food product. Dietary behaviours (e.g. initiation of breastfeeding, frequency of feeding, fast food restaurant frequency), and food insecurity were not considered to be a dietary exposure.

Articles were included if they assessed the relationship between the diet exposure and one or more nutrition or health outcomes relevant to our theoretical framework, including stunting, wasting, overweight, obesity, anemia, micronutrient deficiencies, raised fasting blood glucose, raised blood pressure, and maternal and neonatal outcomes during pregnancy and the perinatal period (e.g. gestational diabetes, gestational hypertension and the hypertensive disorder known as pre-eclampsia, preterm birth, low birthweight, small-for-gestational-age). Other disease outcomes associated with dietary intake were not included (e.g. depression, asthma, kidney or liver disease). We focused on two of the most critical metabolic risk factors for a range of diseases, namely high blood pressure (referred to hereafter as hypertension) and high blood glucose (referred to hereafter as diabetes) (World Cancer Research Fund International and The NCD Alliance 2014). In much of the literature found, high blood glucose and type 2 diabetes mellitus (T2DM) were used interchangeably. We did not include type 1 diabetes mellitus, an autoimmune condition that is not diet related. In the second literature search, articles were further limited to original systematic review articles that included a meta-analysis of observational study data (e.g. crosssectional and prospective or retrospective cohort studies). Systematic review articles were excluded if they reported only on intervention trials (RCTs) or did not report an estimate of the effect size or odds ratio for the risk of the outcome associated with the diet exposure. We did not include experimental studies because we were interested in estimates based on normal population dietary intake. Articles were excluded if they reported evidence specific to high-income countries only.

# **Information sources**

To identify relevant articles, we conducted the first search in April 2023 using the Web of Science Core Collection database and Cochrane Database of Systematic Reviews. We conducted the second search in January 2024 using the same two databases. The search strategies were developed by life stage group and health/nutrition outcome, with search terms for each of the three components (diet exposure, outcome and cost) combined to identify relevant studies. The list of search terms for each life stage group and health/nutrition outcome are shown in **Annex 1.** The electronic database search was supplemented by scanning the reference lists of selected articles and other relevant reviews.

A team of six reviewers assessed the eligibility of the search results, with two reviewers for each search list (by life stage group/outcome). In the first step, each reviewer independently screened the list of articles based on the title and abstract. In the second step, one reviewer scanned the full text of the article and noted the presence or absence of each of the components required by the inclusion criteria. Disagreements on study selection were resolved by consensus and discussion with other reviewers.

# **Approach to data charting and synthesis of results**

A standard data charting table was jointly developed by two reviewers and data from eligible studies were charted by the lead reviewer, grouped by life stage. The table columns captured the relevant information on key study characteristics (e.g. author, publication year, study design, sample size), age and geographic location of study participants, type of diet exposure and metric used, type of nutrition/health outcome(s) and measure used, type of economic cost and measure used, and relevant key findings (e.g. strength of association observed, economic cost 6
estimate). Since the purpose of this scoping review was to provide an overview of the existing evidence regardless of methodological quality or risk of bias, the articles included were not critically appraised (Tricco et al. 2018).

# Results

## Search 1: Evidence on the link between diet quality, health/nutrition outcomes and economic costs (all three components)

### **Search results and characteristics of selected studies**

The study selection process for the first literature search objective is shown in **Figure 2** . The total number of records screened, after removing duplicates was 704; 142 full texts were screened. We identified a total of 20 studies that included data from LMICs and estimated the burden of unhealthy food choice by quantifying the impact of individual dietary factors (dairy products, salt, sugar, sugar-sweetened beverages, ultra-processed foods) or poor-quality diets (measured by healthy eating indices) on diet-related NCD outcomes (cardio-vascular diseases, diabetes, chronic disease) (see **Table 2** ). A comparison of key findings in terms of mortality, DALYs and health care costs are summarized in **Table 3** .

Most of the studies were based on the GBD data and methodology. Several studies estimated the cost (mortality and DALYs) of cardiovascular disease or the NCD burden attributable to the dietary risk factors, using the GBD dataset for 204 countries (B. Zhang et al. 2023; Dong et al. 2022; Qiao et al. 2022) or in specific country contexts (Brazil, Mexico) (Dávila-Cervantes 2020; Machado et al. 2022). Globally, in 2019, 6.9 million CVD deaths and 153.2 million CVD DALYs were attributable to dietary risk factors, with high sodium, low whole grains, low legumes, low fruits and high red meat being the top risks (B. Zhang et al. 2023; Dong et al. 2022). This increased to 7.9 million deaths and 187.7 million DALYs globally when including other NCDs such as cancers and T2DM (Qiao et al. 2022). Using the same dataset at country level for Brazil, Machado and colleagues (2022) show a lower age-standardized death rate (65.3 per 100,000 in Brazil vs. 101 globally in 2019) for NCDs attributable to dietary risks.

Four studies (three in Brazil, one in Costa Rica) estimated the costs associated with excess salt/sodium consumption, one using the GBD dataset (Guedes et al. 2022) and the others assessed health care costs associated with hypertension (Nilson, da Silva, and Jaime 2020; Nilson et al. 2020; Vega-Solano et al. 2023). In 2017, the burden of hypertension and CVDs associated with excessive salt intake in Brazil accounted for USD 192.1 million in health care costs (hospitalization, outpatient care and medication for hypertension) and USD 752.7 million in productivity losses due to premature deaths (Nilson et al. 2020). In Costa Rica, excessive salt intake was estimated to cost USD 15.1 million in health care costs and USD 6.8 million in annual productivity costs (GDP losses) (VegaSolano et al. 2023).

An additional eight studies were found that estimated costs (usually mortality and DALYs) associated with specific dietary risk factors such as diets high in red meat (Liu et al. 2022) or processed meat (Rocha et al. 2023), high in sugar-sweetened beverage intake (Bardach et al. 2023; Li et al. 2021), or low in fiber (Zhuo et al. 2022).

Two studies modeled the savings in health care costs associated with improvements in population dietary intake. Basto-Abreu and colleagues (2020) estimated that a 36.8 kcal/day reduction in energy intake from beverages and snacks would result in a 5 percentage point decrease over five years in adult obesity prevalence and save USD 1.84 billion in direct and indirect health care costs in Mexico. In the Islamic Republic of Iran, the estimated reduction in CVD and T2DM incidence due to optimal dairy food consumption would result in $0.43 per capita savings in health care costs over one year and $190.25 in 20 years (Javanbakht et al. 2018).

Three studies included children in their study population. In a study of SSB consumption in four countries (Argentina, Brazil, Trinidad & Tobago, El Salvador), Alcaraz et al. estimated that SSB consumption in one year was associated with 12% of overweight and obesity cases in children and adolescents, as well as 2.8% of those in adults. The total cost associated with SSB consumption was 18,000 deaths, 0.5 million DALYs and USD 2 billion in 7
direct medical costs. Bardach and colleagues (2023) produced similar estimates for children, adolescents and adults in Argentina alone.

Walters and colleagues (2019) estimated the global human and economic costs of not breastfeeding according to recommendations, including 974,956 cases of childhood obesity each year, 595,379 childhood deaths per year (659 months) from diarrhea and pneumonia, and 98,243 women’s deaths from cancer (breast and ovarian) and T2DM. The total annual global losses associated with inadequate breastfeeding are estimated to be between US$257 billion and US$341 billion (0.37%-0.70% of global gross national income), including US$1.1 billion in health care costs, US$53.7 billion in economic losses due to premature mortality, and US$285.4 billion in cognitive losses.

# Search 2: Evidence on the link between diet quality and health/nutrition outcomes **Search results and characteristics of selected studies**

The study selection process for the second literature search objective is shown in **Figure 3** . The total number of records screened, after removing duplicates was 2,335 and 222 full texts were screened. We identified a total of 82 systematic reviews with meta-analyses (SRM) that included data from LMICs and estimated the burden of nutrition and/or health outcomes associated with specific dietary risk factors. The following sections provide an overview of the evidence available for the diet’s contribution to nutrition and health outcomes, organized by life-stage group.

## Pregnant and lactating women

A total of 19 SRM articles were found that included women of reproductive age in LMICs and assessed the association of diet with maternal and/or neonatal nutrition and health outcomes. **See Table 4.**

### **Maternal nutrition outcomes**

Five (5) SRM assessed risk factors for maternal nutrition outcomes, including anemia (Seid et al. 2023; Geta, Gebremedhin, and Omigbodun 2022; J. Zhang et al. 2022), thinness (Getaneh et al. 2021), and zinc deficiency (Berhe, Gebrearegay, and Gebremariam 2019). Four of the five were based either exclusively or dominantly on studies in Ethiopia, with extensive overlap of studies included. All five SRM included dietary diversity as a risk factor and proxy for diet quality during pregnancy. Low diet diversity was associated with higher risk of maternal anemia, with the effect size estimates ranging from 2.15 to 2.61. However, there was heterogeneity in the number of food groups and dietary diversity score (DDS) cutoffs used to define low or inadequate diet diversity across the studies included in the SRM.

Consumption of specific food groups (e.g. meat, dark green leafy vegetables) relevant to anemia as an outcome was assessed in two studies. Risk of anemia was over two times higher among pregnant women eating meat ≤1 time per week (OR 2.02; 95% CI 1.55, 2.50) and those eating vegetables ≤3 times per week (OR 2.97; 95% CI 1.59, 4.34) (J. Zhang et al. 2022). In a review of four studies from Ethiopia, zinc deficiency was also associated with a low intake of ASF (OR 2.57; 95% CI 1.80, 3.66) and low dietary diversity (OR 2.12; 95% CI 1.28, 3.53) (Berhe et al. 2022).

### **Maternal health outcomes**

Twelve (12) SRM summarized the evidence for the contribution of diet during or before pregnancy to maternal health outcomes, including gestational diabetes, hypertension, pre-eclampsia and gestational weight gain. Three of these assessed the association of maternal adherence to a ‘healthy’ or ‘unhealthy’ dietary pattern (Abdollahi et al. 2021; Chia et al. 2019; Kibret et al. 2019; Haghighatdoost et al. 2023; X. Gao et al. 2023; Hassani Zadeh, Boffetta, and Hosseinzadeh 2020) with various maternal health outcomes during pregnancy. Paula et al. (2022) estimated the risks during pregnancy of UPF-rich diet consumption.

**_Gestational diabetes mellitus (GDM)_** – A total of 25 effect estimates for GDM were recorded. Dietary risk factors for GDM included low diet diversity in two Ethiopian studies (effect size 1.51; 95% CI 1.25, 1.83) (Beyene et al. 2023) and unhealthy food intake, including fried food (Cui et al. 2023), fast food (Cui et al. 2023; Quan et al. 2021), red and processed meat (Cui et al. 2023; Quan et al. 2021), and a diet rich in UPF (Paula et al. 2022). Women

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consuming a ‘western’ dietary pattern were at higher risk of GDM (RR 1.27; 95% CI 1.03, 1.56) in one meta-analysis of 13 cohort studies (Hassani Zadeh, Boffetta, and Hosseinzadeh 2020) with a similar effect size that was marginally non-significant in 2 other meta-analyses due to the lower confidence limit (Haghighatdoost et al. 2023; Quan et al. 2021).

Higher maternal adherence to a healthy diet (defined in various ways) was protective for GDM in 10 of 11 effect estimates (including between 6 and 26 studies), with the effect size ranging from 0.39 (95% CI 0.31, 0.48) for a high quality diet that adhered to national dietary guidelines to 0.86 (95% CI 0.76, 0.96) for a healthy dietary pattern rich in fruits, vegetables and whole grains (Haghighatdoost et al. 2023). Higher quality diet, as measured by higher adherence to Mediterranean, DASH, AHEI or plant-based diets, also was associated with lower risk of gestational diabetes (range of OR 0.51-0.66).

**_Hypertensive disorders of pregnancy_** - Higher maternal adherence to a healthy diet was associated with lower risk of gestational hypertension (OR 0.86) (Abdollahi et al. 2021) and pre-eclampsia (OR 0.78) (Kibret et al. 2019). Risk of pre-eclampsia was also lower among pregnant women consuming adequate vegetables and fruit, based on 4 and 5 studies in LMICs, respectively (Kinshella et al. 2021). Conversely, unhealthy dietary pattern adherence increased the odds of gestational hypertensive disorders (OR 1.23) (Abdollahi et al. 2021) and maternal diets rich in UPF also were associated with increased risk of pre-eclampsia (OR 1.28) (Paula et al. 2022).

## **Birth outcomes**

Seven (7) SRM summarized the evidence for the contribution of diet during pregnancy to neonatal birth outcomes such as preterm birth, birth weight/length, small/large-for-gestational-age, and low birthweight (LBW). The odds of preterm birth were lower among pregnant women with healthy dietary pattern adherence, with estimates from meta-analyses of 6-10 studies ranging from 0.44 to 0.79 (Abdollahi et al. 2021; Chia et al. 2019; Kibret et al. 2019).

Evidence was also available on the association of maternal diet characteristics to the infant’s birth weight and length. Low dietary diversity during pregnancy was associated with an increased risk of having a LBW baby (OR 2.04) based on 9 studies in Africa (Seid et al. 2023). An _unhealthy_ dietary pattern (high intake of refined grains, processed meat, and foods high in saturated fat or sugar) was associated with lower birth weight (mean difference -40 g; 95% CI -61, -20) in 3 studies but the pooled effect size for birth weight among mothers with high adherence to _healthy_ dietary patterns during pregnancy in 13 studies (n=25,499) was close to zero (Chia et al. 2019). Yet higher maternal adherence to a healthy diet was associated with higher birth weight (mean difference +0.19 g; 95% CI 0.05, 0.32) in a meta-analysis of 15 studies (n=75,041) by Abdollahi et al. (2021) as well as lower risk of LBW (OR 0.72; 0.53, 0.97), based on 7 prospective cohort studies. Milk consumption during pregnancy was associated with higher birth weight (mean diff =51.0 g; 95% CI 24.7, 77.3) and birth length (mean diff 0.33 cm; 95% CI 0.03, 0.64), as well as lower risk of small-for-gestational-age (OR 0.69; 95% CI 0.56, 0.84) and LBW (OR 0.63; 95% CI 0.48, 0.84) (Pérez-Roncero et al. 2020).

# Children < 5 years of age

Evidence on the risk of malnutrition associated with nutrient-inadequate and unhealthy diets for children less than five years of age includes nine (9) SRM articles with effect size estimates (see **Table 5** ). Diet diversity is a recommended measure of nutrient adequacy for this age group in LMICs. We found only three SRMs, all based on studies from Ethiopia, that looked at the association of low diet diversity in children < 5 years with stunting (Abdulahi et al. 2017) and anemia (Azmeraw et al. 2023; Belachew and Tewabe 2020). The evidence for dietary risk factors for overweight and obesity in children <5 years was also very limited, with one SRM on breastfeeding and three SRMs that included overweight and/or obesity among children 1-21 years as an outcome. There is strong evidence across 159 studies that breastfeeding is protective against overweight or obesity among children 1-9 years (pooled OR 0.73, 95% CI 0.71, 0.76), with subgroup analysis showing an even higher effect size for LMICs (OR 0.70; 95% CI 0.64, 0.78) (Horta et al. 2023). Breastfed children are also at lower risk for diabetes in later life, with a 9
higher protective effect among adolescents 10-19 years (OR 0.49; 95% CI 0.38, 0.63) compared to adults 20+ years (OR 0.77; 95% CI 0.66, 0.90) (Horta and de Lima 2019).

Based on cross-sectional studies, total dairy product (including milk, yogurt and cheese) consumption in children 2- 21 years is associated with a reduced risk of obesity (OR 0.66; 95% CI 0.48, 0.91) but not overweight (Babio et al. 2022). In a meta-analysis of 14 studies (11 cross-sectional and 3 cohort), regular consumption of _whole_ milk (compared to reduced fat milk) was associated with a lower risk of overweight and obesity among children 1-18 years (OR 0.61; 95% CI 0.52, 0.72) (Vanderhout et al. 2020).

High SSB intake is also associated with increased BMI in children 2-18 years, with a mean difference of 0.75 kg/m2 (95% CI 0.35, 1.15), higher waist circumference (WMD 2.35 cm; 95% CI, 1.34, 3.37; p = 0.016) and higher body fat percentage (WMD: 2.81; 95% CI 2.21–3.41; p < 0.001) (Abbasalizad Farhangi et al. 2022).

# Children 5+ years of age

Our search identified 16 SRM on the risk of malnutrition associated with dietary intake in older children, with four overlapping with children <5 years and one overlapping with adults (see **Table 6** ). As with children <5 years, there is evidence that low diet diversity among children 5-18 years across six LMICs is associated with increased risk of stunting (OR 1.43; 95% CI 1.08, 1.89), and wasting (OR 2.18; 95% CI 1.41, 3.36), but not thinness (Zeinalabedini et al. 2023). Low diet diversity is also associated with increased risk of low BMI-for-age (<-2 SD) in adolescents in Ethiopia (OR 1.95; 95% CI 1.31, 2.92) (Berhe et al. 2019). Low diet diversity increases the odds of anemia among adolescent girls in Ethiopia, with effect size estimates ranging from 1.35-2.81 (Berhe et al. 2022; Habtegiorgis et al. 2022; Endale et al. 2022). Based on two studies from Ethiopia, low diet diversity is also associated with increased risk of overweight and obesity (OR 2.26; 95% CI 1.28, 3.99) (Gezaw et al. 2023a).

Several SRM have summarized the evidence on the contribution of unhealthy diets, including SSB intake, to the growing burden of overweight and obesity in children 5-19 years. Higher adherence to an unhealthy dietary pattern (mainly represented by red and processed meat, confectionery and bakery items, full-fat dairy products, refined grains, desserts and candies) was associated with higher BMI (mean diff 0.57 kg/m2 (95% CI 0.51, 0.63) and waist circumference (mean diff 0.57 cm (95% CI 0.47, 0·67) in children and adolescents 7-19 years (Cunha et al. 2018). SSB consumption, in particular, has been shown to increase the odds of overweight and obesity, with an effect size of 1.2 in two very large meta-analyses (Jakobsen, Brader, and Bruun 2023; Poorolajal et al. 2020). As stated above for children <5 years, high SSB intake is associated with higher BMI, body fat percentage and waist circumference (Abbasalizad Farhangi et al. 2022). Other unhealthy foods associated with overweight/obesity in children and adolescents include fast food (OR 1.17; 95% CI 1.07, 1.28) and refined grains (OR 1.28; 95% CI 1.05, 1.56) (Jakobsen, Brader, and Bruun 2023).

Healthy dietary patterns (mostly composed of legumes, vegetables, fruit, fish, low-fat dairy products, nuts, olive oil and others) have been shown to be protective for overweight and obesity in children 7-19 years, associated with lower mean BMI (-0.41 kg/m2; 95% CI -0.46, -0.36) and waist circumference (-0.43 cm; 95% CI -0.52, -0.33) (Cunha et al. 2018). Milk and dairy product consumption are also associated with reduced risk of overweight and obesity in children of all ages, with effect sizes ranging from 0.54 to 0.87 (Babio et al. 2022; Vanderhout et al. 2020; W. Wang, Wu, and Zhang 2016).

# Adults

Our search identified 44 SRM articles that assessed the risk of overweight and obesity, T2DM or hypertension associated with dietary intake in adults (see **Table 7** ).

## **Nutrition outcomes**

Only one SRM was found that looked at a micronutrient-related outcome in adults. Haider et al. (2018) found that adult vegetarians had significantly lower serum ferritin concentration compared to non-vegetarians.

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Dietary risk factors for overweight and obesity outcomes were assessed in 25 meta-analyses across 15 SRM articles, with the diet exposure varying from dietary diversity to healthy/unhealthy dietary patterns, as well as consumption of specific food groups, including UPF, SSB, fried foods, red and processed meat, dairy products, fruit and vegetables. A healthy dietary pattern was associated with reduced risk of overweight and obesity (OR 0.64) (Mu et al. 2017). There was no evidence of an association with dietary diversity (Qorbani et al. 2022; SalehiAbargouei et al. 2016). Vegetarian and vegan diets were associated with lower BMI compared to omnivore diets (Dinu et al. 2017). Higher consumption of fruits and vegetables was associated with lower risk of overweight and obesity as well as lower waist circumference and body weight (Schwingshackl et al. 2015). Total dairy consumption also was protective in multiple studies (Feng et al. 2022; Schwingshackl et al. 2016; W. Wang, Wu, and Zhang 2016).

Evidence for dietary risk factors for higher overweight and obesity included fried food consumption (OR 1.16) as well as high consumption of SSB (RR 1.12) (Qin et al. 2020). High UPF intake was consistently associated with increased risk of overweight and obesity across four SRM, as a combined outcome (OR 1.39) (Pagliai et al. 2021) or for overweight (range OR 1.02-1.36) and obesity (range OR 1.26-1.55) (Askari et al. 2020; Lane et al. 2021; Moradi et al. 2023). While red meat consumption was not associated with obesity in a meta-analysis that included mostly HICs and only the Islamic Republic of Iran, red and processed meat intake was associated with increased risk of obesity (OR 1.37) and higher mean waist circumference and BMI (Rouhani et al. 2014). High adherence to an unhealthy ‘western’ dietary pattern was a strong risk factor for overweight/obesity in one meta-analysis of 18 studies in HICs and LMICs (OR 1.65; 95% CI 1.45, 1.87) (Mu et al. 2017) but not in a meta-analysis of 8 studies from China (OR 1.34, 95% CI 0.98, 1.84) (Jiang et al. 2022).

## **Health outcomes – diabetes and hypertension**

The SRM for adults included 24 meta-analyses with T2DM and 11 with hypertension as the outcome. In the only study that summarized the evidence for dietary diversity, there was no evidence of an association with cardiometabolic risk factors, including T2DM and hypertension (Qorbani et al. 2022). High adherence to healthy dietary patterns is protective for both T2DM and hypertension, with relative risk estimates in the range of 0.79-0.86 for T2DM (Alhazmi et al. 2014; Esposito et al. 2014; McEvoy et al. 2014; Morze et al. 2020; Maghsoudi, Ghiasvand, and Salehi-Abargouei 2016). Adherence to a vegetarian diet also was associated with lower blood glucose levels (Dinu et al. 2017) and risk of T2DM (Lee and Park 2017). The odds of hypertension were lower (OR 0.87; 95% CI 0.78, 0.98) among adults with higher adherence to a Mediterranean diet(Cowell et al. 2021). Total dairy consumption was also associated with reduced risk of T2DM and hypertension, although there were very small numbers of LMIC studies included in these estimates (Chen et al. 2022; Feng et al. 2022; Mishali et al. 2019; Khoramdad et al. 2017; D. Gao et al. 2013).

In contrast, high consumption of unhealthy foods and beverages, including UPF and SSB was associated with increased risk of T2DM and hypertension. Relative risks for diabetes incidence associated with SSB intake ranged from 1.27 to 1.38 (Neelakantan et al. 2021; B. Li et al. 2023). Adherence to unhealthy dietary patten also increased the risk of T2DM (Alhazmi et al. 2014; Maghsoudi, Ghiasvand, and Salehi-Abargouei 2016; McEvoy et al. 2014). UPF consumption significantly increased the risk of hypertension (OR 1.23; 1.11, 1.37) (M. Wang et al. 2022).

# Evidence gaps by life stage group, dietary exposures and outcomes

In **Table 8** we show a mapping of the associations observed in the included SRM by life stage group, dietary exposures and outcomes.

**Diet quality during pregnancy** – the evidence available on the risks associated with various dietary factors during pregnancy covers a wide range of maternal nutrition and health outcomes, as well as birth outcomes (e.g. LBW). Diet diversity is a common metric used to assess nutrient adequacy during pregnancy, particularly in LMICs, but is less useful for estimating the risk of pregnancy complications such as gestational diabetes and hypertension. Dietary risk factors for NCD-related outcomes were commonly assessed using specific food groups as well as

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healthy and unhealthy dietary patterns. Intake of ASF was used to assess risk of micronutrient deficiency and anemia during pregnancy. There were no studies found for LMICs that used a common diet metric to look at both nutrient inadequacies and NCD-related complications during pregnancy.

**Diet diversity was also a common and consistent measure of diet quality for children,** with studies from LMICs focused largely on the association of diet diversity with undernutrition outcomes, including stunting, wasting and anemia. No SRM was found that looked at diet diversity or consumption of meat, fish or eggs in relation to young child overweight and obesity. While several SRM assessed the risk of overweight and obesity with SSB intake in children <5 years, no other SRM with studies from LMICs was found for this age group that looked at nutrition or health outcomes associated with energy-dense and nutrient-poor foods, including UPF. In older children 5-19 years, various dietary exposures, including specific food groups (e.g. dairy, meat, SSB) and food patterns (e.g. diet diversity, unhealthy dietary pattern, fast food) were assessed in relation to risk of overweight and obesity.

**Diets for adults** – the mapping of evidence from meta-analyses of dietary risk factors for nutrition and health outcomes in adults in LMICs shows a wide range of food groups and dietary patterns, with emphasis on their contribution to obesity and diet-related NCD outcomes. In general, dietary diversity has shown no evidence of an association with overweight and obesity, diabetes or hypertension. However, there have been multiple SRM looking at healthy and unhealthy dietary patterns, as well as specific food groups such as UPF, SSB, red and processed meat, dairy, and fruits and vegetables in relation to these outcomes.

# Discussion

Published evidence on the nutrition and health economic costs associated with dietary risk factors in LMICs is limited. Most of the studies found were based on one specific modeling approach, the GBD, that focuses its estimates on adults and a group of dietary risk factors that are associated with NCD outcomes. While useful for describing the high costs of diet-related NCDs to human life and the health care system, this body of literature has limited relevance for countries facing the DBM, where the burden of undernutrition is still a public health concern, especially in young children. The one study that captured both ends of the spectrum was the Cost of Not Breastfeeding model which described the global costs of child deaths due to infection, child obesity and women’s cancer and T2DM (Walters, Phan, and Mathisen 2019). The estimations nevertheless omit the unpaid health care costs of not breastfeeding, such as lost paid work hours or reduced leisure associated with caring for a sick child, opportunity costs which are largely borne by mothers and contribute to gender inequalities (Smith 2019). NonGBD studies commonly estimated the direct and indirect health care costs for the burden of disease associated with dietary risk factors, evidence that is valuable for increasing awareness of decision makers in LMICs of the burden of disease attributable to diets (Alcaraz et al. 2021).

Similarly, our review of the literature available on the risk of malnutrition associated with diets has revealed the challenges in summarizing a very broad body of research that is not yet well-standardized in terms of common metrics for diet quality. Diet metrics were either limited to the dietary diversity score or they were concentrated on specific dietary factors with known risks for nutrition and health outcomes. Definitions of ‘healthy’ or ‘unhealthy’ dietary patterns varied widely across studies and were often data-driven (derived from analytic approaches such as factor analysis) and context-dependent. The lack of consensus on the use of standardized metrics and the heterogenous dietary measures and outcomes used in quantitative studies are an important impediment for a more precise understanding of the dietary effects on different forms of malnutrition.

Restricting our review to articles with systematic reviews that estimated effect size through meta-analysis was a pragmatic way to consolidate the large body of evidence on associations between diet quality and nutrition/health outcomes; however, this approach resulted in some limitations. Most meta-analyses included a limited number of studies conducted in LMICs and these were more often middle-income countries such as Brazil, China and the Islamic Republic of Iran. The small number of meta-analyses that were focused on low-income countries mostly 12
explored associations between diets and undernutrition, with less attention given to the full range of malnutrition outcomes. While the nutrition transition is often more advanced in middle-income countries, the pace of change and the impact in low-income countries cannot be overlooked (Popkin, Corvalan, and Grummer-Strawn 2020). Food consumption patterns vary widely across geographic regions, with big differences between regions in terms of insufficient intakes of healthy foods and excess intakes of unhealthy foods (Miller et al. 2022). Spatial variation in dietary patterns (Zhao et al. 2022) and dietary diversity (Alemu et al. 2022) also occur within the same country. Therefore, it is important for all countries to have evidence based on their local context.

Our scoping review also revealed the polarization in evidence in LMICs. One group of studies focused on the foods that matter for achieving nutrient adequate diets in environments where there are still widespread nutrient inadequacies and undernutrition, especially among nutritionally vulnerable individuals like young children and pregnant women. Another group of studies explored the foods that matter for preventing the rapid rise of overweight/obesity and NCDs with a focus on adult population. For the latter group, although all the SRM reviewed included at least one LMIC, the evidence was heavily weighted toward research conducted in highincome countries. The results of our review show the gaps in evidence for foods and dietary measures that can monitor multiple subconstructs of healthy diets in low-income country contexts, including nutrient adequacy, diversity, balance and moderation (Verger et al. 2023). This is an area of ongoing work to develop, validate and use more widely healthy diet metrics that concurrently capture these two sides of the DBM and are feasible to operationalize in LMICs taking a full life cycle approach.

By relying on meta-analyses for effect size estimates, our review also did not capture emerging approaches to assessment of unhealthy diets and associated outcomes that may be well-suited to monitor the rapid nutrition transition faced by several countries. For example, two relatively new metrics, the Global Dietary Recommendations Score (Herforth et al. 2020) and Global Diet Quality Score (Bromage et al. 2021), have been validated in several countries and are increasing in use in LMICs (Angulo et al. 2021; H. Wang et al. 2022; P. H. Nguyen et al. 2023). Both approaches utilize two sub-scores to monitor dietary factors protective against NCDs and those associated with increased risk of NCDs.

The results of our study provide an overview of the effect sizes for some of the dietary ‘culprits’ in both highincome and LMIC contexts. While consumption of UPF was not historically a major concern, the average dietary share of UPFs has already reached over 50% of total energy intake in some high-income countries and consumption of these foods has been rising quickly in LMICs over the past 30 years (Scrinis and Monteiro 2022). The summary of effect size ranges from SRM that include data from LMICs provide a starting point for better understanding what links exist and how these could be used to inform modeling assumptions on the nutrition and health costs associated with unhealthy diets in LMICs.

No meta-analysis for UPF as a risk factor for overweight/obesity in children <5 years was found in our search, however. UPF consumption among young children has been an active area of investigation and there are several systematic reviews of observational studies. For example, Pries and colleagues (2019) conducted a systematic review of snack food and SSB intake and nutritional status of children 0-23 months in LMICs, noting mixed results, with only one of three studies reporting a positive association with overweight. Two other systematic reviews also explored the risk of obesity (De Amicis et al. 2022) and body fat (Costa et al. 2018) associated with UPF consumption in children. In a group of 26 studies (15 with cohort design) that assessed groups of UPF (e.g. snacks, fast food, junk food) or specific UPF (e.g. SSB, sweets, ready-to-eat cereals), most reported positive associations between UPF consumption with body fat (Costa et al. 2018). However, De Amicis and colleagues (2022) found in their systematic review that consumption of UPF was positively associated with obesity/adiposity only in four longitudinal studies in children with four or more years of follow-up but not in cross-sectional studies. They suggest that a consistent intake of UPF over time may be needed to have an impact on the nutrition status of children.

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Our study also found that, as expected, there are few studies for children in LMICs that assess the contribution of diet to risk factors for NCDs. This is partly due to the limited research on older children and adolescents, when the most critical metabolic risk factors for a range of diseases are most likely to occur compared with younger children. Another reason for the limited research in this area might be due to the emerging nature of the nutrition transition, which is currently under explored also for the adult population in LMICs. It is also likely that there is less recognition of how excess consumption of unhealthy foods such as UPF contributes to nutrient deficiencies in LMICs, since there is a tendency to associate UPF consumption with overweight/obesity and NCDs (Scrinis 2020). In young children, unhealthy snack food and beverages can account for a significant proportion of total energy intake and the micronutrient dilution that results from high consumption of energy-dense but nutrient-poor foods may contribute to micronutrient deficiencies and poor growth outcomes (Pries, Filteau, and Ferguson 2019).

More research is needed to describe how dietary patterns interact with undernutrition (stunting, wasting and micronutrient deficiencies) and overweight/obesity and NCDs at individual, household and country level and in which direction they influence the economic effects. For example, recent evidence from a national survey in South Africa highlights the complexity of malnutrition and diet quality within households, where 18% of children were stunted and 70% of these children lived with an overweight or obese adult (Harper et al. 2022). While children <5 years and adolescents living in households with medium dietary diversity were more likely to be stunted than children in households with high dietary diversity, increased household dietary diversity was associated with increased risk of adult overweight and obesity. The contribution of diet to the growing double and triple burden of malnutrition in LMIC populations is not well-tested in the research available to date, with most SRM looking primarily at the role of diet in contributing to either undernutrition or overweight, obesity and diet-related NCDs.

The widespread use of diet diversity metrics in young children and women of reproductive age along with emerging metrics of diet quality has potential to better capture the link between diet and overweight/obesity and NCDs. Complementary studies are needed to expand the scope of these efforts to include older children, adolescents, men and older adults. This remains challenging given the limited availability, quality and representativeness of dietary data for such groups in LMICs (Demmler et al. 2024).

While no SRM was found that summarized the effect size for the association of ASF intake with nutritional outcomes, there are several observational studies that have used multi-country datasets to explore this (Zaharia et al. 2021; Headey, Hirvonen, and Hoddinott 2018; Krasevec et al. 2017). Four SRM have estimated the risk of child overweight/obesity associated with consumption of dairy products (Babio et al. 2022; Lu et al. 2016; Vanderhout et al. 2020; W. Wang, Wu, and Zhang 2016).

Where SRM looked at the associations between diet diversity and nutrition and health outcomes, it appears that low diet diversity impacts GDM and poor neonatal birth outcomes in a similar way as unhealthy diets. Similarly, low diet diversity was associated with overweight and obesity among older children in one meta-analysis of only two studies (Gezaw et al. 2023b). We could not find a SRM that looked at the relationship between diet diversity and overweight among children <5 years of age. One cross sectional study in India has looked at this, reporting that a dietary diversity score of 7-9 food items for children 0-59 months of age was associated with increased risk of overweight and obesity (OR 1.22; 95% CI 1.12, 1.34) (Saha et al. 2022).

Limitations of this review The inclusion criteria were designed to identify research studies that were of high relevance to the theoretical framework we had developed to estimate the nutrition and health economic costs of unhealthy diets. This resulted in excluding several groups of literature, including fortified foods and micronutrient supplementation. Many studies examined individual dietary risks and these have limited usefulness since it is not possible to add them up to obtain overall estimates of the costs of unhealthy diets, since the risk factors may be correlated and/or interact. We also limited our review to studies in English, which likely excluded the emerging body of evidence from studies conducted in LMICs and published in other languages. For diet-related NCD outcomes, the search focused on 14
overweight and obesity, diabetes and hypertension as the most commonly assessed in LMICs but excluded the overarching category of CVD and cancers.

Because this was a scoping review, we did not assess the quality of the studies included, which increases the risk of bias in the results reported, although most of the SRM had stringent quality review criteria for studies they included. The characteristics of the included studies and level of detail describing how diet exposures and outcomes were defined were also based on the quality of information provided in the SRM articles. There was high study variability in terms of methods and how independent and dependent variables were defined and also the extent to which confounding factors such as unhealthy lifestyle behaviours were considered in the multivariable models. For some diet exposures, the same studies were included in multiple SRM; however, we assessed the availability of this evidence qualitatively and did not calculate any summary estimate. The effect size was not often estimated for HICs vs. LMICs in subgroup analysis, preventing us from comparing this difference.

# **Conclusion**

Our results highlight the need to develop theoretical frameworks and conduct research to better quantify the contribution of diet to the coexisting burden of multiple forms of malnutrition and NCDs. This will require increased investment in nationally representative dietary surveys at the individual level in LMICs that support monitoring of the nutrition and health effects of the nutrition transition in these contexts and inform program and policy decision making.

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<!-- Start of picture text --> ‘ ‘ BD):<br>Moderation of dietary risk<br><!-- End of picture text --> <!-- Start of picture text --> a4ERD:\ ’<br>Child stunting, wasting<br><!-- End of picture text --> <!-- Start of picture text --> wre‘ ,<br><!-- End of picture text -->
~~<u>=</u>~~ ~~<u><mark>=n</mark></u>~~ ~~<mark>=a</mark>~~
Figure 3: Flowchart of study selection process for burden of malnutrition associated with diets

|**Identification of studies via database**|**Identification via other methods**|
|---|---|
|Records identified from:<br>Web of Science Core<br>Collection Database<br>(n = 2786)<br>Cochrane Database<br>(n = 305)<br>Records removed_before_<br>_screening_:<br>Duplicate records removed<br>(n = 451)<br>**Identification**|Records identified from:<br>Citation searching (n = 11)|
|Records screened<br>(n = 2335)<br>Records excluded<br>(n = 2124)||
|Reports sought for retrieval<br>(n = 211)<br>Reports not retrieved<br>(n = 0)<br>**Screening**|Reports sought for retrieval<br>(n = 11)|
|Reports assessed for eligibility<br>(n = 211)<br>Reports excluded:<br>Diet intervention, only<br>RCTs (n = 46)<br>|Reports assessed for eligibility<br>(n = 11)|
|No LMIC studies (n = 33)<br>No primary meta-analysis<br>(n = 26)<br>Diet exposure not relevant<br>(n = 17)<br>Outcome not relevant (n =<br>14)<br>Other (n = 3)<br>Total studies included in review<br>(n = 82)<br>**Included**<br>Studies included in review<br>(n = 71)||

<!-- Start of picture text --> Identification<br><!-- End of picture text --> <!-- Start of picture text --> Screening<br><!-- End of picture text --> <!-- Start of picture text --> Included<br><!-- End of picture text --> 18
# **Table 1: Summary of Search 2 literature search elements**

|**P**|**Population**|**General population**grouped by age and physiological status:<br>• Infants and young children aged zero to 59 months<br>• Children and adolescents five years or older<br>• Women of reproductive age, including pregnant and lactating women<br>• Adults*|
|---|---|---|
|**I**|**Intervention/**<br>**Exposure**|**Any measure of ‘diet’ or ‘food’ intake:**<br>• Intake of food items or food groups/categories (breastmilk†, animal source foods, dairy<br>products, ultra-processed food, unhealthy food, sugar-sweetened beverages, salt‡)<br>• Measures of dietqualityandpatterns(diet diversity,healthy/unhealthydietpattern)|
|**C**|**Comparison**|Anyor no comparison|
|**O**<br>Note:|**Outcome**<br>|**Anthropometric measures of nutritional status**: incidence and/or prevalence of low<br>birthweight, small-for-gestational age,child stunting (height-for-age), child wasting<br>(weight-for-height, MUAC), thinness (BMI, MUAC), overweight and obesity (weight-for-<br>height, BMI), body fat percentage, waist circumference<br>**Biochemical measures of nutritional status**: incidence and/or prevalence of specific<br>micronutrient deficiencies (e.g. serum retinol or serum ferritin), anemia (hemoglobin)<br>**Biological measures of NCD risk factors and outcomes**: gestational diabetes, gestational<br>hypertensive disorder, pre-eclampsia, raised blood glucose/type 2 diabetes mellitus, raised<br>bloodpressure/hypertension|

*Older adults were not separated given the literature gap in LMICs.

†Breastmilk was one of the food groups considered as part of the diet for infants and young children but was not included as a specific search term.

‡Although salt is considered a condiment/seasoning, it was included as part of the diet due to its diet-related NCD policy relevance and direct contribution to NCD outcomes of interest to our search (i.e. hypertension) 19
**Table 2: Summary of study characteristics that describe costs associated with unhealthy diets** (listed in groups by type of dietary component assessed)

|Author (Year)|Country and<br>dataset|Life stage group|Diet Assessed|Nutrition/health Outcomes Assessed|Cost Assessed|
|---|---|---|---|---|---|
|GBD 2019 Risk<br>Factors<br>Collaborators<br>(2020)|Global (204<br>countries), GBD<br>1990-2019|General<br>population|13 dietary risk factors* (combined<br>and individually)|_[not reported in the article but deaths_<br>_and DALYs based on diet-related risk-_<br>_disease pairings for outcomes like_<br>_CVD, T2DM]_|Mortality, DALYs|
|Afshin et al.<br>(2019)|Global (195<br>countries), GBD<br>1990-2017|General<br>population|15 dietary risk factors<sup>‡</sup>|_[not reported in the article but deaths_<br>_and DALYs based on diet-related risk-_<br>_disease pairings for outcomes like_<br>_CVD, T2DM]_|Mortality, DALYs|
|Studies estimatingc|ost associated with|all dietaryrisk factors||||
|Zhang et al. (2023)|Global (204<br>countries), GBD<br>1990-2019|General<br>population|13 dietary risk factors*|CVD|Mortality, DALYs|
|Dong et al. (2022)|Global (204<br>countries), GBD<br>1990-2019|General<br>population|13 dietary risk factors relevant to<br>CVD outcomes*|all forms ofCVD|Mortality, DALYs|
|Machado et al.<br>(2022)|Brazil, GBD 1990-<br>2019|General<br>population|15 dietary risks‡|NCDs -CVD,diabetesand neoplasms|Mortality, DALYs|
|Qiao et al. (2022)|Global (204<br>countries), GBD<br>1990-2019|General<br>population|13 dietary risk factors*|23NCDsattributable to dietary risks in<br>GBD|Mortality, DALYs|
|Davila-Cervantes<br>(2020)|Mexico,<br>GBD 1990-2017|General<br>population|dietary riskswere CVD risk factor|CVDas outcome<br>• nutrition-related risk factor:high<br>BMI<br>• health-related CVD risk factors:<br>high systolicblood pressure, high<br>LDL cholesterol, high fasting<br>plasmaglucose|Mortality, DALYs|
|Duncan et al.<br>(2017)<br>Studies estimatingc|Brazil,<br>GBD 1990-2015<br>ost associated with|General<br>population<br>salt/sodium consump|Dietary risk factors(low in whole<br>grains, nuts and seeds, and fruits;<br>high in red and processed meat,<br>sweetened beverages)<br>tion|Diabetes, hyperglycemia(high fasting<br>plasma glucose)|Mortality, DALYs|
|Vega-Solano et al.<br>(2023)|Costa Rica|Adults|Saltconsumption|High blood pressure|Annual cost of<br>hospitalization,<br>consultations and<br>medications|

20
|Author (Year)|Country and<br>dataset|Life stage group|Diet Assessed|Nutrition/health Outcomes Assessed|Cost Assessed|
|---|---|---|---|---|---|
|Guedes et al.<br>(2022)|Brazil,<br>GBD 2019|General<br>population|sodiumconsumption (excessive =<br>>3g/day)|hypertension(intermediate risk factor),<br>CVD,chronic kidneydisease|Mortality, DALYs|
|Nilson, da Silva,<br>and Jaime (2020)|Brazil||Saltconsumption|CVD, mediated by effect of salt<br>consumption on systolicblood<br>pressure|Direct costs<br>(hospitalization for CVD<br>causes)|
|Nilson et al.<br>(2020)<br>Studies estimating|Brazil<br>cost associated with|Adults 30+ years<br>other individual dietar|Sodiumconsumption<br> yrisk factors|High blood pressure|Mortality and costs-of-<br>illness (inpatient &<br>outpatient care and<br>medications)|
|Liu et al. (2022)|Global (204<br>countries), GBD<br>1990-2019|General<br>population|diet high in red meat(beef, pork,<br>lamb and goat, and all processed<br>meats - notpoultry,fish or eggs)|ischemic heart disease,diabetes, and<br>colorectal cancer (3 leading diseases<br>attributable to diet high in red meat)|Mortality, DALYs|
|Rocha et al.<br>(2023)|Brazil|Adults 25+ y|diet rich in processed meats(daily<br>consumption of cured, smoked,<br>salted meats)|ischemic heart disease,diabetes,<br>colorectal cancer|Mortality, DALYs<br>+ costs of hospitalizations<br>and outpatient<br>procedures|
|Zhuo et al. (2022)|Global (204<br>countries), GBD<br>1990-2019|General<br>population|Diet low infiber|NCDs|Mortality, DALYs|
|Alcaraz et al.<br>(2023)|Argentina, Brazil,<br>El Salvador, and<br>Trinidad and<br>Tobago|Children/<br>adolescents,<br>adults|SSBconsumption|Effects on healthvia BMIand<br>independent effects ondiabetesand<br>CVD|Mortality, DALYs, direct<br>medical costs|
|Pradhananga et<br>al. (2022)|Nepal +<br>comparator<br>countries<sup>†</sup>, GBD<br>2010-2019|General<br>population|suboptimal breastfeeding, diet high<br>in SSB, diet high in trans-fatty acids|Moderate/severe acute wasting<br>high BMI(BMI>20-25 in adults 20+y,<br>IOTF cutoffs for obesity in children<br><20)|Mortality, DALYs|
|Li et al. (2021)|China, GBD 2017|General<br>population|diet high in SSB|Ischemic heart disease (IHD),T2DM|Mortality, DALYs|
|Basto-Abreu et al.<br>(2020)|Mexico|Adults 20-59 y|Beverages and snacks intake<br>(kcal/day)|Weight change (kg),obesity|Direct health care and<br>indirect costs (premature<br>deaths, work<br>absenteeism,others)|
|Javanbakht et al.<br>(2018)|Islamic Republic<br>of Iran, micro-<br>simulation<br>modelingstudy|Adults|Dairy foods consumption|CVD,T2DM|annual per capita health<br>care costs|

21
|Author (Year)|Country and<br>dataset|Life stage group|Diet Assessed|Nutrition/health Outcomes Assessed|Cost Assessed|
|---|---|---|---|---|---|
|Walters et al.<br>(2019)|Global (130<br>countries)|Infants and<br>children <2 y|Not breastfeeding according to<br>recommendations|Child morbidity (diarrhea, pneumonia)<br>MaternalT2DM, breast & ovarian<br>cancer|Child and maternal<br>mortality, direct health<br>care costs, household<br>formula costs, future<br>economic cost of<br>mortality and cognitive<br>losses|

* diets low in fiber, fruits, legumes, nuts and seeds, polyunsaturated fatty acids, seafood omega-3 fatty acids, vegetables and whole grains, and diets high in processed meat, red meat, sodium, sugar-sweetened beverages and trans fatty acids

† India, Bangladesh, Bhutan, Pakistan, Sri Lanka, Maldives, and Afghanistan

> ‡ includes diets low in calcium and milk (in addition to the other 13 dietary risk factors included in GBD 2019)

22
Table 3: Summary of costs associated with dietary risk factors for nutrition and health outcomes in Search 1 studies

|Dietary risk factor<br>(Author,Year)|Geographic<br>context, year|Nutrition/health<br>outcomes|Mortality cost|DALYs cost|Health care and other<br>economic costs|
|---|---|---|---|---|---|
|*Dietary risks<br>(Zhang et al. 2023)|Global, 1990-<br>2019|CVD|2019: 41% of CVD mortality<br>attributable to dietary risk<br>factors<br>6.86 million deaths<br>(ASDR 87.38)<br>High sodium 21.51<br>Low whole grains 20.48<br>Low legumes 14.30|2019: 39% of CVD DALYs<br>attributable to dietary risk<br>factors<br>153.19 million DALYs<br>(ASR 1870.06 per 100,000)<br>High sodium 490.68<br>Low whole grains 433.82<br>Low legumes 297.51|n/a|
|*Dietary risks – 13<br>relevant toCVD<br>(Dong et al. 2022)|Global, 1990-<br>2019|CVD|6.9 million (37%) CVD deaths<br>attributed to dietary risks|153.2 million (39%) CVD DALYs<br>attributed to dietary risks<br>(ASR 1870.1 per 100,000)<br>Low whole grains 20.1%<br>High sodium 18.1%<br>Low fruits 14.9%<br>Low nuts and seeds 12.2%<br>Low vegetables 9.8%<br>Low seafood OmFA 9.3%|n/a|
|*Dietary risks – 13<br>related to CVD<br>(Qiao et al. 2022)|Global, 1990-<br>2019|_NCDs attributable to_<br>_dietary risks (CVD,_<br>_cancers and_T8DM_)_|2019:7.9 million deaths<br>(ASDR 101.0)|2019:187.7 million DALYs<br>Top fve dietary risks<br>High sodium<br>Low whole grains<br>Low legumes<br>Low fruits<br>High red meat|n/a|
|*Dietary risks - 15<br>(Davila-Cervantes<br>2020)|Mexico, 1990-<br>2017|CVD|n/a|Risk factors contributing to CVD<br>age-standardized DALY rate per<br>100,000:<br>High systolic BP 1423.8<br>Dietary risks 1397.9<br>High BMI 883.4|n/a|
|*Dietary risks for<br>diabetes - 15<br>(Duncan et al. 2017)|Brazil, 1990-<br>2015<br>Whole<br>population|Diabetesand<br>hyperglycemia|62,466 diabetes deaths|Total 2.11 million<br>(ASR 1102.8 per 100,000)<br>High BMI610.7 (60.1%)<br>Low whole grains 217.3 (21.4%)<br>High SSB 11.9 (1.2%)<br>Low nuts and seeds 138.1<br>(13.6%)<br>High red meat 42.0(4.1%)|n/a|

23
|Dietary risk factor<br>(Author,Year)|Geographic<br>context, year|Nutrition/health<br>outcomes|Mortality cost|DALYs cost|Health care and other<br>economic costs|
|---|---|---|---|---|---|
|||||High processed meat 125.3<br>(12.3%)||
|*Dietary risks - 15<br>(Machado et al.<br>2022)|Brazil, 1990-<br>2019<br>Adults 25+ y|_CVD,_T8DM_and_<br>_neoplasms are main_<br>_NCDs attributable to_<br>_unhealthy diet_|ASDR 65.3 deaths per 100,000<br>due to NCDs attributable to<br>dietary risks<br>Top 3:<br>High red meat 17.1, low whole<br>grains 14.4,high sodium 13.4|2019:ASR 1,617.7per 100,000<br>Top 3:<br>High red meat 487.0<br>Low whole grains 348.7<br>High sodium 293.1|n/a|
|Salt consumption<br>(Vega-Solano et al.<br>2023)|Costa Rica,<br>2018<br>Population 15+<br>y|_High blood pressure_<br>_and CVD outcomes_<br>_(CHD, hypertensive_<br>_diseases, stroke)_|n/a|n/a|USD 15.1 milliontotal cost of<br>CVD hospitalizations,<br>consultations and medications<br>USD 6.8 millionannual<br>productivity costs of losses of<br>GDP|
|*Salt consumption<br>(Guedes et al. 2022)|Brazil, 2019|_CVD, chronic kidney_<br>_disease, stomach_<br>_cancer_|30,814 deaths|699,119 DALYs|n/a|
|Salt consumption<br>(Nilson, Metlzer et<br>al. 2020)|Brazil, 2017|_Hypertension and_<br>_associated CVD_|46,651 deaths from CVDs (15%<br>of CVD deaths)|575,172 YLL as result of CVD<br>deaths attributable to excessive<br>sodium consumption|USD 192.1 millionin health care<br>costs (hospitalization,<br>outpatient care & medication<br>for hypertension)<br>USD 752.7 millionproductivity<br>losses due topremature deaths|
|Salt/sodium<br>consumption|Brazil, 2013|_Increased systolic_<br>_blood pressure and_|n/a|n/a|USD 102 millionCVD<br>hospitalization costs|
|(Nilson, da Silva et<br>al. 2020)||_associated CVD_|||attributable to excessive salt<br>intake in 2013 (9.4% of total<br>hospitalization costs)|
|*Low fbre<br>(Zhuo et al. 2022)|Global, 1990-<br>2019|All causes, colon<br>and rectum cancer,<br>T2DM,IHD,stroke|ASDR 7.74|ASR 186.89|n/a|
|*High red meat diet<br>(Liu et al. 2022)|Global, 1990-<br>2019<br>Adults 25+ y|_IHD (39%),_T8DM<br>_(9%), colorectal_<br>_cancer (6%)_<br>_(3 leading diseases_<br>_attributable to diet_<br>_high in red meat)_|895,675 deaths (ASDR 11.3per<br>100,000)|23.9 million DALYs<br>(ASR 289.8per 100,000)|n/a|
|*High processed<br>meat diet|Brazil, 1990-<br>2019|_NCDs attributable to_<br>_high processed meat_|2019:ASDR 2.36per 100,000<br>T2DM 1.22|2019:ASR 79.35per 100,000<br>T2DM 50.75|USD 9.4 millionin health care<br>costs (hospitalization and|
|(Rocha et al. 2023)|Adults 25+y|<br>_diet_|IHD 0.92|IHD 23.35|outpatientprocedures)|

24
|Dietary risk factor<br>(Author,Year)|Geographic<br>context, year|Nutrition/health<br>outcomes|Mortality cost|DALYs cost|Health care and other<br>economic costs|
|---|---|---|---|---|---|
||||Colorectal cancer 0.21|Colorectal cancer 5.26||
|SSB consumption in<br>1 year<br>(Alcaraz et al. 2023)|Argentina,<br>Brazil, Trinidad<br>& Tobago,<br>El Salvador|1.5 million<br>overweight & obesity<br>cases in children<br>and adolescents<br>(12%)<br>2.8 million in adults<br>(2.8%)<br>2.2 millionT2DM<br>cases in adults<br>(19%)|18,000 deaths(3.2% of total<br>disease-related deaths)|500,000 DALYs|USD 2 billiondirect medical<br>costs|
|SSB consumption<br>(Bardach et al. 2023)|Argentina, 2020|520,000overweight<br>& obesecases in<br>adults<br>774,000 in children<br>and adolescents<br>23% ofT2DMcases<br>attributed to SSB|4,425 deaths|110,000 YLL due to premature<br>death and disability<br>(no DALYs reported)|$47 millionin adults and$15<br>millionin children and<br>adolescents|
|*SSB consumption,<br>high<br>(Li et al. 2021)|China, 1990-<br>2017|T2DM|924 deathsdue to T2DM|152,780 DALYs(8.2 per<br>100,000) for T2DM|n/a|
|Not breastfeeding<br>(Walters et al. 2019)|Global|Child morbidity:<br>diarrhea,<br>pneumonia,obesity<br>(974,956 cases of<br>childhood obesity<br>each year)<br>Women:T2DM,<br>cancer|595,379 childhood deaths per<br>year (6-59 mon) from diarrhea<br>and pneumonia<br>98,243 women’s deaths from<br>breast & ovarian cancers, T2DM||USD 341.3 billiontotal costs<br>(0.7% of global GNI)<br>USD 1.1 billionannual global<br>health system treatment costs<br>USD 53.7 billionin economic<br>losses due to premature child<br>and women’s mortality;<br>USD 285.4 billionin cognitive<br>losses|
|‡Beverage and<br>snack intake (Basto-<br>Abreu et al. 2020)|Mexico, 2019|4.98 percentage<br>point decrease in<br>obesityprevalence<br>in adults <60 y over 5<br>years, as a result of a<br>36.8 kcal/day<br>reduction in energy<br>intake of beverages<br>and snacks|n/a|n/a|USD 1.84 billionsavings in<br>direct and indirect health care<br>costs|

25
|Dietary risk factor<br>(Author,Year)|Geographic<br>context, year|Nutrition/health<br>outcomes|Mortality cost|DALYs cost|Health care and other<br>economic costs|
|---|---|---|---|---|---|
|‡Dairy foods intake|Islamic|Reduction in CVD|n/a|n/a|Per capita savings in health care|
|(Javanbakht et al.|Republic of|andT2DMincidence|||costs: $0.43 in 1 year|
|2018)|Iran, 2016|due to optimal dairy|||$190.25 in 20 years|
|||food consumption|||Total cost$33.8 millionin 1year|

* study based on GBD model data

† dietary risks comprise the sum of adverse effects of diets in which 15 food types were either underconsumed (fruits, vegetables, legumes, whole grains, nuts and seeds, milk, fiber, calcium, omega-3 fatty acids from seafood, and polyunsaturated fatty acids) or overconsumed (red meat, processed meat, sugar-sweetened beverages, trans-fatty acids, and sodium) ‡ study modeling the cost savings associated with improved diet quality Abbreviations: ASDR age-standardized death rate; ASR age-standardized rate; BMI body mass index; BP blood pressure; CHD coronary heart disease; CVD cardiovascular disease; DALY disability adjusted life year; IHD ischemic heart disease; n/a not available (not reported); NCD non-communicable diseases; SSB sugar-sweetened beverages; T2DM type 2 diabetes mellitus; USD US Dollar; YLL years of life lost due to premature mortality 26
Table 4: Summary of systematic review and meta-analyses studies on burden of malnutrition/disease associated with diet during pregnancy

|Author (Year)|Study Type &|Participants|Diet Assessed|Nutrition, health|Key findings|
|---|---|---|---|---|---|
||Location|||outcomes assessed||
|Maternal nutrition out|<br>comes,includinganem|ia or other nutrient defi|ciencies|||
|Seid et al. (2023)|SRM of 22<br>observational<br>studies in Africa (11<br>cross-sectional, 6<br>cohort, 5 case-<br>control)|9,696 pregnant<br>women|Dietary diversityduring<br>pregnancy<br>_(note: studies used_<br>_different cutoffs for_<br>_‘inadequate DD’,_<br>_including <5/10 (n=9)_<br>_and≤4/9(n=2))_|Maternal anemia, low<br>birthweight|Low diet diversity associated with maternal<br>anemia (OR 2.15;95% CI 1.66, 2.65; 13<br>studies)<br>Low diet diversity associated with LBW (OR<br>2.04;1.45, 2.63; 9 studies)|
|Geta, Gebremedhin,<br>and Omigbodun<br>(2022)|SRM of 60 studies<br>(53 cross-<br>sectional, 5 case-<br>control, 2 cohort)<br>from Ethiopia|Pregnant women,<br>Ethiopia|Dietary diversity(low,<br>medium, high) assessed<br>in 10 studies|Anemia (Hb <11 g/dL)|Low diet diversity associated with higher risk<br>of anemia (RR 2.61; 1.85, 3.68)|
|J. Zhang et al. (2022)|SRM of 51 studies<br>(42 cross-<br>sectional, 6 case-<br>control, 3 cohort),<br>all in LMICs (36 in<br>Ethiopia)|Pregnant women 15-<br>49 y (n=73,919)|Model tested for 8<br>dietary habit exposures,<br>including frequency of<br>eating vegetables (n=10)<br>or meat (n=11), DDS<br>(n=7)|Anemia (Hb <11 g/DL)|Risk factors for anemia included frequency<br>of eating meat≤1 time per week (OR 2.02;<br>1.55, 2.50), eating vegetables≤3 times per<br>week (OR 2.97; 95% CI 1.59, 4.34), diet<br>diversity score≤3 (OR 2.38; 1.55, 3.21)|
|Getaneh et al.<br>(2021)|SRM of 24 studies<br>(22 cross-<br>sectional, 1 cohort,<br>1 case-control) in<br>Ethiopia|Pregnant women<br>15–49 y (n=12,893)|Dietary diversity<br>(DDS <5 food groups)<br>assessed in 5 studies|Undernutrition,<br>assessed as MUAC<br><21 (n=6), <22 (n=9) or<br><23 cm (n=6);<br>BMI <18.5 kg/m<sup>2</sup>(n=3)|Pooled prevalence of undernutrition was<br>29.2% (MUAC) and 27.9% (BMI).<br>Low diet diversity associated with risk of<br>undernutrition during pregnancy (effect size<br>2.89;95% CI 1.28,6.53)|
|Berhe, Gebrearegay,<br>and Gebremariam<br>(2019)|SRM of 7 studies,<br>Ethiopia|N=2371 pregnant<br>women|ASF intake, diet diversity|Zinc deficiency<br>(plasma or serum Zn<br>concentration)|Pooled prevalence of zinc deficiency 59.9%<br>(95% CI 51.9, 67.7)<br>Dietary risk factors associated with Zn<br>deficiency (n=4 studies):<br>low intake of ASF (OR 2.57; 1.80, 3.66),<br>low diet diversity (OR 2.12;1.28,3.53)|
|Health outcomes,incl|uding gestational diab|etes,hypertension, pre-|eclampsia and birth outcom|es||
|Beyene et al. (2023)|SRM of 10 studies<br>(8 cross-sectional,<br>1 cohort, 1 case-<br>control)|N=6525|Diet diversity(2 studies)<br>_(no info on how_<br>_assessed or cutoffs_<br>_used)_|GDM|Risk factors for GDM included inadequate<br>diet diversity (effect size 1.51; 1.25, 1.83)<br>and high BMI≥25 (2.24; 2.07, 2.42)|
|Cui et al. (2023)|SRM of 65 studies<br>(63 cohort and 2<br>case-control),|Pregnant women<br>18+ y (n=831,798)|Dietary intake, pre-<br>pregnancy (foods or|GDM, neonatal<br>outcomes, preterm<br>birth,SGA|GDM positively associated with pre-<br>pregnancy intake of fried food (pooledRR<br>1.59;1.08,2.34),fast food(RR 1.93;1.27,|

27
|Author (Year)|Study Type &<br>Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key findings|
|---|---|---|---|---|---|
||includes Brazil,<br>China, the Islamic<br>Republic of Iran<br>Meta-analysis<br>(n=38)||food groups, dietary<br>patterns)||2.94), red and processed meat (RR 1.61;<br>1.06, 2.45). High dietary fiber negatively<br>associated with GDM (p<0.05). No<br>association for SSB, potato and fish intake.|
|Haghighatdoost et<br>al. (2023)|SRM of 31<br>observational<br>studies (18<br>prospective cohort,<br>7 cross-sectional, 6<br>case-control),<br>includes Brazil,<br>China, the Islamic<br>Republic of Iran;<br>meta-analysis<br>(n=27)|Pregnant women<br>16+ y (healthy diet<br>patterns 26 studies<br>n=80 849);<br>unhealthy diet<br>patterns 15 studies<br>n= 32 965)|Dietary patternsduring<br>pregnancy (n=25) or<br>pre-pregnancy (n=5);<br>assessed using FFQ<br>(n=23), dietary recall or<br>record (n=8)|GDM<br>Assessed using<br>glucose test (n-24) or<br>self-report (n=7)|Pregnant women with a healthier dietary<br>pattern (a diet rich in fruits, vegetables, and<br>whole grains) had lower risk for GDM(RR =<br>0.86; 0.76–0.96). Marginally significant<br>association between unhealthy dietary<br>patterns and GDM risk (1.28; 0.99–1.67).|
|X. Gao et al. (2023)|SRM of 19 studies<br>(15 cohort, 4 case-<br>control), 5 in China,<br>3 in the Islamic<br>Republic of Iran<br>(11 studies<br>included in meta-<br>analysis)|Pregnant and pre-<br>pregnant (6 studies)<br>women (n=108,084)|Diet quality<br>(Mediterranean diet,<br>DASH, Alternate Healthy<br>Eating Index, other<br>quality indices or<br>scores)|GDM|Higher quality diet reduced risk of GDM<br>(Higher Mediterranean diet (OR 0.51;<br>0.30,0.86), DASH (0.66;0.44-0.97), AHEI<br>(0.61; 0.44-0.83), overall plant-based diet<br>index (0.57; 0.41-0.78), and adherence to<br>national dietary guidelines (0.39; 0.31-0.48).|
|Paula et al. (2022)|SRM of 61 studies<br>(47 cohort, 9 cross-<br>sectional, 5 case-<br>control) from<br>various countries|Pregnant women<br>(n=698,803)|UPF-rich diet<br>consumption(Western<br>diet pattern n=17;<br>sweetened beverages<br>n=12; specific UPF food<br>groups n=12; et al.)|GWG (n=5), GDM<br>(n=15), hypertension<br>(n=3), pre-eclampsia<br>(n=4)<br>LBW (n=11); LGA<br>(n=8); preterm birth<br>(n=4)|Higher consumption of UPF-rich diet<br>increased the odds of GDM (OR 1.48; 1.17,<br>1.87) and pre-eclampsia (OR 1.28; 1.15,<br>1.42) but not hypertension, excessive GWG,<br>LBW, LGA or PTB.|
|Abdollahi et al.<br>(2021)|SRM of 66 cohort<br>studies included in<br>meta-analysis,<br>various HICs and<br>LMICs|Adult mothers (18+<br>y)|Dietary patterns:<br>healthy, unhealthy,<br>mixed|GDM (n=17),<br>hypertension (n=15),<br>GWG (n=10), preterm<br>birth (n=12), LBW (n=7)|Higher maternal adherence to healthy diet<br>associated with lower risk of gestational<br>hypertension(OR 0.86; 0.81, 0.91), LBW (OR<br>0.72; 0.53, 0.97), preterm birth(OR 0.44;<br>0.31, 0.62), and higher birth weight (Hedges’<br>g: 0.91; 0.05, 0.32); adherence to unhealthy<br>or mixed diet associated with higher risk of<br>gestational hypertension (OR 1.23; 1.14,<br>1.34)|

28
|Author (Year)|Study Type &<br>Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key findings|
|---|---|---|---|---|---|
|Kinshella et al.<br>(2021)|SRM of 13 studies<br>(10 case-control, 2<br>cohort, 1 RCT), all<br>LMICs (5 studies<br>included in meta-<br>analysis)|Pregnant women|Consumption of<br>vegetables(n=4<br>studies),fruit(n=5)|Hypertensive<br>disorders, pre-<br>eclampsia|Lower risk of pre-eclampsia associated with<br>adequate (compared with no or low)<br>consumption of vegetables (OR0.38; 0.18,<br>0.80) and adequate (compared with no or<br>low) consumption of fruit (OR0.42; 0.24,<br>0.71).|
|Quan et al. (2021)|SRM of 21 cohort<br>studies|Pregnant women<br>(n=191 589)|Westerndietary<br>patterns|GDM|Consumption of animal meat(pooled RR<br>1.35; 1.16, 1.57) and fast food (1.75; 1.41,<br>2.19) positively associated with risk of<br>developing GDM, but not potatoes (RR 1.12;<br>0.93,1.35).|
|Pérez-Roncero et al.<br>(2020)|SRM of 14 studies<br>(meta-analysis of<br>10 studies),<br>included India|Pregnant women<br>(n=111,184)|Milkand related product<br>consumption|Perinatal outcomes:<br>birth weight and length,<br>SGA, LGA, LBW|Consuming higher amount of milk<br>associated with higher birth weight (mean<br>diff =51.0 g, 95% CI 24.7, 77.3; n=10<br>studies), and infant length (mean diff 0.33<br>cm, 0.03, 0.64; n=5 studies); as well as<br>reduced risk of SGA(OR 0.69, 95% CI 0.56,<br>0.84) and LBW(OR 0.63; 0.48, 0.84), and<br>increased risk of LGA (OR 1.11;1.02,1.21)|
|Hassani Zadeh,|SRM of 18 cohort|Pregnant women 18-|Dietary pattern|GDM|Decreased risk of GDM associated with|
|Boffetta, and<br>Hosseinzadeh<br>(2020)|studies, 1 in Latin<br>America, 2 in Asia<br>(n=13 studies in the<br>meta-analysis)|40 y|(western, prudent,<br>vegetable,<br>Mediterranean), pre-<br>pregnancy (n=6) or<br>during pregnancy (n=7)<br>Assessed using FFQ,<br>24HR or food record|Assessed by medical<br>test record (n=3) or<br>self-reports|“prudent” (RR 0.78, 0.63–0.96), “vegetable”<br>(0.86, 0.76–0.98), and “Mediterranean”<br>(0.71, 0.56–0.91), dietary patterns with high<br>levels of whole grain, fruits, vegetables, and<br>low fat dairy intake. The ‘Western’ dietary<br>pattern associated with increased risk of<br>GDM(1.27,1.03–1.56).|
|Chia et al. (2019)|SRM of 36 studies<br>(33 cohort, 1 case-<br>control, 1 cross-<br>sectional included<br>in meta-analysis, 1<br>RCT), various HICs<br>and LMICs|Healthy pregnant<br>women with no pre-<br>existing health<br>conditions reported|Dietary patterns:<br>‘healthy’ and<br>‘unhealthy’‡<br>[assessed by FFQ<br>(n=29), 24HR (n=6), 3-d<br>food diary (n=5)]|Preterm birth,<br>birthweight, LBW,<br>SGA, LGA,<br>macrosomia|Healthy dietary pattern (top vs. bottom<br>tertile, n=6 studies) associated with lower<br>risk of preterm birth (OR 0.79; 95% CI 0.68,<br>0.91); weak trend to lower risk of small-for-<br>gestational age (n=10 studies,OR 0.86;<br>0.73, 1.01);<br>Unhealthy dietary patterns (n=3 studies)<br>associated with lower birth weight (mean<br>difference:-40 g; -61, -21) and trend to<br>higher risk of preterm birth (OR 1.17; 0.99,<br>1.39)|
|Kibret et al. (2019)|SRM of 21 studies<br>(18 cohort,3 cross-|Pregnant women<br>(n=302,450)|Healthy diet(intake of<br>vegetables,fruits,|Hypertensive disorders<br>ofpregnancy (n=6),|Healthy dietary pattern adherence<br>associated with lower risk ofpre-eclampsia|

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|Author (Year)|Study Type &<br>Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key findings|
|---|---|---|---|---|---|
||sectional), various<br>HICs and LMICs||legumes, whole grains)<br>[diet assessed by FFQ<br>(n=15), 24HR (n=5), 4-d<br>food record(n=1)]|GDM (n=6), PTB (n=9),<br>LBW (n=2)|(OR 0.78; 0.70, 0.86), GDM (OR 0.79; 0.56,<br>0.99), preterm birth (OR 0.75; 0.57, 0.93)|
|Tan, Zhao, and Wang<br>(2019)|SRM of 19<br>observational<br>studies|Pregnant women|Vegetarian Diet|LBW, birth weight,<br>GDM, maternal anemia|Association between vegetarian diet in<br>pregnancy and LBW was marginally<br>significant (1.27 (0.98, 1.65), P = 0.07). No<br>conclusive results regarding the risks of<br>maternal anemia and GDM|
|Liao et al. (2023)|SRM of 12 cohort<br>studies, including<br>China (5), the<br>Islamic Republic of<br>Iran(2)|Pregnant women<br>18+ y|Fruit, vegetable<br>consumption, highest vs<br>lowest intake quartile|GDM|Fruit consumption (n=8 studies)RR 0.92<br>(0.86, 0.99)<br>Vegetable consumption (4 studies) RR 0.95<br>(0.87, 1.03)|

Abbreviations: ASF animal source foods; BP blood pressure; F&V fruit and vegetable; GDM gestational diabetes mellitus; GHT gestational hypertension; GWG gestational weight gain; HT hypertension; LBW low birth weight; LGA large for gestational age; OR odds ratio; OWOB overweight and obesity; PTB preterm birth; RR relative risk; SGA small for gestational age; SSB sugarsweetened beverages; T2DM type 2 diabetes mellitus; UPF ultra processed food; Zn def’y zinc deficiency 30
**Table 5: Summary of systematic reviews with meta-analyses on the risk of malnutrition/disease associated with diet in children <5 years**

|Author (Year)|Study Type &<br>Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key Findings|
|---|---|---|---|---|---|
|Undernutrition outcom|es|||||
|Azmeraw et al.<br>(2023)|SRM of 10 studies<br>in Ethiopia|Children 6-23 mon<br>(n=14,733)|Dietary diversity|Anemia|Low diet diversity associated with risk<br>of anemia (OR 2.73, 95% CI 2.06,<br>3.39)|
|Belachew and<br>Tewabe (2020)|SRM of 16 cross-<br>sectional studies,<br>Ethiopia|Children <5 y<br>(n=11,924)|Dietary diversity(n=5<br>studies)|Anemia(Hb<11 g/dL)|Low diet diversity (<4 food groups per<br>day) associated with anemia (OR<br>1.71;1.10,2.68)|
|Abdulahi et al.<br>(2017)|SRM of 18 cross-<br>sectional studies,<br>Ethiopia|Children 0-5 y<br>(n=39,585)|Dietary diversity|Stunting, underweight,<br>wasting|Diet diversity was significant<br>protective factor for stunting (n=2<br>studies) OR=0.78 (95% CI 0.20,1.35)|
|Overweight and obesi|tyoutcomes|||||
|Horta et al. (2023)|SRM of 159 studies|Most studies carried<br>out on individuals 1-9y|Breastfeeding|Overweight or obesity|Breastfeeding protective for OWOB:<br>pooledOR 0.73 (0.71,0.76)|
|M. Nguyen et al.<br>(2023) †|SRM of 48 articles<br>(40 cohorts)|Children, median age<br>10 y, range 6 mo to 17<br>y (n=91,713)|SSB intake|BMI|Each serving/day increase in SSB<br>intake was associated with a0.07<br>kg/m<sup>2</sup> (95% CI 0.04, 0.10) higher BMI<br>in children|
|Abbasalizad<br>Farhangi et al.<br>(2022) †|SRM of 33 studies<br>(23 cross-<br>sectional, 1 case-<br>control, 4 cohort, 6<br>longitudinal),<br>included Argentina<br>(3), Mexico, China<br>(7), the Islamic<br>Republic of Iran,<br>Lebanon|Children 2-18 y<br>(n=121,282)|SSB intake|BMI(n=19),<br>body fat percentage(n=5),<br>waist circumference<br>(n=15)|High SSB intake associated with<br>0.75 kg/m<sup>2</sup>increase in BMI in children<br>and adolescents (WMD 0.75; 95% CI<br>0.35, 1.15), higher WC (WMD<br>2.35 cm; 1.34, 3.37) and BFP (WMD<br>2.81; 2.21–3.41).|
|Babio et al. (2022) †|Cross-sectional<br>and prospective<br>cohort studies<br>(most in HICs but<br>included China,<br>Mexico & Lebanon)|Children 2-21 y<br>(n=28,740 for total<br>dairy and obesity)|Dairy product<br>consumption|Overweight, obesity|_Cross-sectional studies:_Total dairy<br>consumption inversely associated<br>with risk of obesity (OR 0.66, 95% CI<br>0.48, 0.91) but not overweight (OR<br>1.04, 95% CI 0.73, 1.49); no evidence<br>for association with milk or yogurt<br>_Prospective studies_: total milk<br>consumption associated with<br>overweight (OR 1.13; 95% CI 1.01,<br>1.26)|

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|Author (Year)|Study Type &<br>Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key Findings|
|---|---|---|---|---|---|
|Vanderhout et al.<br>(2020) †<br>Other health outcomes|SRM of 28 studies<br>(20 cross-<br>sectional, 8<br>cohort), 7 countries<br>(6 HICs, 1 in Brazil)<br>meta-analysis n=14<br>(11 cross-<br>sectional, 3 cohort)<br>|Children 1-18 y<br>(n=20,897 in meta-<br>analysis)|Milk consumption,<br>whole (3.25% fat) vs<br>reduced fat (0.1-2%)<br>[assessed using FFQ,<br>24HR, multiday food<br>record, other] Regular<br>consumption defined as<br>typically, daily, or≥4<br>timesper week|Overweight and obesity<br>[assessed by BMI z-score<br>(n=19), body fat<br>percentage (n=4), other<br>(n=5)]|Regularly consuming whole (3.25%<br>fat) vs reduced-fat milk associated<br>with lower risk of OWOB (OR 0.61;<br>95% CI 0.52, 0.72)|
|Horta and de Lima<br>(2019)|SRM of 14 studies<br>(3 case-control, 4<br>cross-sectional, 7<br>cohort), 2 in LMICs|Breastfed subjects<br>(mean age at<br>assessment ranged<br>from 13-71 y)|Breastfeeding|T2DMin later life|Risk of T2DM in later life is lower<br>among subjects who had been<br>breastfed vs. non-breastfed (pooled<br>OR0.67; 95% CI 0.56, 0.80);<br>subgroup analysis found higher<br>protective effect among adolescents<br>10-19 y (OR 0.49; 0.38, 0.63)<br>compared to adults 20+ y (OR 0.77;<br>0.66,0.90)|

Abbreviations: 24HR 24-hour recall; ASF animal source foods; BFP body fat percentage; BMI body mass index; BP blood pressure; CI confidence interval; FFQ food frequency questionnaire; F&V fruit and vegetable; HIC high-income country; LMIC low and middle-income country; OR odds ratio; OWOB overweight and obesity; RR relative risk; SSB sugar-sweetened beverages; SRM systematic review and meta-analysis; T2DM type 2 diabetes mellitus; UPF ultra processed food; WC waist circumference; WMD weighted mean difference † Study also included in Table 6 for children 5+ y due to age range of participants.

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Table 6: Summary of systematic review and meta-analyses studies on burden of malnutrition/disease in children 5-19 y associated with their diet

|Author (Year)|Study Type &<br>Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key Findings|
|---|---|---|---|---|---|
|Undernutrition outcom|es|||||
|Zeinalabedini et al.<br>(2023)|SRM of 20 studies,<br>all LMICs|Children 5-18 y (6<br>studies were girls only)|Diet diversity(17 based<br>on 24HR, 1 7-d recall, 1<br>30-d FFQ, 1 unknown)|Stunting, thinness, wasting|Low diet diversity increased odds of<br>stunting (OR 1.43, 95% CI 1.08, 1.89)<br>and wasting (OR 2.18; 95% CI 1.41,<br>3.36) but not thinness (OR 1.10, 95%<br>CI 0.81,1.49)|
|Berhe et al. (2019)|SRM of 22 cross-<br>sectional studies,<br>Ethiopia|Adolescents<br>(n=17,854)|Dietary DiversityScore,<br>low (DDS<4)|Underweight (BMI-Z <-2<br>SD) (n=5 studies)|Low diet diversity associated with<br>underweight (OR 1.95; 95% CI 1.31,<br>2.92)|
|Berhe et al. (2022)|SRM of 15 cross-<br>sectional studies,<br>Ethiopia<br>(includes 2 national<br>surveys in 2016 –<br>EDHS & MNS)|Adolescent girls 10-19<br>y (n=9,669)|Dietary diversityScore,<br>low (DDS <4 of 9 food<br>groups)|Anemia (using WHO Hb<br>cutoffs)|Low diet diversity (n=2 studies) was<br>associated with anemia (OR=2.81;<br>1.33, 5.90)|
|Habtegiorgis et al.<br>(2022); Endale et al.|SRM of 10 cross-<br>sectional studies,|Adolescent girls|Dietary diversityScore,<br>low (DDS <4 of 9 food|Anemia (using WHO 2011<br>Hb cutoffs by age and|Low diet diversity (n=5 studies)<br>associated with anemia|
|(2022)<br>(both articles based on<br>the same 10 studies)|Ethiopia||groups)|pregnancy status)|(OR 1.35; 95% CI 1.00, 2.34) or<br>(OR 1.56; 95% CI 1.05, 2.32)|
|Jensen (2023)|SRM of 11 cross-<br>sectional studies<br>(most in HICs, 3 in<br>India, 2 in China)<br>8 studies included<br>in meta-analysis|Children and<br>adolescents 5-18 y<br>(n=1545 in meta-<br>analysis)|Adherence toplant based<br>diet(vegan, vegetarian,<br>macrobiotic, etc)|Vitamin B12 level|Vegan or macrobiotic diets<br>associated with lower vitamin B-12<br>levels compared to omnivore diet (-<br>97 pmol/L; 95%CI -187, -7)|
|Overweight and obesit|youtcomes|||||
|Gezaw et al. (2023b)|SRM of 33 studies<br>(32 cross-<br>sectional, 1 case<br>control),Ethiopia|Adolescents 10-19 y<br>(n=25,172)|Diet DiversityScore|Overweight(BMI >+1 SD),<br>obesity(BMI >+2 SD),<br>thinness(WAZ <-2 SD)|Low DDS associated with OW/OB<br>(OR 2.26; 95% CI 1.28, 3.99) based<br>on pooled results from 2 studies, but<br>not thinness(8 studies)|
|Cunha et al. (2018)|SRM of 19 studies<br>(17 cross-<br>sectional, 2<br>cohort), most in<br>HICs, 1 in Tunisia|Boys and girls 7-19 y|Unhealthy dietary pattern<br>[assessed using FFQ<br>(n=11), factor analysis<br>(n=14)]|BMI(n 18),waist<br>circumference(WC) (n 9),<br>systolicblood pressure(n<br>7), diastolic blood<br>pressure (n 6),blood|Dietary patterns with highest vs low<br>intake of unhealthy foods resulted in<br>a higher mean BMI (0.57 kg/m2; 95 %<br>CI 0.51, 0.63) and WC (0.57 cm;<br>0.47, 0·67)|
||7 studies in meta-|||glucose(n 5) and lipid|Low intake of healthy foods|
||analysis(7 with BMI|||profile(n 5)|associated with lower mean BMI|

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|Author (Year)|Study Type &<br>Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key Findings|
|---|---|---|---|---|---|
||data, 5 with WC<br>data)||||(−0.41 kg/m2;−0.46,−0.36) and WC<br>(−0.43 cm; −0.52,−0.33)|
|Nguyen et al. (2023)<br>†|48 articles (40<br>cohorts)|Children, median age<br>10 y, range 6 mo to 17<br>y (n=91,713)|SSBintake|BMI|Each serving/day increase in SSB<br>intake was associated with higher<br>BMI (0.07 kg/m<sup>2</sup>; 95% CI 0.04, 0.10)<br>in children|
|Abbasalizad<br>Farhangi et al.<br>(2022) †|SRM of 33 studies<br>(23 cross-<br>sectional, 1 case-<br>control, 4 cohort, 6<br>longitudinal),<br>included Argentina<br>(3), Mexico, China<br>(7), the Islamic<br>Republic of Iran,<br>Lebanon|Children 2-18 y<br>(n=121,282)|SSBintake|BMI(n=19),body fact<br>percentage(n=5),waist<br>circumference(n=15)|High SSBs intake was associated with<br>0.75 kg/m<sup>2</sup>increase in BMI in children<br>and adolescents (WMD: 0.75; CI<br>0.35–1.15), higher WC (WMD:<br>2.35 cm; 95% CI, 1.34, 3.37; p =<br>0.016) and BFP (WMD: 2.81; CI 2.21–<br>3.41; p < 0.001).|
|Jakobsen, Brader,<br>and Bruun (2023)|SRM of 60 studies<br>(51 cross-<br>sectional, 9<br>longitudinal),<br>includes mix of<br>HICs and LMICs|Children and<br>adolescents 5-18 y<br>(n=242,061)|Consumption of 14<br>differentindividual food<br>or beverage categories<br>_(excluded dietary_<br>_patterns)_|Overweight and/or obesity<br>(based on age- and sex-<br>specific BMI cut-offs)|Higher intake of<br>SSB (OR 1.20; p <0.05, n=26),<br>fast food (OR 1.17; p <0.05, n=24),<br>meat (OR 1.02, p < 0.05, n=7),<br>and refined grains (OR 1.28, p<0.05,<br>n=3) associated with<br>overweight/obesity;<br>Conversely, higher whole grain (OR<br>0.86, p = 0.04, n:5) and sweet bakery<br>(OR 0.59, p < 0.05, n:3) intake<br>associated with lower risk.|
|Poorolajal et al.<br>(2020)|SRM of 199 studies|Children and<br>adolescents 5-19 y<br>(n=1,636,049)|Diet-related risk factors|Overweight/obesity|Factors associated with child<br>OW/OB:<br>sufficient consumption of<br>fruits/vegetables 0.92 (0.84, 1.01)<br>breastfeeding <4 months 1.24 (1.16,<br>1.33); eating sweets≥3 times/week<br>0.78 (0.71, 0.85); eating snack≥4<br>times/week 0.84 (0.71, 1.00); drinking<br>SSB≥4 times/week 1.24 (1.07, 1.43);<br>eating fast-food≥3 times/week 1.03<br>(0.89, 1.18); eating fried-food≥3<br>times/week 1.09(0.90,1.33)|

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|Author (Year)|Study Type &<br>Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key Findings|
|---|---|---|---|---|---|
|Babio et al. (2022) †|SRM of cross-<br>sectional and<br>prospective cohort<br>studies (most in<br>HICs but included<br>China, Mexico &<br>Lebanon)|Children 2-21 y<br>(n=28,740 for total<br>dairy and obesity)|Dairy product<br>consumption|Overweight, obesity|_Cross-sectional studies:_Total dairy<br>consumption inversely associated<br>with risk of obesity (OR 0.66, 95% CI<br>0.48, 0.91) but not overweight (OR<br>1.04, 95% CI 0.73, 1.49); no evidence<br>of association with milk or yogurt<br>consumption.<br>_Prospective studies_: total milk<br>consumption associated with<br>overweight (OR 1.13; 95% CI 1.01,<br>1.26)|
|Vanderhout et al.<br>(2020) †|SRM of 28 studies<br>(20 cross-<br>sectional, 8<br>cohort), 7 countries<br>(6 HICs, 1 in Brazil)<br>14 studies in meta-<br>analysis (11 cross-<br>sectional, 3 cohort)|Children 1-18 y<br>(n=20,897 in meta-<br>analysis)|Whole (3.25% fat) vs<br>reduced fat (0.1-2%)milk<br>consumption<br>[assessed using FFQ,<br>24HR, multiday food<br>record, other]<br>Regular consumption<br>defined as typically, daily,<br>or≥4 timesper week|Overweight and obesity<br>[assessed by BMI z-score<br>(n=19), body fat<br>percentage (n=4), other<br>(n=5)]|Regularly consuming whole (3.25%<br>fat) vs reduced-fat milk associated<br>with lower risk of overweight and<br>obesity (OR 0.61; 95% CI 0.52, 0.72)|
|W. Wang, Wu, and<br>Zhang (2016) ‡|SRM of 17 studies<br>for total dairy<br>products (14 cross-<br>sectional, 2 case-<br>control, 1 cohort)<br>and 16 for milk (12<br>cross-sectional, 4<br>cohort), 10 in Asia,<br>2 in South America|Children 6-19 y<br>(13 studies)|Milk and dairyproducts<br>consumption<br>[assessed using FFQ<br>(n=12) and/or 24HR (n=7)<br>or questionnaire (n=9)]|Obesitydefined using<br>waist circumference or<br>BMI (with cutoffs of≥25,<br>28 or 30 kg/m<sup>2</sup>)|Lower risk of obesity in children<br>associated with consumption of dairy<br>products (pooledOR 0.54; 0.38,<br>0.77) and milk (pooledOR 0.87; 95%<br>CI 0.80, 0.95)|
|Other health outcome|s|||||
|Leyvraz et al. (2018)|SRM of 85 studies<br>(14 experimental,<br>60 cross-sectional,<br>6 cohort and 5<br>case-control<br>studies)|Children and<br>adolescents 0-18 y<br>(n=58,531)<br>(meta-analysis<br>included 3406)|Saltconsumption, g/day|Systolic and diastolic BP|For every additional gram of sodium<br>intake per day, systolic blood<br>pressure increased by 0.8 mmHg<br>(95% CI: 0.4, 1.3) and diastolic blood<br>pressure by 0.7 mmHg (95% CI: 0.0,<br>1.4).|

Abbreviations: 24HR 24-hour recall; ASF animal source foods; BFP body fat percentage; BMI body mass index; BP blood pressure; CI confidence interval; FFQ food frequency questionnaire; F&V fruit and vegetable; HIC high-income country; LMIC low and middle-income country; OR odds ratio; OWOB overweight and obesity; RR relative risk; SSB sugar-sweetened beverages; SRM systematic review and meta-analysis; T2DM type 2 diabetes mellitus; UPF ultra processed food; WC waist circumference; WMD weighted mean difference † Study also included in Table 5 for children <5 y due to age range of participants.

35
‡ Study also included in Table 7 for adults due to age range of participants.

36
Table 7: Summary of systematic review and meta-analyses studies on burden of malnutrition/disease in adults associated with their diet

|Author (Year)|Study Type & Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key fndings|
|---|---|---|---|---|---|
|Nutrition.outcomes||||||
|Haider et al.<br>(2018)<br>Overweight.and.ob<br>|SRM of 27 studies (27<br>cross-sectional, 3 RCTs)<br>includes 1 in Thailand,<br>India, the Islamic Republic<br>of Iran<br>esity<br>|Adults >18y|Vegetarian diet<br>|Iron status<br>|Vegetarians had signifcantly lower<br>serum ferritin levels compared to non-<br>vegetarian controls (**−**29.71**μ**g/L; 95%<br>CI -39.69, -19.73). The impact was<br>more pronounced in men, than in<br>premenopausal women and all women<br>|
|Qorbani et al.<br>(2022)|SRM of 23 studies (22 cross<br>sectional, 1 cohort), 13 in<br>LMICs: the Islamic Republic<br>of Iran 7, Burkina Faso 1,<br>Ethiopia 1, Ghana 1, Sri<br>Lanka 1,Togo 1|Adults 18-67y<br>(n= 113-78235)|Dietary Diversity(note:<br>studies used diferent<br>DDS measurement range<br>scores from 5-24 groups)|Obesity and overweight,<br>diabetes, BMI, blood<br>pressure, lipid profle|Association of DDS with obesity,<br>abdominal obesity, overweight, body<br>mass index, diabetes, blood pressure,<br>and lipid profle (TC, LDL, HDL) was not<br>statistically signifcant.<br>|
|Salehi-Abargouei<br>et al. (2016)|SRM of 16 studies (all<br>cross-sectional) 6 in<br>Asia, 5 in Africa, 3 in North<br>America, 2 in South<br>America|Adults (n ranged<br>from 172 to<br>10,424)|Dietary Diversity|Overweight, obesity, BMI|Found no signifcant association on<br>either overweight/obesity (OR 0.72;<br>95% CI 0.45-1.16), or mean<br>diferences in BMI (MD 0.22; - 0.70,<br>1.14) when comparing highest and<br>lowest diverse diets|
|Moradi et al.<br>(2023)|SRM of 12 studies (9 cross-<br>sectional, 3 cohort)|Adults (n=140<br>577)|UPF|overweight, obesity,<br>abdominal obesity|UPF associated with increased risk of<br>obesity (OR 1.55; 95% CI 1.36, 1.77),<br>overweight (OR 1.36; 1.14, 1.63),<br>abdominal obesity (OR 1.41; 1.18,<br>1.68)|
|Lane et al. (2021)|SRM of 43 studies ((21<br>cross-sectional, 19<br>prospective, 2 case-control,<br>1 both prospective & cross-<br>sectional analysis) 17 in<br>Brazil,1 in Lebanon|Adults,<br>adolescents,<br>children<br>(n=891,723)|UPF|Overweight, obesity,<br>abdominal obesity|UPF associated with an increased risk<br>of overweight (OR 1.36; 95% CI 1.23-<br>1.51), obesity (OR 1.51; 1.34-1.70),<br>abdominal obesity (OR 1.49; 1.34-<br>1.66)|
|Pagliai et al.<br>(2021)|SRM of 23 studies (10<br>cross-sectional, 13<br>prospective cohort), 3 in<br>Brazil, 1 in Lebanon, rest in<br>HICs|General<br>population|UPF|overweight/obesity (5<br>studies), high waist<br>circumference (4<br>studies), hypertension|_Cross-sectional studies_: Highest UPF<br>consumption was associated with<br>increased risk of OWOB (OR 1.39, 95%<br>CI 1.29, 1.50), high WC or abdominal<br>obesity (OR 1.39,95 % CI 1.16,1.67).|

37
|Author (Year)|Study Type & Location|Participants|Diet Assessed<br>|Nutrition, health<br>outcomes assessed|Key fndings|
|---|---|---|---|---|---|
|Askari et al. (2020)|SRM of 14 studies (13 cross<br>sectional, 1 cohort), 7 in<br>Brazil, 1 in Guatemala, rest<br>in HICs|Male and female<br>participants 10-<br>64 y (n=189,966)|UPF(defned by NOVA<br>classifcation system in<br>13 of 14 studies)|Overweight (10 studies),<br>obesity (6 studies)|UPF intake associated with overweight<br>(efect size 1.02; 95% CI 1.01, 1.03)<br>and obesity (efect size 1.26; 1.13,<br>1.41)|
|Qin et al. (2022)|Meta-analysis of 32 studies<br>(12 cross-sectional; 19<br>cohort; 1 case control),<br>includes Chile, China, India,<br>the Islamic Republic of Iran<br>and Philippines;rest in HICs|Adults|Fried Food Consumption|overweight, obesity (11<br>studies), T2DM (10<br>studies), hypertension<br>(11 studies)|Fried-food consumption is associated<br>with increased risk of OWOB (RR 1.16;<br>1.07, 1.25) and hypertension (RR 1.20;<br>1.05, 1.38) but not T2DM (RR 1.07;<br>0.90, 1.27).|
|Jiang et al. (2022)|SRM of 23 studies (18 cross<br>sectional, 1 cohort), China|Adults|Dietary patterns<br>(Traditional Chinese diet<br>(10 studies) vs.<br>modern/western diet (8<br>studies))|Obesity, overweight|Traditional Chinese dietary pattern was<br>associated with a lower risk of<br>overweight/obesity than Western diet|
|Mu et al. (2017)|Review of 21 studies (17<br>cross-sectional, 4 cohort), 2<br>in Mexico, 1 in Colombia, 1<br>in Cameroon|Adults|Dietary patterns<br>(prudent/healthy (n=17)<br>vs. Western/unhealthy<br>(n=18)<br>defned diferently for<br>each study: healthy had<br>high loadings of fruit,<br>vegetables, poultry, fsh,<br>low-fat dairy, whole<br>grains. Unhealthy had red<br>and/or processed meats,<br>refned grains, potatoes,<br>sweets and high fat dairy|Obesity, overweight|Highest categories of prudent/healthy<br>dietary pattern associated with<br>reduced overweight/obesity risk (OR<br>0.64; 95% CI 0.52, 0.78).<br>Increased overweight/obesity risk in<br>the highest vs lowest categories of a<br>western/unhealthy dietary pattern (OR<br>1.65; 95% CI 1.45, 1.87)|
|Dinu et al. (2017)|SRM of 108 studies (86<br>cross-sectional, 10 cohort<br>prospective)|Healthy adults,<br>18-81 y|Vegetarian and vegan<br>dietsvs omnivore diet|BMI (71 vegetarian<br>studies; 19 vegan<br>studies)<br>Blood glucose (27<br>vegetarian studies; 4<br>vegan studies)|_Cross-sectional studies_: Vegetarian<br>diet (WMD -1.49kg/m<sup>2</sup>; 95% CI -1.72, -<br>1.25) and vegan diet (WMD -1.72; -<br>2.21, -1.22) associated with lower BMI;<br>Lower blood glucose observed with<br>vegetarian (WMD -5.08 mg/dL; -5.98, -<br>4.19) and vegan (WMD -6.38; -12.35, -<br>0.41)diet|
|Daneshzad et al.<br>(2021)|SRM of 21 studies (17<br>cross-sectional, 3 cohort, 1<br>case-control),6 in Europe,|General<br>population (6 y<br>and above)|Red meat consumption|Overweight (3 studies)<br>Obesity (7 studies)|Red meat consumption not associated<br>with overweight (efect size: 1.19; 0.97,<br>1.46)or obesity (efect size: 1.16;0.93,|

38
|Author (Year)|Study Type & Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key fndings|
|---|---|---|---|---|---|
||10 in Asia, 2 in Africa, 3 in<br>America|(n=193 203)||Overweight/obesity (9<br>studies)|1.44). OWOB (efect size:1.29, 95%<br>CI: 1.09,1.53)|
|Rouhani et al.<br>(2014)|SRM of 21 studies, 1 in the<br>Islamic Republic of Iran,<br>rest in HICs|Adults,<br>n=1,135,661|Red and processed meat<br>consumption|Obesity|consumption of higher quantities of<br>red and processed meats was a risk<br>factor for obesity (OR 1.37; 95% CI<br>1.14,1.64)|
|Feng et al. (2022)|SRM of 42 articles of 52<br>cohort studies in 4 Asian<br>countries|Adults|Dairy consumption|Obesity, overweight,<br>hypertension, T2DM|For overweight/obesity, risk reduction<br>was 25% (total dairy), 7% (high-fat<br>dairy), 12% (milk), and 13% (yogurt)<br>per specifed increase. Hypertension<br>had a nonlinear association with total<br>dairy, while low-fat dairy and milk<br>showed a 6% reduction. T2DM had<br>nonlinear associations; total dairy and<br>yogurt showed 3% and 7% lower risk<br>per 200-g/d and 50-g/d increase.|
|Schwingshackl et<br>al. (2016)|SRM of 22 longitudinal<br>studies, 1 in the Islamic<br>Republic of Iran, 1 in China,<br>rest in HICs|Adults|Dairy consumption|Body weight, waist<br>circumference, risk of<br>overweight, risk of obesity|Found an inverse association between<br>body weight and yogurt (beta -40.99<br>g/year, 95% CI:-48.09, -33.88), and a<br>positive association for cheese (beta -<br>10.97 g/year, 95% CI: 2.86, 19.07)<br>Highest dairy intake reduced risk of<br>abdominal obesity (OR 0.85; 95% CI<br>0.76, 0.95) and overweight (OR 0.87;<br>95% CI 0.76,1.00)|
|W. Wang, Wu, and<br>Zhang (2016) †|SRM of 17 studies for total<br>dairy products (14 cross-<br>sectional, 2 case-control, 1<br>cohort) and 16 for milk (12<br>cross-sectional, 4 cohort),<br>10 in Asia, 2 in South<br>America|Adults 18+ y (19<br>studies)|Milk and dairyproducts<br>consumption<br>[assessed using FFQ<br>(n=12) and/or 24HR (n=7)<br>or questionnaire (n=9)]|Obesitydefned using<br>waist circumference or<br>BMI (with cutofs of≥25,<br>28 or 30 kg/m<sup>2</sup>)|Lower risk of obesity in adults<br>associated with consumption of dairy<br>products (pooledOR 0.75;95% CI<br>0.69, 0.81) and milk (pooledOR 0.77;<br>95% CI 0.68, 0.87)|
|Schwingshackl et<br>al. (2015)|SRM of 17 cohort studies, 1<br>in the Islamic Republic of<br>Iran, rest in HICs|n=563 277|Fruit and vegetable<br>consumption|Body weight (8 studies),<br>waist circumference (3<br>studies), BMI (2 studies),<br>overweight/obesity (7<br>studies)|Higher fruit intake was linked to weight<br>decrease (beta -13.68 g/year; 95% CI -<br>22.97, -4.40) and reduced WC (beta -<br>0.04 cm/year; -0.05, -0.02). Lower risk<br>of adiposity associated with highest<br>intake of combined fruit & vegetable<br>(OR 0.91,95% CI 0.84,0.99),fruit(OR|

39
|Author (Year)|Study Type & Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key fndings|
|---|---|---|---|---|---|
|Type.8.Diabetes.Me|llitus.(T8DM).outcomes||||0.83, 95% CI 0.71, 0.99), and<br>vegetable(OR 0.83,95% CI 0.70,0.99)|
|B. Li et al. (2023)|Meta-analysis 72 articles of<br>prospective cohort studies,<br>11 in Asia, 11 in Europe. 46<br>in the United States|Adults|SSB,ASB, and fruit juice<br>consumption|T2DM, hypertension|T2DM risk was associated with SSB<br>(RR 1.27; 95% CI 1.17, 1.38), ASBs,<br>(RR 1.32; 1.11, 1.56) and fruit juices<br>(RR 0.98; 0.93, 1.03). Intakes of SSBs<br>and ASBs were signifcantly associated<br>with risk of hypertension.|
|Neelakantan et al.<br>(2021)|SR of 17 studies (9<br>prospective, 7 cross-<br>sectional, 1 clinical), Hong<br>Kong SAR, China; Republic<br>of Korea; Thailand;<br>Singapore;Japan|Adults, Asian<br>population<br>(n=114 208)|SSBconsumption|T2DM|High SSB consumption was associated<br>with greater T2DM risk (RR 1.38; 95%CI<br>1.09-1.73)|
|Mishali et al.<br>(2019)|SRM of 16 prospective<br>cohort studies, 2 in China,<br>rest in HICs|Men and women<br>> 18 years<br>(n=545 677)|Dairy consumption|T2DM (16 studies)|T2D is inversely associated with dairy<br>intake. Subgroup analysis for sex<br>showed that the association between<br>dairy intake and T2D is signifcant in<br>women but not in men.|
|Khoramdad et al.<br>(2017)|SRM of 14 prospective<br>cohort studies, 1 from<br>China, rest from HICs|n=458 082|Dairy consumption|T2DM|Total dairy consumption associated<br>with decreased risk of T2DM (RR 0.88,<br>95% CI 0.80, 0.96), even lower risk<br>with consuming low-fat dairy (RR 0.81;<br>0.68,0.96)|
|D. Gao et al.<br>(2013)|SRM of 16 cohort studies<br>(15 prospective cohort, 1<br>case-cohort), 1 from China,<br>rest from HICs|n=526 998|Dairy consumption, high<br>vs low total dairy intake<br>(13 studies, n=457,893)|T2DM|Found a nonlinear association of total<br>dairy intake and T2DM risk (RR 0.89;<br>95% CI 0.81, 0.98)|
|Aune et al. (2013)|SRM of 16 cohort studies, 1<br>from China, rest from HICs|Adults<br>n=258 078|Whole grain(10 studies,<br>n=385,868) andrefned<br>grain(6 studies,<br>n=258,078)<br>consumption, high vs low<br>intake|T2DM (10 studies)|Non linear association was observed<br>between whole grains and T2DM (RR<br>0.74; 95% CI 0.71, 0.78) but not<br>refned grains (RR 0.94; 0.82, 1.09).<br>Size of the association between whole<br>grains and T2DM was stronger when<br>the analyses were not adjusted for BMI<br>compared with adjustment for BMI (RR<br>0.53 vs. 0.69)|

40
|Author (Year)|Study Type & Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key fndings<br>|
|---|---|---|---|---|---|
|Becerra-Tomás et<br>al. (2021)|SRM of 8 studies (5<br>prospective, 3 cross-<br>sectional), 1 in China, rest<br>in HICs|(cross-sectional<br>n=72,559; cohort<br>7559 cases|Nut intake|T2DM|Nonsignifcant association between<br>total nut consumption and T2DM|
|Quan et al. (2022)|SRM of 9 cohort studies (7<br>articles), 1 from the Islamic<br>Republic of Iran, rest from<br>HICs|Adults 25-75<br>years<br>(n=383 211)|Potato intake|T2DM|Association found between potato<br>intake and risk of T2DM (RR 1.13, 95%<br>CI 1.02, 1.26)|
|Yang et al. (2020)|Meta analysis of 28 cohort<br>studies, 11 from the United<br>States, 10 from Europe, 7<br>from Asia|Total meat<br>consumption<br>(n=386,496)<br>Red meat<br>(n=663,144)|Meat and fsh intake,<br>highest vs lowest intake<br>categories|T2DM|A linear dose-response relationship<br>between total meat (RR 1.33; 95% CI<br>1.16, 1.52), red meat (RR 1.22; 95% CI<br>1.16, 1.28) and processed meat (RR<br>1.25; 95% CI 1.13-1.37) intakes and<br>T2DM risk. In addition, a non-linear<br>relationship of intake of processed<br>meat with risk of T2DM was detected.|
|Zhou, Tian, and Jia<br>(2012)|Meta-analysis of 13 cohort<br>studies, 1 from China, rest<br>from HICs|Adults<br>(n=367 757)|Fish consumption(9<br>studies, n=367,757), high<br>vs low intake|T2DM|Fish intake is weakly associated with<br>T2DM (RR 1.15; 95% CI 1.05, 1.27),<br>based on 7 cohorts without<br>heterogeneity|
|Micha et al. (2010)|SRM of 20 studies (17<br>prospective cohort, 3 case-<br>control), 1 from China, rest<br>from HICs|Healthy adults<br>(n= 1218 380)|Red meat(5 studies) and<br>processed meat(7<br>studies) consumption|T2DM|No association between red meat<br>intake and T2DM (RR 1.16; 95% CI<br>0.92, 1.46). Processed meat intake<br>associated with higher risk of T2DM<br>(RR 1.19;1.11,1.27)|
|Morze et al. (2020)|SRM of 113 reports (47 for<br>this update), 1 in the Islamic<br>Republic of Iran, 1 in Israel,<br>5 in China|n=3,277,684|Diet qualityassessed by<br>the HEI, AHEI, DASH<br>score|T2DM (16 studies)|Diets of the highest quality were<br>inversely associated with risk of T2DM<br>(RR 0.81, 95% CI 0.78, 0.85)|
|Uloko et al. (2018)|SRM of 23 studies (12 cross<br>sectional, 7 cross sectional<br>perspective, 4 prospective),<br>Nigeria|n=14,650|Unhealthy dietary habits<br>(not stated how this was<br>defned)|T2DM|Unhealthy dietary habits are a risk<br>factor for T2DM (OR 8.0; 95% CI 5.4,<br>10.5)|
|Maghsoudi et al.<br>(2016)|SRM of 10 cohort studies, 1<br>from China, rest from HICs|n= 404 528|Healthy and unhealthy<br>dietary habits|T2DM|Adherence to healthy dietary patterns<br>associated with lower risk of T2DM (RR<br>0.86; 95 % CI 0.82, 0.90); unhealthy<br>dietary patterns adversely afected<br>T2DM risk (RR 1.30; 95 % CI 1.18,<br>1.43)|

41
|Author (Year)|Study Type & Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key fndings|
|---|---|---|---|---|---|
|Alhazami et al<br>(2013)|SRM of 15 cohort studies, 2<br>from China, rest from HICs|Adults 20-90<br>years|Healthy and unhealthy<br>dietary patterns, highest<br>vs. lowest adherence<br>(assessed by FFQ n=14;<br>dietaryhistoryn=1)|T2DM|Reduced risk of T2DM for healthy<br>dietary patterns (RR 0.79, 95% CI 0.74,<br>0.86); increased risk for unhealthy<br>dietary patterns (RR 1.44, 1.33, 1.57)|
|Esposito et al.<br>(2014)|Meta-analysis of 18<br>prospective cohort studies<br>(20 cohorts), 4 regions (the<br>United States 2, Europe 5,<br>Australia 1,Asia 5)|Adults 20-90<br>years<br>n=21 372|Healthy dietary patterns<br>(Mediterranean, DASH,<br>AHEI)|T2DM|Participants with the greatest<br>adherence to healthy diets were less<br>likely to develop T2DM (RR 0.80; 95%<br>CI 0.74, 0.86)|
|McEvoy et al.<br>(2014)|SRM of 33 studies (19<br>cross-sectional, 12<br>prospective cohort, 2<br>nested case control)<br>included Mexico, the<br>Islamic Republic of Iran,<br>India,and China|Adults<br>n=309 430|Dietary patterns(healthy/<br>prudent vs. unhealthy/<br>western)|T2DM|Those in the highest category of<br>healthy/prudent diet lower risk for<br>T2DM (OR 0.85; 95% CI 0.80, 0.91).<br>Highest vs lowest category of<br>unhealthy/western diet had increased<br>risk (OR 1.41; 1.32, 1.52)|
|Lee and Park<br>(2017)|SRM of 14 studies (12 cross<br>sectional, 2 cohort),<br>included India; Barbados;<br>Pakistan; China; Taiwan,<br>China|_Not stated_|Vegetarian diet|T2DM|Vegetarian diet is inversely associated<br>with T2DM risk (OR 0.73; 95% CI 0.61,<br>0.87)|
|Sarsangi et al.<br>(2022)|SRM of 16 prospective<br>studies, 1 from the Islamic<br>Republic of Iran, rest from<br>HICs|n= 759 806|Mediterranean diet|T2DM|Greater adherence to Mediterranean<br>diet associated with reduced risk of<br>T2DM (RR 0.83; 95% CI 0.77, 0.90),|
|Yu et al. (2022)|SRM of 19 studies (8 cohort,<br>11 RCTs), includes China,<br>South Asia, the Middle East,<br>and HICs|Adults 18-87y<br>(n=1034)|White vs Brown rice<br>intake|T2DM|Found a positive association between<br>white rice intake and risk of T2DM (RR,<br>1.16; 95% CI 1.02, 1.32). Findings also<br>suggest that brown rice is inversely<br>associated with risk of T2DM (RR 0.89;<br>95% CI 0.81, 0.97 (based on limited<br>data)|
|Carter et al.<br>(2010)|SRM of 6 prospective cohort<br>studies, 1 from China, rest<br>from the United States|Adults 30-74<br>years<br>(n=223 512)|Fruit and vegetable intake|T2DM|Greater intake of green leafy<br>vegetables associated with reduced<br>risk of T2DM (hazard ratio0.86, 95% CI<br>0.77, 0.97); no signifcant benefts of<br>increasing consumption of vegetables,<br>fruit,or fruit and vegetables combined.|

42
|Author (Year)|Study Type & Location|Participants|Diet Assessed|Nutrition, health<br>outcomes assessed|Key fndings|
|---|---|---|---|---|---|
|Hypertension.outc|omes|||||
|Chen et al. (2022)|SRM of 55 prospective<br>cohort studies, 1 from the<br>Islamic Republic of Iran, 1<br>of 21 LMIC countries|included cohorts<br>between 337 and<br>409,885 adult<br>participants|Dairy consumption,<br>highest compared with<br>lowest level of intake|risk of hypertension|Total dairy consumption was<br>associated with a moderately lower<br>risk of hypertension (RR 0.91, 95% CI:<br>0.86, 0.95); RR for 1-serving/d<br>increase: 0.96,95% CI: 0.94,0.97)<br>|
|M. Wang et al.<br>(2022)|SRM of 9 studies (5 cross-<br>sectional, 4 cohort),<br>includes 2 from Brazil, 1<br>from Mexico, 1 from<br>Lebanon|Adults≥18<br>(n=111,594)|UPFconsumption|hypertension|higher UPFs consumption signifcantly<br>increased the risk of hypertension (OR<br>1.23; 95% CI 1.11, 1.37)|
|Cowell et al.<br>(2021)|SRM of 35 studies (19 RCTs,<br>14 cross-sectional, 2<br>prospective)|Adults≥18<br>n=59,001|Mediterranean diet,<br>higher vs lower<br>adherence|hypertension|Odds of hypertension were 13% lower<br>with higher versus lower MedDiet<br>adherence(OR 0.87,0.78,0.98)|
|Riaz et al. (2021)|SRM of 37 studies (3- cross-<br>sectional, 7 case-control),<br>all in Pakistan|Adults, children,<br>adolescents 5-<br>80y<br>n=99,391|unrestricted salt in their<br>diet(n=3)|hypertension|Individuals having unrestricted salt in<br>their diet were less likely to have<br>hypertension (OR 0.24; 95% CI 0.12,<br>0.47)|
|Filippini et al.<br>(2022)|SRM of 11 cohort studies,<br>includes 1 from China, rest<br>HICs|Adults 18-75 y|Sodium intake(3 studies<br>used dietary recall, 8<br>estimated based on<br>urinaryexcretion)|hypertension|Excess risk of hypertension found from<br>an exposure of≥3 g/day: 4 g/day (RR<br>1.04; 95% CI 0.96, 1.13) and 6 g/day<br>(RR 1.21;1.06,1.37)|
|Schwingshackl et<br>al. (2017)|SRM of 28 studies, includes<br>1 from the Islamic Republic<br>of Iran|Adults >20y|Various food groups(12:<br>whole grains, refned<br>grains, vegetables, fruits,<br>nuts, legumes, eggs,<br>dairy, fsh, red meat,<br>processed meat, and<br>SSB)|Hypertension|An inverse association with risk of<br>hypertension was observed for 30 g<br>whole grains/d (RR 0.92; 95% CI 0.87,<br>0.98), 100 g fruits/d (RR 0.97; 0.96,<br>0.99), 28 g nuts/d (RR 0.70; 0.45, 1.08),<br>and 200 g dairy/d (RR 0.95; 0.94, 0.97),<br>and a positive association for 100 g red<br>meat/d (RR 1.14; 1.02, 1.28), 50 g<br>processed meat/d (RR 1.12; 1.00,<br>1.26), and 250 mL SSB/d (RR 1.07;<br>1.04,1.10)|

Abbreviations: 24HR 24-hour recall; ASF animal source foods; BFP body fat percentage; BMI body mass index; BP blood pressure; CI confidence interval; FFQ food frequency questionnaire; F&V fruit and vegetable; HIC high-income country; LMIC low and middle-income country; OR odds ratio; OWOB overweight and obesity; RR relative risk; SSB sugar-sweetened beverages; SRM systematic review and meta-analysis; T2DM type 2 diabetes mellitus; UPF ultra processed food; WC waist circumference; WMD weighted mean difference † Study also included in Table 6 for children 5+ y due to age range of participants.

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**Table 8: Mapping the evidence available for the relationship between food and diet measures and nutrition and health outcomes by life stage group (based only on SRM included)**

||**PR**|**EGNANCY**|**CHILD**|**REN < 5 Y**|**CHILD**|**REN 5-19 Y**||**ADULTS**||
|---|---|---|---|---|---|---|---|---|---|
||**Maternal**|**Birth Outcome**|**Under-**|**Overweight,**|**Thinness**|**Overweight,**|**Thinness**|**Overweight,**|**NCDs:**|
|**DIET**<br>**MEASURE**|**Complications**||**nutrition**|**obesity**|**Anemia**|**obesity**|**Anemia**|**obesity**|**Hypertension,**<br>**Diabetes**|
|**Low diet**|Thinness|LBW|Stunting|_No SRM found_|Stunting|<br>OWOB|_No SRM_|_Inconsistent_|_Inconsistent_|
|**diversity**|Anemia<br>Zn def’y<br>GDM||Anemia||Wasting<br>Anemia||_found_|_association_<br>_(n=60+_<br>_studies)_|_association_<br>_(n=41+ studies)_|
|**Intake of**<br>**ASF (meat,**<br>**fish, eggs)**|**Low ASF intake**<br>Anemia<br>Zn deficiency<br>**Red, processed**<br>**meat**GDM|**Dairy**<br>LBW, SGA|_No SRM_<br>_found_|**Dairy**<br>Obesity|_No SRM_<br>_found_|**Dairy**<br>Obesity<br>**Meat**<br>obesity|Vegetarian<br>diet  low<br>sFerritin|<br> <br>**Dairy**<br>Obesity<br>**Red,**<br>**processed**<br>**meat**<br>OWOB|**Dairy**<br>T2DM, HT<br>**Red, processed**<br>**meat**<br> T2DM, HT|
|**Diet low**<br>**in...**|**DGL Vegetables**<br>Anemia|||||||||
|**Diet high**|**Healthy diet**|**Healthy diet**||**SSB** % body||**Unhealthy**||**Healthy diet,**|**Healthy diet,**|
|**in...**|GDM, GHT,<br>pre-eclampsia<br>**Fruit**<br>GDM, pre-<br>eclampsia|LBW, SGA<br>PTB<br>**Unhealthy diet**LBW||fat||**diet, SSB,**<br>**fast food**<br>OWOB<br>**Salt**<br>BP||**F&V, fiber**<br>body weight<br>**Unhealthy**<br>**diet, SSB, UPF**|**,**<br>**whole grains,**<br>**fiber, F&V**<br>T2DM, HT<br>**Unhealthy diet,**|
||**Vegetables**<br>pre-eclampsia<br>**Unhealthy diet,**<br>**fast food, fried**<br>**food, UPF**<br>GDM|||||||**fried food**<br>OWOB|**SSB, UPF,**<br>**fried food**<br>T2DM, HT<br>**Salt**HT|
|**GBD 13**<br>**dietary risk**<br>**factors**||||||||high BMI|T2DM, HT,<br>CVD|

Abbreviations: ASF animal source foods; BP blood pressure; F&V fruit and vegetable; GDM gestational diabetes mellitus; GHT gestational hypertension; HT hypertension; LBW low birth weight; OWOB overweight and obesity; PTB preterm birth; SSB sugar-sweetened beverages; T2DM type 2 diabetes mellitus; UPF ultra processed food; Zn def’y zinc deficiency 44