## **The Multifaceted Nature of Food and Nutrition Insecurity around the World and Foodservice Business**

Edited by António Raposo and Heesup Han Printed Edition of the Special Issue Published in *Sustainability*

www.mdpi.com/journal/sustainability

## **The Multifaceted Nature of Food and Nutrition Insecurity around the World and Foodservice Business**

## **The Multifaceted Nature of Food and Nutrition Insecurity around the World and Foodservice Business**

Editors

**Ant ´onio Raposo Heesup Han**

MDPI ' Basel ' Beijing ' Wuhan ' Barcelona ' Belgrade ' Manchester ' Tokyo ' Cluj ' Tianjin

*Editors* Antonio Raposo ´ CBIOS (Research Center for Biosciences and Health Technologies) Universidade Lusofona de ´ Humanidades e Tecnologias Lisboa Portugal

Heesup Han College of Hospitality and Tourism Management Sejong University Seoul Korea

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Sustainability* (ISSN 2071-1050) (available at: www.mdpi.com/journal/sustainability/special issues/ FNI).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

**ISBN 978-3-0365-4732-9 (Hbk) ISBN 978-3-0365-4731-2 (PDF)**

© 2022 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND.

## **Contents**


for Sustainable Livelihood

Reprinted from: *Sustainability* **2021**, *13*, 6137, doi:10.3390/su13116137 . . . . . . . . . . . . . . . . **127**


## **About the Editors**

## **Ant ´onio Raposo**

Antonio Raposo is an Assistant Professor at Universidade Lus ´ ofona de Humanidades e ´ Tecnologias and he is an Integrated Member of CBIOS (Research Center for Biosciences and Health Technologies).

He graduated in Nutritional Sciences from Instituto Superior de Ciencias da Sa ˆ ude Egas Moniz, ´ Portugal, 2009, and obtained his Ph.D. with European Mention in Animal Health and Food Safety from the University Institute of Animal Health and Food Safety, University of Las Palmas de Gran Canaria, Spain, 2013.

His main research interests are: studies on the utilization of Catostylus tagi jellyfish in health sciences, particularly as a food ingredient; food habits; food safety evaluation; food innovation; natural food products; food security; and sustainability.

He is a member of the editorial boards and an invited reviewer of relevant international peer-reviewed journals in his research field. He has published more than 70 papers in indexed JCR journals, as well as scientific book chapters and a patent model, and co-authored a book in English with a special focus on Nutrition, Food Security, and Food Safety Sciences. He has been a Guest Editor for several Special Issues published in high-impact JCR journals such as *Foods*, *Sustainability* and *International Journal of Environmental Research and Public Health*. He has collaborated as a Visiting Professor in Portuguese, Spanish, Chilean, and Vietnamese Universities.

## **Heesup Han**

Heesup Han is a Professor in the College of Hospitality and Tourism Management at Sejong University, Korea. His research interests include sustainable tourism, green hotel, cruise, airline, medical tourism, digital currency, the Fourth Industrial Revolution, and hospitality and tourism marketing. Heesup Han is a highly cited researcher (HCR) (2019, 2020, and 2021) of the world in social science (identified by the Web of Science Group—Clarivate).

## **Preface to "The Multifaceted Nature of Food and Nutrition Insecurity around the World and Foodservice Business"**

Food and nutrition are undoubtedly the core aspects of human life. This book introduces the multifaceted nature of food, such as food security, nutrition, and food quality and service. (1) Food security is an international concept as all individuals across the world should have physical, social, and economic access at all times to sufficient, safe, and nutritious foods that meet their dietary needs and food preferences for an active and healthy life. Four parameters, namely availability, access, utilization, and stability, should accordingly be measured to determine food security status. In the past, the food security term has been the issue of food availability and accessibility, and the utilization aspect has been identified as essential more recently. (2) Nutrition, on the other hand, is centered on consuming adequate diversified meals and nutrient absorption that could contribute to other forms of malnutrition, such as hidden hunger and obesity. (3) Food quality is another critical issue across the globe. In their daily life, an individual eats at a restaurant/cafe/hotel and seeks better quality ´ food and service. The quality performance of food and service at a foodservice operation contributes to making one's consumption happier and his/her life healthier. A total of 13 research works are included in this book. The authors are from diverse countries and address the critical issues of food.

The editors are very grateful to their families and friends for all of the support they provided. We would also like to extend a very special thanks to all researchers who published their works herein and the entire MDPI team for their commitment and dedication. Only in this way was it possible to carry out this successful project.

> **Ant ´onio Raposo and Heesup Han** *Editors*

## *Editorial* **The Multifaceted Nature of Food and Nutrition Insecurity around the World and Foodservice Business**

**António Raposo 1,\* and Heesup Han 2,\***


**Keywords:** agriculture; consumer behavior; food habits; food industry and technology; food policy; food safety and quality; food security; nutritional diseases; tourism; service quality at restaurants/cafés/hotels

Food security is more than a basic requirement for survival; it is a human right that has implications for global safety, economic strength, security, and sustainability [1,2]. The international concept of food security is a situation where all people have physical, social, and economic access at all times to sufficient, safe, and nutritious food that meets their dietary needs and food preferences for an active and healthy life. All four parameters (availability, access, utilization, and stability) should therefore be measured to determine food security status [3,4].

The matter of hunger, which is generally equated with access to inadequate amounts of food and compromised food quality to reach the required daily intake, is addressed by both food and nutrient insecurity. In the past, the food security term has been the issue of food availability and accessibility, and the utilization aspect has more recently been identified as essential [5,6]. Nutrition, on the other hand, centered on consuming adequate diversified meals and nutrient absorption that could contribute to other forms of malnutrition, such as hidden hunger and obesity [7].

Food quality is another critical issue across the globe. In their daily life, individuals eat at restaurants/cafés/hotels and seek better quality food and service. The quality performance of food and service at a foodservice operation contributes to making one's consumption more satisfying and his/her life healthier [8].

Taking into account these premises and the multifaceted nature of food and nutrition insecurity around the world and foodservice business, this Special Issue presents 12 papers published by researchers from 19 different countries all over the world, including Brazil, China, Czech Republic, Egypt, Finland, India, Indonesia, Iraq, Italy, Korea, Malaysia, Norway, Pakistan, Poland, Portugal, Russia, Saudi Arabia, Slovakia, and the USA.

Regarding the review papers included in this Special Issue, two investigations can be found that address the following themes: the evaluation of the relationship between negative affect and maladaptive eating behavior as a regulation strategy in normal-weight individuals [9] and a summary of the data on the chemical composition of reindeer meat depending on the region of the *Rangifer tarandus*—a systematic review and meta-analysis [10].

In terms of original articles, we can mention 10 relevant works that focus on different areas common to the objectives of this Special Issue, namely: the study by Batista et al. [11], which constructed and validated an instrument containing three questionnaires to identify the level of knowledge, practices, and risk perception of food safety by low-income students between 11 and 14 years old; the application of a multidisciplinary approach based on ecological and medical research methods with the inclusion of socioeconomic

**Citation:** Raposo, A.; Han, H. The Multifaceted Nature of Food and Nutrition Insecurity around the World and Foodservice Business. *Sustainability* **2022**, *14*, 7905. https:// doi.org/10.3390/su14137905

Received: 21 June 2022 Accepted: 23 June 2022 Published: 29 June 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

analysis to investigate the impact of climate change on the food (in)security of the Siberian indigenous peoples in the Arctic, focused on the environmental and health risks [12]; and the evaluation of the yield of *Melaleuca bracteata* essential oil together with its antioxidant and antimicrobial properties under local prevailing conditions of the subtropics [13]. Furthermore, on 8 May 2021, Abdullah et al. [14] published the significant work entitled "A Comprehensive Appraisal of the Wild Food Plants and Food System of Tribal Cultures in the Hindu Kush Mountain Range; a Way Forward for Balancing Human Nutrition and Food Security" dedicated to the memory of Habib Ahmad (TI), Emeritus of Hazara University, Pakistan, and Fellow of the Pakistan Academy of Sciences who passed away on 7 April 2021. Habib was an extraordinary scholar and great human being, and he represented an irreplaceable academic guide for generations of young botanists, plant ecologists, and agricultural scientists across the globe. Langyan et al. [15] presented an investigation planned to understand the variability and inter-relationships among various nutritional quality attributes of maize kernels to identify potential donors of the respective traits for future hybridization programs. Akbara et al. [16] conducted an importance–performance analysis to examine international students' perceived importance and perceived performance of university foodservice attributes. Lee et al. [17] conducted a study to define detailed factors by combining the factors of social network services information attributes and dual processing process theory and to investigate the relationship between customer satisfaction, brand attitude, and sustainable use intention. Poto and Porrone [18] promoted a co-created methodological approach to address the relational dimension of environmental challenges: where critical legal analysis meets illustrated storytelling. Vindigni et al. [19] presented the results of an online survey carried out in Italy with 700 randomly selected participants on consumer attitudes towards food obtained by new plant breeding techniques. Bogdanova et al. [20] developed a study that aimed to reflect on appropriate policies for strengthening resilience and reducing migration outflows in the Arctic Siberian population.

The issues raised in this Special Issue are thought-provoking, and researchers, academics, policymakers, food processors, indigenous peoples, and other stakeholders should reflect on them.

**Author Contributions:** This Special Issue was edited jointly by A.R. and H.H. This editorial was written jointly by the editors. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** We would like to thank and congratulate all the authors who published their manuscripts in this Special Issue with *Sustainability*/MDPI for this valuable data collection. We also thank the reviewers, the *Sustainability* Editor-in-Chief, and the entire MDPI team, without which it would be impossible to construct this successful Special Issue.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**


## *Review* **Negative Affect and Maladaptive Eating Behavior as a Regulation Strategy in Normal-Weight Individuals: A Narrative Review**

**Anna Brytek-Matera**

Katowice Faculty of Psychology, SWPS University of Social Sciences and Humanities, 40-326 Katowice, Poland; abrytek-matera@swps.edu.pl

**Abstract:** Emotions have a powerful influence on eating behavior, and eating behavior can have a powerful effect on emotions. The objective of the present narrative review was to evaluate the relationship between negative affect and maladaptive eating behavior as a regulation strategy in normal-weight individuals. A search of the literature within PubMed®, MEDLINE® and PsycINFO was conducted using a combination of the following terms: "affect", "negative affect", "affect regulation" and "maladaptive eating behavior". A total of 106 papers were identified for full text review and were included in the final set of literature. The manuscript presents an overview of the literature on negative affect and maladaptive eating behavior. It offers a brief overview of restrained, uncontrolled and emotional eating in normal-weight individuals and looks at maladaptive eating behavior used to regulate their affect. Based on the previous research findings, we argue that using more adaptive strategies for emotion regulation (cognitive reappraisal) might result in downregulating integral negative affect to food and in improving eating behavior.

**Keywords:** negative affect; affect regulation; maladaptive eating behavior; normal weight

## **1. Introduction**

Maladaptive eating behavior is a serious problem for health and psychological wellbeing. In eating psychology, two main maladaptive eating behaviors have been defined: restrained eating (persistent and conscious food intake restriction) [1] and disinhibited eating (an incapacity to restrain food intake once begun) [2] divided into emotional eating (overeating in answer to internal cues, e.g., emotions, affect, mood state) and external eating (overeating in response to external cues, e.g., seeing or smelling food) [3]. Maladaptive eating behavior is related to unhealthy attitudes and behaviors regarding food and could also be defined as eating unhealthy food (having less nutritional value and increased intake of high-sugar and high-fat foods) and not eating healthy food.

People are exposed to a multiplicity of external environmental cues in their daily lives, which have an impact on eating or not eating different foods. Human eating behavior is guided by response to food-related cues rather than by a physiological need [4]. Previous studies have suggested that both external and internal cues influence eating behavior [5–7]. Researchers are interested in internal states as, apparently, external environmental cues are unable to explain all of the observed maladaptive eating behavior. One of these internal cues is affect. This term is mainly used in referring to any state that represents how a situation affects a person [8]. Affect is used to describe the physiological, conscious or behavioral components of emotion. It can be described as the superordinate category for emotion episodes, moods, dispositional states and traits [9].

Experiencing negative affect has strong effects on unhealthy eating behavior (e.g., increased food intake in reaction to negative emotions, more palatable and less healthy meals) and poor food choices (e.g., more snacking behavior, a decrease in fruit and vegetable consumption) in both normal and overweight individuals [10–19]. Strategies that

**Citation:** Brytek-Matera, A. Negative Affect and Maladaptive Eating Behavior as a Regulation Strategy in Normal-Weight Individuals: A Narrative Review. *Sustainability* **2021**, *13*, 13704. https://doi.org/10.3390/ su132413704

Academic Editors: António Raposo and Heesup Han

Received: 26 October 2021 Accepted: 9 December 2021 Published: 11 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

individuals can use to regulate affect might be an effective method for changing existing maladaptive eating behaviors and improving them. individuals can use to regulate affect might be an effective method for changing existing maladaptive eating behaviors and improving them.

meals) and poor food choices (e.g., more snacking behavior, a decrease in fruit and vegetable consumption) in both normal and overweight individuals [10–19]. Strategies that

The objective of the present review is to investigate the link between negative affect and maladaptive eating behavior as a regulation strategy in normal-weight individuals (with the body mass index range from 18.5 to 24.99 kg/m<sup>2</sup> ). We first focus on distinctions in negative affect and in restrained eating, uncontrolled eating and emotional eating (maladaptive eating behavior). Next, we examine emotion regulation strategies and their connection with different maladaptive eating behaviors. We finish by arguing that using more adaptive strategies for emotion regulation (cognitive reappraisal) might result in downregulating integral negative affect to food and in improving eating behavior. The objective of the present review is to investigate the link between negative affect and maladaptive eating behavior as a regulation strategy in normal-weight individuals (with the body mass index range from 18.5 to 24.99 kg/m2). We first focus on distinctions in negative affect and in restrained eating, uncontrolled eating and emotional eating (maladaptive eating behavior). Next, we examine emotion regulation strategies and their connection with different maladaptive eating behaviors. We finish by arguing that using more adaptive strategies for emotion regulation (cognitive reappraisal) might result in downregulating integral negative affect to food and in improving eating behavior.

A search of the literature within the electronic databases PubMed®, MEDLINE® and PsycINFO was conducted. The search terms were "affect", "negative affect", "affect regulation" and "maladaptive eating behavior". In the present review, the titles and abstracts of the search results were assessed. For each paper, the type of the study (natural setting and laboratory experiments), the characteristics of the sample and the conclusion/results were defined. The key stages of literature search guidance are presented in Figure 1. A search of the literature within the electronic databases PubMed® , MEDLINE® and PsycINFO was conducted. The search terms were "affect", "negative affect", "affect regulation" and "maladaptive eating behavior". In the present review, the titles and abstracts of the search results were assessed. For each paper, the type of the study (natural setting and laboratory experiments), the characteristics of the sample and the conclusion/results were defined. The key stages of literature search guidance are presented in Figure 1.

*Sustainability* **2021**, *13*, x FOR PEER REVIEW 2 of 15

**Figure 1.** The key stages of literature search guidance for conducting a narrative literature review. **Figure 1.** The key stages of literature search guidance for conducting a narrative literature review.

The author (A.B-M) identified the published studies focusing on the relationship between negative affect and maladaptive eating behaviors over a 20-month period (until 30 September 2021). Only articles published in English were considered. Population-based studies, reviews, systematic reviews and meta-analyses were included in the literature selection process. Case reports, case series, commentary letters and articles published in languages other than English were excluded. The author (A.B-M) identified the published studies focusing on the relationship between negative affect and maladaptive eating behaviors over a 20-month period (until 30 September 2021). Only articles published in English were considered. Population-based studies, reviews, systematic reviews and meta-analyses were included in the literature selection process. Case reports, case series, commentary letters and articles published in languages other than English were excluded.

A narrative review was proposed in an attempt to summarize the literature and to answer the research question on the relationship between negative affect and maladaptive eating behavior as a regulation strategy in normal-weight individuals, focusing especially on restrained eating, uncontrolled eating and emotional eating. The purpose of this narrative review was also to present a theoretical rationale for the relevant role of negative affect on eating behaviors, integrating research in natural and laboratory settings. A narrative review was proposed in an attempt to summarize the literature and to answer the research question on the relationship between negative affect and maladaptive eating behavior as a regulation strategy in normal-weight individuals, focusing especially on restrained eating, uncontrolled eating and emotional eating. The purpose of this narrative review was also to present a theoretical rationale for the relevant role of negative affect on eating behaviors, integrating research in natural and laboratory settings.

## **2. Affect and Maladaptive Eating Behavior**

**2. Affect and Maladaptive Eating Behavior** The link between emotion and eating behavior has always been of interest to human behavior research. Over the last decades, it has been recognized that emotions have a strong impact on eating behavior and that eating behavior can have a powerful influence on emotions [20]. Emotions can induce changes in eating behavior [21], and eating is per se related to emotions [22]. Emotion is a complex reaction pattern being composed of subjective experience, expressive behavior (e.g., facial, bodily) and peripheral physiological responses (e.g., respiration, heart rate) [23]. Specific emotions such as anger, fear, joy and sadness affect eating responses in motivation to eat, food choice, affective reaction to food, The link between emotion and eating behavior has always been of interest to human behavior research. Over the last decades, it has been recognized that emotions have a strong impact on eating behavior and that eating behavior can have a powerful influence on emotions [20]. Emotions can induce changes in eating behavior [21], and eating is per se related to emotions [22]. Emotion is a complex reaction pattern being composed of subjective experience, expressive behavior (e.g., facial, bodily) and peripheral physiological responses (e.g., respiration, heart rate) [23]. Specific emotions such as anger, fear, joy and sadness affect eating responses in motivation to eat, food choice, affective reaction to food, eating speed, metabolism and digestion [24].

eating speed, metabolism and digestion [24]. Furthermore, studies have found that positive and negative emotions can differ in their effects on eating. Negative emotions (such as anger, fear and sadness) may result in an increase in food intake and the consumption of unhealthy food (junk food) but in a Furthermore, studies have found that positive and negative emotions can differ in their effects on eating. Negative emotions (such as anger, fear and sadness) may result in an increase in food intake and the consumption of unhealthy food (junk food) but in a decrease in food pleasantness in individuals who use eating as way to regulate their negative emotions [24]. Negative emotions, such as boredom, may be related to increased

appetite, but sadness may be related to decreased appetite [25]. In contrast, positive emotions, such as joy or happiness, can increase food pleasantness and the intake of healthy foods [24,26–28]. Positive emotions, in general, seem to be relevant triggers for eating indulgent food amongst healthy individuals with a normal weight [28]. A metaanalysis (including 33studies with a total of 2491 participants including healthy controls and patients with an eating disorder and with obesity) on how negative and positive emotions affect food intake across laboratory settings [29] showed that, overall, negative emotions resulted in increased eating (a small effect). Other outcomes from a meta-analysis based on laboratory-based studies (which included 20 studies with a total of 3670 participants including healthy controls and individuals with pathological eating behavior [30]) found a lack of an overall effect of negative emotions on eating behavior. In addition, positive emotions had a small effect on eating behavior, and overall positive emotions lead to increased food intake.

The empirical results on emotion-induced changes in eating are contradictory. On the one hand, high-intensity emotions are believed to suppress eating (these emotions influence on the autonomic nervous system activity that triggers physiological changes, e.g., slowed gastric emptying, the release of appetite-inhibiting hormones, that may induce satiety [31]. On the other hand, high-arousal emotions (such as anger) have been found to increase food consumption [26,27] or to not decrease eating in reply to highly intense emotions (results of most laboratory studies). In relation to moderately intense emotions, it is supposed that negative and positive emotions increase food consumption among people with a more controlled eating style [32]. To sum up, empirical results reflect the same inconsistencies as the discrepancy in views on how emotions impact eating [30]. Moreover, although extensive research has been carried out on negative emotions and eating, the very fundamental question of whether negative emotions influence eating, and in whom, remains unclear. It is worth pointing out that much research has been conducted on disordered eating among patients with eating disorders or obesity, and a much smaller number of studies have been devoted to maladaptive eating behavior among normal-weight individuals without a diagnosis of an eating disorder or obesity.

## *2.1. Negative Affect*

Negative affect increases over time to the point where disordered eating occurs as a maladaptive emotion regulation strategy [33]. Some authors suggest that negative affect reduces after maladaptive eating behavior [34], whereas others point out that it does not decrease or continues to increase [35]. Taking into account the existing literature, it seems that negative affect and maladaptive eating behavior are interrelated and causally linked.

Negative affect is described as feelings of emotional distress [36]. An extensive literature has shown that negative affect leads to maladaptive eating behavior (e.g., overconsumption of high energy density foods or highly palatable foods), but it is not yet clear exactly how and why this happens. Prior work suggests that negative affect may lead to maladaptive eating because maladaptive eating reduces aversive affective states or because negative affect impairs top-down control. Thus, maladaptive eating behavior occurs, at least in part, in response to negative affective states [24,31,37].

Numerous laboratory and field studies have shown that various forms of negative affect (including stress and other negative emotional states) lead to maladaptive eating behavior, which includes both the overconsumption of unhealthy food and the underconsumption of healthy food [18,24,37]. Laboratory studies have shown that incidental and experimentally induced negative affect leads to maladaptive eating behavior with regard to both hypothetical food choices and actual eating behavior [17,38]. Crosssectional and longitudinal field studies using self-report meal questionnaires, as well as ecological momentary assessment, have also shown that higher levels of negative affect predict maladaptive eating behavior [31,39]. Even though there is a known, robust relationship between negative affect and maladaptive eating behavior, it is not yet clear

which of the two possible pathways between negative affect and maladaptive eating behavior is most important.

Negative affect can impact typical eating behavior in two ways. In the first pathway, negative affect causes an increase in tasty food craving (often unhealthy), and consumption of foods with a higher energy density (i.e., have a high calorie content, such as hamburgers or candy) [40,41]. Researchers have shown that, in at least some cases, individuals use tasty, highly rewarding high energy density food as a means of reducing negative affect [28,42]. In the second pathway, negative affect impairs top-down control over behavior, which studies have shown to be often required for choosing healthy, low energy density foods which also have vitamins, minerals and nutrients that play essential roles in a healthy diet [43,44]. While some people may find it rewarding to engage in healthy eating, the full benefits of maintaining a healthy diet come with more delay than the reward of eating tasty food [45]. Choosing to align eating (and other) behaviors with the pursuit of more delayed rewards is thought to require top-down control, and in the second pathway, negative affect disrupts the effective exercise of such control [43,46,47].

There are at least two pathways by which negative affect influences eating: by increasing the consumption of tasty, high energy density food as a means of reducing affect or by decreasing consumption of healthy, low energy-density food due to impaired top-down control [43]. In our model, these two pathways reflect the fact that negative affect might have an impact on eating either by increasing the weight given to taste or by decreasing the weight given to health in dietary decisions. As already mentioned, the taste value of a food is a marker of the immediate reward that can be used to palliate negative affect [42], whereas the health value of a food is the delayed reward that a person must represent using top-down control in order to make a healthy food choice [48]. Given that affect regulation leads to decreases in negative affect, such regulation should lead to improvements in eating behavior via both pathways, in other words, both via a decreased intake of tasty, high energy density foods and via an increased intake of healthy, low energy density foods.

It is worth adding that food choices in three eating situations, a neutral/typical meal (foods with medium nutrient density and medium energy), a healthy meal (foods with high nutrient density and low energy) and an unhealthy meal (foods with low nutrient density and high energy), provide information about a range of prototypical behaviors [49]. The actual process of making healthy choices is more difficult than making unhealthy ones in normal-weight individuals. Prior work suggests that negative affect may impair top-down control over behavior (which is needed to make healthy food choices) which may lead to underconsumption of healthy foods. Choosing tasty unhealthy foods does not require top-down control over behavior, whereas choosing untasty healthy foods does. In addition, the suppression of a thought (e.g., planning not to eat unhealthy snacks) may lead to this thought becoming more prevalent and will result in the increased consumption of unhealthy food [49].

Previous research studies have tested whether affect regulation can be used to decrease maladaptive eating [50,51]. However, in nearly all of these studies participants downregulated food-related affect (i.e., craving for food) rather than negative affect which is incidental to food (but which may be a cause of maladaptive eating behavior). Prior studies have shown that affect regulation strategies can effectively reduce craving for unhealthy foods, but all previous work has deployed these strategies to reduce craving itself rather than to downregulate the negative affect that may be a root cause of the craving [50,51]. It is worth pointing out that although many studies have examined negative affect and eating behavior, few studies have attempted to dissociate the contributions of two known pathways from negative affect to maladaptive eating behavior—the use of tasty food to cope with negative affect or the inability to choose healthy food due to impaired top-down control.

## *2.2. Integral and Incidental Affect 2.2. Integral and Incidental Affect*

Distinguishing different affective experiences could help us to understand the decisionmaking at hand, as well as the confounding findings about the influence of affect on decision making. It is important to differentiate two types of affects, integral and incidental, because they have a substantial influence on decision making and final judgment [52]. Integral (or endogenous) affect is defined as an affect stemming from consideration of the decision or judgmental target itself (i.e., food craving); a "genuine" subjective reaction to a target [53]. In other words, integral affect concerns experienced feelings about a stimulus [54]: for example, how people feel about various choice options while purchasing a food product. In making a decision, people can use their affective reactions towards options as proxies for values and use them as information in the evaluation of the options [55]. Individuals' experiences of integral affect allow them to categorize experiences on a good– bad dimension and enable them to reach a decision [56]. To sum up, integral affect is linked with the decision, decision attributes or to the decision situation. It can come about through anticipatory thinking (thinking about possible outcomes) or through activation by an actual stimulus (e.g., when a task associated with a decision is presented in a pleasant or unpleasant way) [57]. Distinguishing different affective experiences could help us to understand the decision-making at hand, as well as the confounding findings about the influence of affect on decision making. It is important to differentiate two types of affects, integral and incidental, because they have a substantial influence on decision making and final judgment [52]. Integral (or endogenous) affect is defined as an affect stemming from consideration of the decision or judgmental target itself (i.e., food craving); a "genuine" subjective reaction to a target [53]. In other words, integral affect concerns experienced feelings about a stimulus [54]: for example, how people feel about various choice options while purchasing a food product. In making a decision, people can use their affective reactions towards options as proxies for values and use them as information in the evaluation of the options [55]. Individuals' experiences of integral affect allow them to categorize experiences on a good–bad dimension and enable them to reach a decision [56]. To sum up, integral affect is linked with the decision, decision attributes or to the decision situation. It can come about through anticipatory thinking (thinking about possible outcomes) or through activation by an actual stimulus (e.g., when a task associated with a decision is presented in a pleasant or unpleasant way) [57].

On the other hand, incidental (or exogenous) affect includes all factors that induce affect but are unrelated to the judgmental target or the decision being made [53]. In other words, feelings independent of a stimulus, such as mood states, can be misattributed to it or can have an effect on decision processes [54]. Similarly to integral affect, incidental affect can also influence judgments and decisions [58] (Figure 2). To sum up, the distinction between integral and incidental affect based on its relevance to decisions may provide the key to explaining the complex influence of affect on psychological processes. On the other hand, incidental (or exogenous) affect includes all factors that induce affect but are unrelated to the judgmental target or the decision being made [53]. In other words, feelings independent of a stimulus, such as mood states, can be misattributed to it or can have an effect on decision processes [54]. Similarly to integral affect, incidental affect can also influence judgments and decisions [58] (Figure 2). To sum up, the distinction between integral and incidental affect based on its relevance to decisions may provide the key to explaining the complex influence of affect on psychological processes.

**Figure 2.** Integral and incidental affect and its influence on decision making. **Figure 2.** Integral and incidental affect and its influence on decision making.

Both mild incidental and integral affects are omnipresent in daily life [54] and interact with each other (Figure 2). Thus, a food consumption decision may be affected by two different types of affect: affect integral to the decision (food craving) and affect incidental to the choice (mood state). A previous review [52] has suggested that if integral and incidental affect is concurrently present, (1) integral affect dominates the overall response (a current incidental mood will have a significant effect on the overall judgment when integral affect is moderate or low in intensity), and (2) incidental affect has a significant influence on the integral response (when incidental affect is salient). In addition, the abovementioned review [52] has found that incidental affect congruent with the target may be beneficial to efficient decision making (it may amp up integral affect or the overall affective reaction), while incidental affect incongruent with the target may be detrimental (may attenuate the response). Both mild incidental and integral affects are omnipresent in daily life [54] and interact with each other (Figure 2). Thus, a food consumption decision may be affected by two different types of affect: affect integral to the decision (food craving) and affect incidental to the choice (mood state). A previous review [52] has suggested that if integral and incidental affect is concurrently present, (1) integral affect dominates the overall response (a current incidental mood will have a significant effect on the overall judgment when integral affect is moderate or low in intensity), and (2) incidental affect has a significant influence on the integral response (when incidental affect is salient). In addition, the abovementioned review [52] has found that incidental affect congruent with the target may be beneficial to efficient decision making (it may amp up integral affect or the overall affective reaction), while incidental affect incongruent with the target may be detrimental (may attenuate the response).

Both integral and incidental affect play relevant roles in judgment and decision-making processes [54]. First, affect can act as information. Second, it can act as a spotlight concentrating people on different information depending on the extent of their affect and allowing them to compare the values of very different decision options or information. In Both integral and incidental affect play relevant roles in judgment and decisionmaking processes [54]. First, affect can act as information. Second, it can act as a spotlight concentrating people on different information depending on the extent of their affect and allowing them to compare the values of very different decision options or information. In this case, the two-step approach should be taken into consideration: (1) the extent

(e.g., weak vs. strong affect) or type of affective feelings (e.g., anger versus fear) focuses the decision maker on new information, and (2) the new information is utilized to guide the judgment or decision. Third, affect seems to be a motivator of information processing and behavior. Lastly, affect has been associated with the extent of systematic processing in decision making [54].

## Subtypes of Incidental Affect

Incidental affect (affect that is unrelated to the decision) influences decision making (non-normative influence). There are two sources of incidental affect: dispositional (trait) affect and situational (state) affect [53]. Dispositional affect is related to a tendency to respond in a special affective way to a diversity of events across time and situations. On the other hand, situational affect is affected by incidental moods and emotions and depends on the valence of the emotion and on specific emotion effects. Even minimal sensory cues can contribute to this type of affect and influence consecutive decision making [53]

## *2.3. Maladaptive Eating Behavior*

Eating behavior is an umbrella term that includes food choice and motives, dieting, feeding practices and eating-related pathologies such as eating disorders and obesity [59]. Eating behavior is complexly affected by psychological, physiological, nutritional, sociological and cultural factors. The modern eating patterns followed by the U.S. population are not adjusted to the Dietary Guidelines: about three-fourths of the population consume an eating pattern low in fruits, vegetables, dairy and oils, and most of the population does not follow nutritional advice and does not follow added sugars, saturated fats and sodium recommendations (U.S. Department of Health and Human Services and U.S. Department of Agriculture, 2015). Eating- and weight-related issues are highly prevalent in both the United States and Europe.

In the literature, three major maladaptive eating behaviors have been described: restrained eating, uncontrolled eating and emotional eating. It is worth pointing out that these types of maladaptive eating behaviors can be found in healthy, normal-weight individuals. Restrained eating is related to the intention to restrict food intake for the purpose of preventing weight gain or promoting weight loss [60]. Restrained eating is not equivalent to dieting, mainly because it solely illustrates the intent (not the action) of food restriction [61]. Nevertheless, restrained eating has often been linked to a total restricting dietary energy intake [62], a lower total energy intake [63] (e.g., lower meal and snack frequencies, breakfast skipping), disinhibited overeating as an effect of a loss of cognitive control [1] (restrained eating is under cognitive control rather than physiological one) and higher body mass index [60]. For decades, restraint theory indicated that restrained eating elicits counter-regulatory responses, reduces the sensitivity of the individual to satiety signals and leads to dietary disinhibition (associated with, e.g., overeating, loss of control over energy intake [64,65]. In the 1970s, laboratory-based studies revealed that individuals trying to reduce their energy intake for achieving weight control consumed more palatable foods with regard to high-calorie preloads [65]. This brought about the development of 'Restraint Theory' [1,6], according to which restrained eating is under the cognitive control of eating (e.g., eating in response to rigid dietary rules) and replaces eating in response to physiological cues. Sensitivity to internal cues for satiety is reduced and results in disinhibition and intake of large amounts of food that is not associated with hunger [35] in situations where cognitive control is weakened. Even a minor violation of rigid diet rules (e.g., by consuming high-calorie, "forbidden" foods) can lead to ignoring dietary rules (cognitive abandonment of the rule) to disinhibit the suppressed eating desires [1] and to overeating. Numerous studies have demonstrated that restrained eaters also increase food intake in response to negative emotions, possibly because these emotions deplete the cognitive resources needed for abiding by the dietary rules [29,66]. To sum up, evidence for emotional overeating is surprisingly inconsistent [66]. Cardi et al. [29] found evidence for emotional overeating in negative mood across studies among restrained eaters

and individuals with binge eating symptomatology [29], while a more comprehensive and recent meta-analysis questioned it [30]. The present meta-analysis has demonstrated that solely restrained eaters were found to be vulnerable to negative-emotion-induced eating, and negative emotions did not influence eating behavior amongst self-reported emotional eaters.

Uncontrolled eating (sometimes called external eating) is characterized by the overeating of unhealthy food in reaction to external food cues [3]. Individual differences in uncontrolled eating could be explained by two psychological processes. The first one is reduced cognitive control (processes that permit individuals to behave in a goal-directed manner, including inhibition, interference control, cognitive flexibility and working memory) [67]. The second process is automatic action tendencies towards external food cues, which are assumed to be modulated by reward networks in the brain [68]. The previous findings [69,70] have suggested that overconsumption in response to external cues may represent a general concept of uncontrolled eating characterized by low perceived self-control and high-calorie food consumption.

Emotional eating, also referred to as 'comfort eating' [71] or 'stress-induced eating' [21], indicates overconsumption in response to negative emotions [72]. Emotional eating involves a conscious or unconscious excess food consumption (including sweetened, salty or fatty foods) for reasons other than physical symptoms such as hunger. Emotion-congruent eating versus emotion-regulating eating can explain the influence of emotions on the quantity and quality of food intake. Emotion-congruent eating means that positive emotions increase and negative emotions decrease the motivation and pleasure of eating. Emotionregulating eating, also known as 'mood control eating,' explains that food intake serves to reduce unpleasant emotions (e.g., eating ice cream to relieve sadness) [73]. Several explanations regarding the psychological mechanism of emotional eating have been proposed. Psychosomatic theory underlines that overeating in response to negative emotions results from a lack of interoceptive awareness (e.g., an internal sensation of hunger), the incapacity to differentiate hunger sensations from arousal because of other aversive internal states or eating as a way to reduce negative emotions [32]. Psychological models regarding emotional eating [74,75] emphasize overconsumption in response to negative emotions as a maladaptive emotion regulation strategy. Masking theory demonstrates that overeating is an attempt to misattribute perceived stress to eating in order to divert an individual's attention from the original source of distress [74]. It is worth pointing out that while learning theories indicate that emotional overeating primarily fulfils an emotional regulatory function, cognitive theories indicate that emotional overeating results from disinhibition rather than from emotional regulation [66]. According to all these theories, before overeating occurs, individuals are unable to regulate negative affect that they experience, inducing them to use a maladptive strategy they do have access to, namely, overeating [66]. This suggests that the problem is not necessarily related to negative emotional experiences per se but rather with the absence of adaptive emotion regulation strategies available to regulate negative affect. Recent reviews have demonstrated inconsistent results as to which theory best explains emotional overeating [66].

Emotional eating may be the outcome of lower interoceptive awareness, difficulty with recognizing physiological cues of hunger or satiety and emotion regulation difficulties [76–78]. Emotional eating has also been found to be associated with overeating, excessive intake of sweet, high-fat and energy-dense foods [79], weight gain and difficulties losing weight [80], depression [81], overweight and obesity [81] and poor inhibitory control [69]. The findings propose that emotional eating might be an indicator of overconsumption generally and not specifically in the presence of negative or positive [29,69] emotions.

## **3. Affect Regulation and Maladaptive Eating Behavior**

Affect regulation is fundamentally considered as a mechanism by which individuals initiate, maintain, modulate or change the occurrence, intensity or duration of their own

emotions, moods and feelings [82] so as to pursue an affective equilibrium or homeostasis (maximize pleasant experiences and minimize unpleasant ones) [83]. According to [9], affect regulation is superordinate to coping, emotion regulation, mood regulation and traditional ego-defensive processes. One of several major forms of affect regulation is emotion regulation.

## *3.1. Emotion Regulation Strategies*

The previous section showed that negative affect is a source of many difficulties connected with eating. Therefore, downregulating negative affect could lead to improvements across a wide variety of maladaptive eating behaviors. For this, adaptive emotion regulation is advisable for successfully reducing negative affective states, strengthening or controlling positive affective states and restoring emotional balance [84].

Emotion regulation has been described as a subtype of both behavioral self-regulation and coping [85]. Emotion regulation as a construct has been described in a number of different ways. One of the best-known models [9,86] defines emotion regulation as the efforts individuals engage to impact the experience and expression of their emotions. Emotion regulation strategies are comprised of two components: antecedent- and response-focused strategies [87]. Antecedent-focused strategies are adopted before the complete activation of emotion response tendencies has taken place and have changed behavioral and peripheral physiological responding, whereas response-focused strategies appear once an emotion is already underway, thus, after the response tendencies have been generated [86]. There are two prototypical strategies that are commonly used in daily life: cognitive reappraisal and expressive suppression [86]. The first one is an antecedent-focused strategy that requires reframing or changing the way of thinking about an emotion-prompting situation in order to change the emotional effect of a situation once it has occurred. Expressive suppression, on the other hand, is a response-focused strategy that entails actively inhibiting the internal experience and external expression of emotion after emotional activation has occurred [86]. These two strategies differ in the required amount of self-regulatory resources and have different consequences. Generally, cognitive reappraisal appears to change the primary appraisals of emotional stimuli without the need for persistent self-regulatory effort over time [88]. It is associated with less experience and less expression of negative emotion, less physiological activation and more positive experience of emotion, which means that this strategy can be regarded an adaptive emotion regulation strategy. In contrast, expressive suppression entails active efforts to inhibit dominant responses, resulting in comparatively greater "resource depletion" than reappraisal [28]. It is associated with increases in physiological responding and decreases in behavioral expression, but it ends in failure to reduce the experience of emotion, which is why this strategy can be regarded as a maladaptive one [89].

It has been noted that some individuals use eating as a strategy to regulate their emotions. The means in which emotions are regulated influence eating behavior [28]. Some individuals become involved in eating as a way to downregulate negative emotions, which is likely because of using more maladaptive emotion regulation skills. Impaired emotion regulation is related to difficulties in regulating eating behavior. Deficits in emotion regulation skills result in dysregulated (overeating sweet or high-caloric foods) or overregulated eating behavior, which may result in underweight, malnutrition or excess body weight (Figure 3). The regulation strategies used to cope with negative emotions are responsible for increased eating (emotional eating). It is worth pointing out that the use of more adaptive emotion regulation strategies (e.g., cognitive reappraisal) might result in reducing maladaptive eating behavior. Cognitive reappraisal has been widely theorized to be protective against psychopathology [90], eating pathology and eatingrelated symptoms (medium to large effect size) [91]. Thus, it is plausible that cognitive reappraisal strategies, in particular those concentrating on the benefits of not eating, could potentially enhance the capacity to reduce unhealthy food intake [92]. We argue that using cognitive reappraisal might be useful in downregulating integral negative affect associated

with food by improving eating behavior (making more very healthy choices and fewer unhealthy ones, i.e., placing a higher weight on health when making dietary decisions). food by improving eating behavior (making more very healthy choices and fewer unhealthy ones, i.e., placing a higher weight on health when making dietary decisions).

*Sustainability* **2021**, *13*, x FOR PEER REVIEW 9 of 15

symptoms (medium to large effect size) [91]. Thus, it is plausible that cognitive reappraisal strategies, in particular those concentrating on the benefits of not eating, could potentially enhance the capacity to reduce unhealthy food intake [92]. We argue that using cognitive reappraisal might be useful in downregulating integral negative affect associated with

**Figure 3.** Emotional eating as a consequence of maladaptive emotion regulation strategy. **Figure 3.** Emotional eating as a consequence of maladaptive emotion regulation strategy.

Emotion dysregulation has been described as a potential transdiagnostic risk factor [93] and a robust correlate of disordered eating behaviors (including a spectrum of maladaptive eating behaviors and cognitions linked to negative psychological and physiological health outcomes). Emotion regulation difficulties are related to increased binge eating [94], dietary restraint [95] and eating pathology in general [91]. Emotion regulation has mostly been recognized as a correlate of emotional eating [28,96]. Recently, Braden and colleagues [96] examined the psychological (emotion regulation and disordered eating behaviors) correlates of emotional eating across negative and positive emotional eating dimensions, determining that negative emotional eating was linked to increased emotion regulation difficulties and disordered eating behaviors. These data are consistent with research founding a positive association between negative emotional eating and emotion regulation difficulties [78]. Emotion dysregulation has been described as a potential transdiagnostic risk factor [93] and a robust correlate of disordered eating behaviors (including a spectrum of maladaptive eating behaviors and cognitions linked to negative psychological and physiological health outcomes). Emotion regulation difficulties are related to increased binge eating [94], dietary restraint [95] and eating pathology in general [91]. Emotion regulation has mostly been recognized as a correlate of emotional eating [28,96]. Recently, Braden and colleagues [96] examined the psychological (emotion regulation and disordered eating behaviors) correlates of emotional eating across negative and positive emotional eating dimensions, determining that negative emotional eating was linked to increased emotion regulation difficulties and disordered eating behaviors. These data are consistent with research founding a positive association between negative emotional eating and emotion regulation difficulties [78].

Most studies have shown that suppression is linked to higher levels of distress and cognitive reappraisal appears to enhance subsequent behavioral self-regulation outcomes [86,88]. In addition, suppression is related to poorer behavioral self-regulation. There are few laboratory-based studies testing the impact of emotional suppression and cognitive reappraisal on eating behavior following negative mood induction. Vohs and Heatherton [97] found that when participants were requested to suppress their emotional reactions (they watched a stimulus video clip planned to induce negative affect), they reported higher levels of food intake, particularly ice cream, demonstrating that suppression is related to decreased subsequent behavioral restraint. Evers and colleagues [28] employed similar emotion-induction procedures but evaluated the impacts of habitual or trait emotion regulation styles (high or low suppression, and high or low cognitive reappraisal) of food consumption [28]. Suppression moderated the relation between sad mood and food intake, such that high suppression participants consumed considerably more than participants who do not frequently make use of suppression. In addition, the previous study demonstrated that suppression was equally efficacious as acceptance in limiting reported chocolate consumption over a week [98]; however, those in the suppression condition consumed significantly more chocolate during the follow-up laboratory session. Most studies have shown that suppression is linked to higher levels of distress and cognitive reappraisal appears to enhance subsequent behavioral self-regulation outcomes [86,88]. In addition, suppression is related to poorer behavioral self-regulation. There are few laboratory-based studies testing the impact of emotional suppression and cognitive reappraisal on eating behavior following negative mood induction. Vohs and Heatherton [97] found that when participants were requested to suppress their emotional reactions (they watched a stimulus video clip planned to induce negative affect), they reported higher levels of food intake, particularly ice cream, demonstrating that suppression is related to decreased subsequent behavioral restraint. Evers and colleagues [28] employed similar emotion-induction procedures but evaluated the impacts of habitual or trait emotion regulation styles (high or low suppression, and high or low cognitive reappraisal) of food consumption [28]. Suppression moderated the relation between sad mood and food intake, such that high suppression participants consumed considerably more than participants who do not frequently make use of suppression. In addition, the previous study demonstrated that suppression was equally efficacious as acceptance in limiting reported chocolate consumption over a week [98]; however, those in the suppression condition consumed significantly more chocolate during the follow-up laboratory session.

Previous research [28] has demonstrated that maladaptive emotion regulation strategies resulted in increased comfort food intake (sweet or salty foods) compared with Previous research [28] has demonstrated that maladaptive emotion regulation strategies resulted in increased comfort food intake (sweet or salty foods) compared with adaptive strategies and with spontaneous emotion expression. The findings also revealed that (1) individuals regularly using suppression ate more when being emotional than individuals rarely using this strategy and (2) participants who suppress their negative emotions consumed more comfort foods than those who reappraise their emotions. To sum up,

these mixed results confirm the thesis that the way people regulate their negative emotions modulates the amount of food intake.

Taut, Renner and Baban [99] investigated the effects of negative emotions (fear, negative affect) and emotion regulation strategies (suppression, cognitive reappraisal) on food consumption in a neutral control condition where participants chose whether and how much they desired to consume and also whether they wanted to consume at all (ad libitum food intake). The authors examined whether participants use eating as a secondary coping strategy when emotion regulation is ineffective. The majority of participants in the reappraisal group were less likely to consume both chocolate and crisps in comparison to the control and suppression groups. However, among individuals who ate, there was no difference in the amount consumed across conditions. Thus, the main discrepancy between the three emotion regulation strategies appears to be whether or not eating is utilized as a secondary regulation strategy at all rather than differences in the amount of food required for secondary regulation as indicated in previous research [28]. The main difference between suppression and reappraisal is whether or not eating is needed as a secondary coping strategy, rather than differences in the amount of food intake per person as proposed in the study by Evers et al. [28]. The findings indicate that when individuals are faced with a negative event, eating is employed as a secondary coping strategy when the adopted emotion regulation strategy is ineffective. Inversely, an adaptive emotion regulation strategy, such as reappraisal, reduces the probability of food consumption, even when emotion regulation is utilized during rather than before the unfolding of the negative event. Thus, the means people cope with negative emotions might be more appropriate for elucidating emotional eating than the distress itself [28].

Cognitive reappraisal, such as thinking of long-term health consequences of eating unhealthy food when regarding images of such foods, enhances inhibitory region activation (less inhibitory control is connected with greater weight gain), decrease reward region (hyper-responsivity of the reward region contributes to overeating) and attention region activation, as well as to prevent weight gain [100]. A recent study [101] has shown that cognitive strategies reduce unhealthy and enhance healthy food consumption (craving). Thus, changes in craving may affect the consumption of both healthy and unhealthy foods. These findings evidence that training-based interventions (specifically, regulation of craving training) affect eating behavior (increasing healthy food choices in the face of enticing unhealthy options) and can reduce unhealthy eating (reducing total caloric consumption, particularly of high-caloric or unhealthy foods) [101] and might be beneficial in helping people enhance their recruitment of inhibitory regions when faced with high-fat or high-sugar foods [100].

## *3.2. Applying Affect Regulation to Incidental Affect in the Context of Eating*

Understanding how incidental affect influences food intake is an important topic. The previous studies showed that incidental affect influences in-store shopping [102] and in-home food choice [103]. In addition, the findings showed that incidental affect (sadness and happiness) impacts food consumption within a general population [104]. A previous study [104] showed that incidental affect influences consumption levels at the individual level. Consumption levels of a hedonic product are lower for individuals in a state of happiness than for those in a state of sadness. In other words, while sad individuals presented a substantial refuse in their consumption, happy individuals appeared to be unswayed by nutritional information. Thus, it seems that happy people are already avoiding food intake, and the presence of nutritional information does not force their food intake any lower. Conversely, sad people took of liberty of trying and overcoming their negative state by eating more (in the information-absent condition) [104]. This study also showed that happy people consumed more raisins in comparison with sad people. In the lack of mood-changing cues, affective evaluation predominates, therefore, people comport according to their mood state (i.e., happy people have positive evaluations and

tend to consume more, while sad people have negative evaluations and tend to consume less) [104].

## **4. Directions for Future Research**

The present review has shown that negative affect is a source of many difficulties connected with eating. Therefore, downregulating negative affect could lead to improvements across a wide variety of maladaptive eating behaviors. For this, adaptive emotion regulation is advisable for successfully reducing negative affective states and strengthening or controlling positive affective states [85].

A rich literature has shown that people can and do use a host of different affect regulation strategies to regulate affective responses including stress responses and negative emotions and moods [85]. Maladaptive emotion regulation strategies (such as suppression of emotions) are positively associated with emotional eating [76]. While suppression appears to be maladaptive in terms of increased comfort food intake in comparison to reappraisal [28], reappraisal seems to be related to reduced food intake. Correlational studies found that negative affect did not predict eating behavior among non-clinical samples [28,99], however many limitations exist in the previous and current studies, therefore further research in this area is needed. In addition, the exact process of emotions affecting eating behavior is still uncharted.

Cognitive reappraisal applied to incidental affect might be more effective than some of prior efforts. Additional studies are needed in order to further investigate the effect that specific emotion regulation strategies have on maladaptive eating behavior in normalweight individuals. These results could provide worthwhile insights into the emotional mechanisms underlying maladaptive eating behavior.

Narrative review is the limitation of the present work. Future work should focus on a systematic review, currently widely considered as studies with the highest level of evidence, and follow a set of strict established guidelines, such as PRISMA or Joanna Briggs Institute guidelines.

In addition, in an ongoing global pandemic of coronavirus disease, individuals can present unfavorable changes in eating behavior [105], therefore, it would be needed to investigate changes in eating behavior and emotion regulation in normal weight individuals during the COVID-19 pandemic.

## **5. Conclusions**

The scientific literature (field and experimental studies) provides clear evidence that negative emotions and maladaptive emotion regulation strategies influence maladaptive eating behavior. It is assumed that moderate arousal or moderately intense emotions affect eating [30].

It is plausible that increased food consumption may be an attempt to downregulate negative emotion. The previous research has demonstrated that the way in which individuals cope with negative emotions (rather than experience negative emotions) may determine the influence of emotion on eating behavior [28]. The more "costly" an emotion regulation strategy is in terms of consuming self-regulatory resources, the more individuals are likely to increase food intake as a secondary regulation strategy [99]. Continuous involvement in self-regulatory acts (such as abstaining from eating or regulating one's emotion states) could lead to resource depletion and a reduction in skill to self-regulate at a later point [106].

Some individuals use eating in order to face their negative emotions and regulate them. That leads to increased or decreased eating and is linked to an increased use of maladaptive emotion regulation strategies. Therefore, it is important to teach normal-weight individuals to use more adaptive emotion regulation strategies to properly manage their emotions and affect. Maintenance of advantageous emotion regulation is necessary in those people in order to have adaptive eating behavior without over-control or loss of control over eating. The use of more adaptive strategies for emotion regulation (cognitive reappraisal) might

reduce maladaptive eating behavior. Therefore, effective interventions can help to sustain eating behavior change.

**Funding:** This research was funded by the National Science Centre, Poland under the Harmonia 10 research project (no. 2018/30/M/HS6/00022).

**Institutional Review Board Statement:** The Harmonia 10 research project was approved by the Research Ethics Committee at the Institute of Psychology, University of Wroclaw, Poland (no. IPE 0019).

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** A.B.-M. appreciates and thanks James J. Gross (Stanford University) and Daniel James O'Leary (University of Chicago) for providing constructive suggestions and comments.

**Conflicts of Interest:** The author declare no conflict of interest.

## **References**


## *Review* **The Relationships among Microelement Composition of Reindeer Meat (***Rangifer tarandus***) and Adaptation: A Systematic Review and Meta-Analysis**

**Sergei Andronov 1,2,\* , Andrey Lobanov <sup>1</sup> , Elena Bogdanova 3,\* , Andrei Popov <sup>1</sup> , Alexander Yuzhakov <sup>4</sup> , Olga Shaduyko <sup>2</sup> , Dele Raheem <sup>5</sup> and Irina Kobelkova <sup>6</sup>**


**Abstract:** This systematic review and meta-analysis based on PRISMA statements aimed to summarise the data on the chemical composition of reindeer meat depending on the region of the *Rangifer tarandus*. We searched SCOPUS, PubMed, Embase, CrossRef, Medline, Cochrane library, eLibrary, and CyberLeninka. A total of 3310 records published between January 1980 and December 2021 were screened. We identified 34 relevant studies conducted in Russia, Norway, the USA, Canada, and Finland for the synthesis. Overall, the consumption of reindeer meat reduces arterial hypertension and atherosclerosis due to many polyunsaturated fatty acids (linoleic, linolenic, arachidonic) and vitamin C, which balances lipid fractions. Venison is an effective means of preventing obesity and adapting to cold due to the content of a complete set of essential trace elements, amino acids, and even L-carnitine. The high content of vitamin C and microelements (iron, zinc, copper) in reindeer meat is likely to increase the body's antioxidant defence against free radicals and help prevent chronic non-infectious diseases. Thus, venison is an essential component of the adaptation mechanism for the Arctic population.

**Keywords:** systematic review; reindeer meat; macro- and microelement analysis; adaptation; Arctic population; meta-analysis

## **1. Introduction**

The unique nutrition of the Arctic Indigenous Peoples is associated with their increased endurance, health, and adaptability to the harsh climate [1]. Reindeer meat, blood, and liver are the most critical elements of this traditional nutrition enriched with minerals [2,3]. Reindeer consumption is a crucial factor of successful adaptation to the cold stress, as well as a component of national culture, food, and economic security and sovereignty, affecting the well-being and health of the Indigenous population in the Arctic [4–9].

The reindeer (*Rangifer tarandus*) habitat covers territories in Eurasia and North America between 50- and 81-degrees north latitude [10] and includes continental and island territories, tundra, taiga, and mountainous areas close to them in vegetation composition and climatic conditions [11]. Reindeer live in Russia, the USA, Norway, Sweden, Finland,

**Citation:** Andronov, S.; Lobanov, A.; Bogdanova, E.; Popov, A.; Yuzhakov, A.; Shaduyko, O.; Raheem, D.; Kobelkova, I. The Relationships among Microelement Composition of Reindeer Meat (*Rangifer tarandus*) and Adaptation: A Systematic Review and Meta-Analysis. *Sustainability* **2022**, *14*, 1173. https://doi.org/ 10.3390/su14031173

Academic Editor: Filippo Giarratana

Received: 30 December 2021 Accepted: 17 January 2022 Published: 20 January 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Denmark, Iceland, Canada, Mongolia, Great Britain, and China [10]. The largest populations of wild reindeer (*Rangifer tarandus caribou*) are in Russia (952.9 thousand; 2015) and Canada (1300 thousand; 2016) [11]. The world's largest livestock of domesticated reindeer is in Russia (1620.8 thousand reindeer in 2021) [12]. In Russia, the largest population of wild reindeer is in the Krasnoyarsky Krai, the Chukotka Autonomous Okrug, the Republic of Sakha (Yakutia), and domesticated reindeer are in the Yamal-Nenets Autonomous Okrug [13]. Such various reindeer habitats make pre-conditions for the different chemical compositions of reindeer products in different northern regions.

The macro- and microelement composition of reindeer meat is impacted by significant differences in the species and mineral composition of forages (plants and lichens), the duration of grazing seasons on winter and summer pastures, the proportion in the diet of green fodder, shrubs, lichens, mushrooms, eggs of birds, and rodents, the macro- and microelement composition of soil and water, pollution, availability of salty seawater, and the cutting of velvet antlers [14,15]. A specific feature of the northern reindeer is its seasonal migration to areas with different forage resources: Summer pastures with a predominance of herbaceous plants and shrubs and winter pastures rich in lichens [16].

The study of the macro- and microelement composition of reindeer meat started in the second half of the 20th century. In the 1970s, in Canada, O. Schaefer (1977) and K. Hoppner (1978) confirmed the high nutritional value of reindeer meat due to high protein and low fat content [17,18]. Two decades later, H.V. Kuhnlein (1992; 1996; 2000; 2002) conducted a study of micronutrient composition of reindeer products [19–22] and developed recommendations for the use of venison by patients with atherosclerosis, vitamin deficiency, diabetes mellitus, and for the prevention of heart, liver, and stomach diseases [23–25]. In the 1990s, in Alaska, the USA, the chemical composition of traditional products, including venison, was studied [26]. Currently, a national database includes the data on the complete quantitative and qualitative chemical composition of reindeer meat in Alaska [27]. In Russia, studies conducted in Yamal-Nenets Autonomous Okrug [28,29], Nenets Autonomous Okrug [30–32], Taimyr [33–35], the Republic of Yakutia [36], and on the Kola Peninsula [37–39] confirmed the nutritional and biological value of reindeer meat. Furthermore, they proved the need to include this product in a healthy diet.

*Rangifer tarandus* is highly adapted to Arctic conditions. The optimal work of enzymes that ensure adaptation to cold stress provides the accumulation of essential trace elements necessary for the practical work of enzymatic chains. The most crucial macronutrients are calcium (Ca), magnesium (Mg), phosphorus (P), potassium (K), and sodium (Na), among others, which activate enzymes, regulate the number of hormones, promote muscle and nervous activity, and therefore are essential components of the daily human diet [40–42]. Thus, the consumption of reindeer meat can increase adaptation to the Arctic conditions, reduce the risk of heart diseases, and improve metabolism [43–45].

Improving knowledge about the macro- and microelement composition of reindeer meat in different northern regions will contribute to the expansion of the use of reindeer products to prevent diseases and increase the adaptation of the Arctic population and shift workers in the circumpolar area, as well as develop effective medicinal and pharmaceutical products. Furthermore, studying the chemical composition of reindeer meat will also increase the value of exported reindeer meat, which is an important factor in promoting the economic sovereignty and well-being of the Indigenous Peoples in the Arctic.

Our systematic review and meta-analysis aim to summarise the data on the chemical composition of reindeer meat depending on the region of the *Rangifer tarandus* and analyse the effects of venison consumption on human health and adaptation in the Arctic.

## **2. Materials and Methods**

In this research, a systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, the PRISMA statement [46,47], was conducted. The PRISMA checklist is presented in Appendix A according to the model [48].

The research questions for this systematic review were: "Does the macro- and microelement composition of reindeer meat vary in different northern regions?".

## *2.1. Search Strategy*

We searched the SCOPUS, PubMed, Embase, CrossRef, Medline, Cochrane library, eLibrary, and CyberLeninka electronic databases to identify relevant studies for the synthesis without language restrictions, using and updating them (from January 1980 to December 2020). In addition, the reference lists of all studies included and all the systematic reviews identified during the search process were checked.

The search strategy for all databases included terms of the Medical Subject Headings. Searches were made using the following keywords or their combination: "chemical composition of reindeer meat", "chemical composition of venison", combined with "sodium", "potassium", "calcium", "magnesium", "phosphorus", "iron", "zinc", "trace elements".

## *2.2. Inclusion Criteria*

Eligible studies were required to meet the following criteria: (1) Evaluate the concentration of the minerals (sodium, potassium, calcium, magnesium, phosphorus, zinc, iron) in reindeer meat; (2) the results were received in the territories located in the High North; (3) experimental descriptive or retrospective studies. We also excluded study protocols, letters to the editor, editorials, and conference abstracts with no full text available. All citations were entered into a bibliographic reference manager, and duplicate studies were excluded, automatically or manually (EndNote®, v. X7, Tomson Reuters, Philadelphia, PA, USA).

The control group included data on the macro- and microelement composition in reindeer meat obtained from our data. The content of trace elements in reindeer meat was assessed in the testing laboratory centre of the Federal Research Center for Nutrition and Biotechnology (Moscow) (certificate No. ROSS RU.0001.21IP14 dated 22 August 2014). In addition, sampling of the studied objects was carried out following the national standard GOST R 51447–99 [49]. The following standard methods were used to determine the chemical composition: (1) Identification of the content of trace elements (potassium, calcium, sodium, magnesium, phosphorus) according to R 4.1.1672-2003 [50]; (2) determination of iron and zinc under the national standard GOST No. 30178-96 [51].

Laboratory studies to identify trace elements in food were conducted in the autumn– winter season. To determine the concentration of metals, during the analysis food products were subjected to mineralisation to remove organic impurities. The determination was made using a model-Z 5300 atomic absorption spectrophotometer by atomic absorption spectrometry. The determination of the content of trace elements (calcium, magnesium, phosphorus) was implemented on a liquid chromatograph (HPLC) (model "Agilent 1100" detector DAD) in the laboratory of vitamins and minerals.

## *2.3. Study Selection, Data Extraction and Assessment of Methodological Quality and Risk of Bias*

According to the search strategy, the authors (SA, EB) screened titles and abstracts and independently assessed the full text of all potentially relevant studies for inclusion in this review. All disagreements were managed through discussion with a third author (AL). Then, following a standardised data collection form, the information was extracted from the included studies: (i) Study characteristics: Setting, study design, and countries; (ii) microelement composition of reindeer meat; and (iii) health impacts. We also evaluated the lists of references of the studied papers to identify other relevant articles to be included. Reasons for exclusion are reported in Figure 1.

**Figure 1.** PRISMA flow chart of study selection. **Figure 1.** PRISMA flow chart of study selection.

To assess the methodological quality and risk of bias, the checklist of Esther F. Myers [52–54] was applied (Appendix B). After a detailed evaluation of the methods and results, the studies were analysed to verify the possibility of "skewed results", "confusions", and "random occurrence". Only studies with a low risk of bias were included. To assess the methodological quality and risk of bias, the checklist of Esther F. Myers [52–54] was applied (Appendix B). After a detailed evaluation of the methods and results, the studies were analysed to verify the possibility of "skewed results", "confusions", and "random occurrence". Only studies with a low risk of bias were included.

### *2.4. Data Analysis and Synthesis 2.4. Data Analysis and Synthesis*

We applied Cochran's Q statistics and calculated I2 [55] to assess the statistical heterogeneity across studies. The interpretation of the value of I2 was: 0 to 40 = low; 30 to 60 = moderate and worthy of investigation; 50 to 90 = severe and worthy of understanding; 75 to 100 = aggregate with major caution [56], and a 95% confidence interval. A *p*-value < 0.05 was considered statistically significant. The interpretation threshold for the weighted effect values was 0.8 [57]. We generated the forest plots for each analysis. A comprehensive analysis of Egger's test and Funnel Plot Visual interpretation were implemented for the We applied Cochran's Q statistics and calculated I<sup>2</sup> [55] to assess the statistical heterogeneity across studies. The interpretation of the value of I<sup>2</sup> was: 0 to 40 = low; 30 to 60 = moderate and worthy of investigation; 50 to 90 = severe and worthy of understanding; 75 to 100 = aggregate with major caution [56], and a 95% confidence interval. A *p*-value < 0.05 was considered statistically significant. The interpretation threshold for the weighted effect values was 0.8 [57]. We generated the forest plots for each analysis. A comprehensive analysis of Egger's test and Funnel Plot Visual interpretation were implemented for the assessment of the publication bias [58–60]. The standardised difference in mean values (Hedge's g) and 95% confidence intervals were calculated using a random-effects

model [58–61]. The Jamovi statistical software (version 1.6, Sydney, Australia) [62] and the MAJOR module [63] were used to generate figures and run the test. Jamovi uses the Graphical User Interface (GUI) version of the R module, and MAJOR uses the R package, Metafor [64]. We used sensitivity analysis to explore the influence of each study in the pooled meta-analysis or publication bias results. This analysis was adopted in the case of substantial or considerable (50 to 100%) heterogeneity or significant publication bias (*p* < 0.05) [65,66].

## **3. Results**

## *3.1. General Characteristics*

A total of 3310 records published between January 1980 and December 2021 were screened. First, the abstracts of the publications were analysed. We excluded duplicated, descriptive (e.g., [67]) articles and publications that did not have information about the content of trace elements in reindeer meat or contained data about other animals (3012) (e.g., [68–81]). In total, 260 studies were excluded due to the unavailability of the full text of the publication (e.g., [82]). Therefore, 38 sources included in the further analysis were assessed by two independent reviewers.

Quantitative synthesis used 34 studies (Figure 1) published in English (*n* = 25) and in Russian (*n* = 9). In addition, fourteen studies were conducted in Russia [3,38,83–94], seven in Norway [74,95–100], six in the USA [27,101–105], four in Canada [19,21,22,106], and three in Finland [107–109]. The details of the included studies are presented in Table 1.


**Table 1.** The data of the included studies.

\* No data.

The retrieved studies involved a total of 328 *Rangifer tarandus*, which were adult animals of both sexes with an average age of 2.0 ± 0.5 years. The sample sizes ranged from 10 to 158. The mean value (mg/100 g) of macro- and microelements varied: Potassium from 225.0 ± 11.2 to 465.0 ± 10.2; sodium—from 49.7 ± 2.5 to 276.0 ± 11.0; phosphorus from 71.0 ± 5.0 to 266.7 ± 6.5; calcium—from 5.0 ± 0.3 to 158.0 ± 40.0; magnesium—from 16.1 ± 0.8 to 120.0 ± 10.0; iron—from 2.9 ± 0.15 to 18.2 ± 1.5; and zinc—from 2.1 ± 0.1 to 10.1 ± 0.8 (Table 1).

Separate meta-analyses were conducted for different macro- and microelements (magnesium, iron, zinc, calcium, potassium, sodium, and phosphorus).

## *3.2. Macro- and Microelement Composition in Reindeer Meat: Heterogeneity Analysis* 3.2.1. Magnesium

The iron content in reindeer meat was available in 11 studies. The standardised mean differences ranged from 2.9107 to 11.0987; most ratings were positive (100%). The estimated standardised mean difference based on a random-effects model was 5.3972 (95% CI: 3.7340– 7.0604). Thus, the mean value was significantly different from zero (z = 6.3602, *p* < 0.0001) (Table 2, Figure 2).

**Table 2.** The content of macro- and microelements in reindeer (*Rangifer tarandus*) meat: Heterogeneity analysis.


\* Note. Tau<sup>2</sup> Estimator: Hedges.

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**Figure 2.** Forest plot of the comparison of the content of magnesium in reindeer (*Rangifer tarandus*) meat by geographical regions. **Figure 2.** Forest plot of the comparison of the content of magnesium in reindeer (*Rangifer tarandus*) meat by geographical regions. **Figure 2.** Forest plot of the comparison of the content of magnesium in reindeer (*Rangifer tarandus*) meat by geographical regions.

The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of magnesium in reindeer (*Rangifer tarandus*) meat (Q(8) = 66.72, *p* < 0.0001, tau2 = 5.85, I2 = 92.17%). The 95% interval was from 0.37 to 10.42. Publication bias was explored with a visual inspection of the funnel plot (Figure 3), where the regression test showed asymmetry in the funnel plot (*p* = 0.026), but not the rank correlation test (*p* = 0.3429) (Table 3). The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of magnesium in reindeer (*Rangifer tarandus*) meat (Q(8) = 66.72, *p* < 0.0001, tau<sup>2</sup> = 5.85, I <sup>2</sup> = 92.17%). The 95% interval was from 0.37 to 10.42. Publication bias was explored with a visual inspection of the funnel plot (Figure 3), where the regression test showed asymmetry in the funnel plot (*p* = 0.026), but not the rank correlation test (*p* = 0.3429) (Table 3). The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of magnesium in reindeer (*Rangifer tarandus*) meat (Q(8) = 66.72, *p* < 0.0001, tau2 = 5.85, I2 = 92.17%). The 95% interval was from 0.37 to 10.42. Publication bias was explored with a visual inspection of the funnel plot (Figure 3), where the regression test showed asymmetry in the funnel plot (*p* = 0.026), but not the rank correlation test (*p* = 0.3429) (Table 3).

*tarandus*) meat by geographical regions. **Figure 3.** Funnel plot for publication bias evaluation of magnesium content in reindeer (*Rangifer tarandus*) meat by geographical regions. **Figure 3.** Funnel plot for publication bias evaluation of magnesium content in reindeer (*Rangifer tarandus*) meat by geographical regions.

**Figure 3.** Funnel plot for publication bias evaluation of magnesium content in reindeer (*Rangifer* 


**Table 3.** The statistical analysis of publication bias of the included sources with the data on macroand microelements content in reindeer (*Rangifer tarandus*) meat \*. *Sustainability* **2022**, *14*, x FOR PEER REVIEW 8 of 27

> \* Fault-tolerant calculation of N using Rosenthal's approach. \* Fault-tolerant calculation of N using Rosenthal's approach.

## 3.2.2. Iron 3.2.2. Iron

The iron content in reindeer meat was available in 11 studies. The standardised mean differences ranged from 0.32 to 11.56, and most ratings were positive (100%). The estimated standardised mean difference was 5.83 (95% CI: 3.25–8.4) based on a random-effects model. Thus, the mean value was significantly different from zero (z = 4.43, *p* < 0.0001) (Table 2, Figure 4). The iron content in reindeer meat was available in 11 studies. The standardised mean differences ranged from 0.32 to 11.56, and most ratings were positive (100%). The estimated standardised mean difference was 5.83 (95% CI: 3.25–8.4) based on a random-effects model. Thus, the mean value was significantly different from zero (z = 4.43, *p* < 0.0001) (Table 2, Figure 4).


**Figure 4.** Forest plot of the sources, including the data on the iron content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 4.** Forest plot of the sources, including the data on the iron content in reindeer (*Rangifer tarandus*) meat in different geographical regions.

The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of iron in reindeer (*Rangifer tarandus*) meat (Q(8) = 269.34, *p* < 0.0001, tau2 = 14.69, I2 = 97.04%). The 95% interval was from −2.11 to 13.77. Publication bias was explored with a visual inspection of the funnel plot (Figure 5), where the regression test showed asymmetry in the funnel plot (*p* = 0.0009), but not the rank correlation test (*p* = 0.12) (Table 3). The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of iron in reindeer (*Rangifer tarandus*) meat (Q(8) = 269.34, *p* < 0.0001, tau<sup>2</sup> = 14.69, I <sup>2</sup> = 97.04%). The 95% interval was from <sup>−</sup>2.11 to 13.77. Publication bias was explored with a visual inspection of the funnel plot (Figure 5), where the regression test showed asymmetry in the funnel plot (*p* = 0.0009), but not the rank correlation test (*p* = 0.12) (Table 3).

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**Figure 5.** Funnel plot of the sources, including the data on the content of iron in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 5.** Funnel plot of the sources, including the data on the content of iron in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 5.** Funnel plot of the sources, including the data on the content of iron in reindeer (*Rangifer tarandus*) meat in different geographical regions.

### 3.2.3. Zinс 3.2.3. Zinc 3.2.3. Zinс

Data on the content of zinс in reindeer meat were available in 11 studies. The standardised mean differences ranged from −0.05 to 1.52, with most ratings being positive (89%). The estimated standardised mean difference based on a random-effects model was 0.51 (95% CI: 0.22–0.80). Thus, the mean value was significantly different from zero (z = 3.45, *p* < 0.0006) (Table 2, Figure 6). Data on the content of zinc in reindeer meat were available in 11 studies. The standardised mean differences ranged from −0.05 to 1.52, with most ratings being positive (89%). The estimated standardised mean difference based on a random-effects model was 0.51 (95% CI: 0.22–0.80). Thus, the mean value was significantly different from zero (z = 3.45, *p* < 0.0006) (Table 2, Figure 6). Data on the content of zinс in reindeer meat were available in 11 studies. The standardised mean differences ranged from −0.05 to 1.52, with most ratings being positive (89%). The estimated standardised mean difference based on a random-effects model was 0.51 (95% CI: 0.22–0.80). Thus, the mean value was significantly different from zero (z = 3.45, *p* < 0.0006) (Table 2, Figure 6).


**Figure 6.** Forest plot of the sources, including the data on the content of zinс in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 6.** Forest plot of the sources, including the data on the content of zinс in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 6.** Forest plot of the sources, including the data on the content of zinc in reindeer (*Rangifer tarandus*) meat in different geographical regions.

The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of zinс in reindeer (*Rangifer tarandus*) meat (Q(8) = 429.42, *p* < 0.0001, tau2 = 0.194, I2 = 97.67%). The 95% interval was from −0.399 to 1.42. Publication bias was explored with a visual inspection of the funnel plot (Figure 7), where the rank correlation and regression The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of zinс in reindeer (*Rangifer tarandus*) meat (Q(8) = 429.42, *p* < 0.0001, tau2 = 0.194, I2 = 97.67%). The 95% interval was from −0.399 to 1.42. Publication bias was explored with a visual inspection of the funnel plot (Figure 7), where the rank correlation and regression tests were *p* = 0.45 and *p* = 0.92, respectively (Table 3). The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of zinc in reindeer (*Rangifer tarandus*) meat (Q(8) = 429.42, *p* < 0.0001, tau<sup>2</sup> = 0.194, I <sup>2</sup> = 97.67%). The 95% interval was from <sup>−</sup>0.399 to 1.42. Publication bias was explored with a visual inspection of the funnel plot (Figure 7), where the rank correlation and regression tests were *p* = 0.45 and *p* = 0.92, respectively (Table 3).

tests were *p* = 0.45 and *p* = 0.92, respectively (Table 3).

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**Figure 7.** Funnel plot of the sources, including the data on the content of zinс in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 7.** Funnel plot of the sources, including the data on the content of zinc in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 7.** Funnel plot of the sources, including the data on the content of zinс in reindeer (*Rangifer tarandus*) meat in different geographical regions.

### 3.2.4. Calcium 3.2.4. Calcium 3.2.4. Calcium

Data on calcium content in reindeer meat were available in 11 studies. The standardised mean differences ranged from −14.9 to 7.2, with most ratings being negative (56%). The estimated standardised mean difference was −2.1 (95% CI: −6.92–2.67) based on a random-effects model. Thus, the mean value was significantly different from zero (z = −0.87, *p* = 0.39) (Table 2, Figure 8). Data on calcium content in reindeer meat were available in 11 studies. The standardised mean differences ranged from −14.9 to 7.2, with most ratings being negative (56%). The estimated standardised mean difference was −2.1 (95% CI: −6.92–2.67) based on a random-effects model. Thus, the mean value was significantly different from zero (z = −0.87, *p* = 0.39) (Table 2, Figure 8). Data on calcium content in reindeer meat were available in 11 studies. The standardised mean differences ranged from −14.9 to 7.2, with most ratings being negative (56%). The estimated standardised mean difference was −2.1 (95% CI: −6.92–2.67) based on a random-effects model. Thus, the mean value was significantly different from zero (z = −0.87, *p* = 0.39) (Table 2, Figure 8).

**Figure 8.** The forest plot of the sources includes the data on the calcium content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 8.** The forest plot of the sources includes the data on the calcium content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 8.** The forest plot of the sources includes the data on the calcium content in reindeer (*Rangifer tarandus*) meat in different geographical regions.

The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of calcium in reindeer (*Rangifer tarandus*) meat (Q(8) = 488.35, *p* < 0.0001, tau2 = 53.18, I2 = 99.3%). The 95% interval was from −17.2 to 12.96. Publication bias was explored with a visual inspection of the funnel plot (Figure 9), where the rank correlation and regression The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of calcium in reindeer (*Rangifer tarandus*) meat (Q(8) = 488.35, *p* < 0.0001, tau2 = 53.18, I2 = 99.3%). The 95% interval was from −17.2 to 12.96. Publication bias was explored with a visual inspection of the funnel plot (Figure 9), where the rank correlation and regression The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of calcium in reindeer (*Rangifer tarandus*) meat (Q(8) = 488.35, *p* < 0.0001, tau<sup>2</sup> = 53.18, I <sup>2</sup> = 99.3%). The 95% interval was from <sup>−</sup>17.2 to 12.96. Publication bias was explored with a visual inspection of the funnel plot (Figure 9), where the rank correlation and regression test did not reveal any asymmetry in the funnel plot (*p* = 0.26 and *p* = 0.89, respectively) (Table 3).

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test did not reveal any asymmetry in the funnel plot (*p* = 0.26 and *p* = 0.89, respectively)

test did not reveal any asymmetry in the funnel plot (*p* = 0.26 and *p* = 0.89, respectively)

**Figure 9.** The funnel plot of the sources includes the data on the calcium content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 9.** The funnel plot of the sources includes the data on the calcium content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 9.** The funnel plot of the sources includes the data on the calcium content in reindeer (*Rangifer tarandus*) meat in different geographical regions.

### 3.2.5. Potassium 3.2.5. Potassium 3.2.5. Potassium

(Table 3).

(Table 3).

Data on potassium content in reindeer meat were available in 11 studies. The standardised mean differences ranged from −25.45 to 73.99, with most ratings being negative (70%). The estimated standardised mean difference was 24.3 (95% CI: −25.45–73.99) based on a random-effects model. Thus, the mean value was significantly different from zero (z = 0.96, *p* = 0.34) (Table 2, Figure 10). Data on potassium content in reindeer meat were available in 11 studies. The standardised mean differences ranged from −25.45 to 73.99, with most ratings being negative (70%). The estimated standardised mean difference was 24.3 (95% CI: −25.45–73.99) based on a random-effects model. Thus, the mean value was significantly different from zero (z = 0.96, *p* = 0.34) (Table 2, Figure 10). Data on potassium content in reindeer meat were available in 11 studies. The standardised mean differences ranged from −25.45 to 73.99, with most ratings being negative (70%). The estimated standardised mean difference was 24.3 (95% CI: −25.45–73.99) based on a random-effects model. Thus, the mean value was significantly different from zero (z = 0.96, *p* = 0.34) (Table 2, Figure 10).

**Figure 10.** Forest plot of the sources, including the data on potassium content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 10.** Forest plot of the sources, including the data on potassium content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 10.** Forest plot of the sources, including the data on potassium content in reindeer (*Rangifer tarandus*) meat in different geographical regions.

The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of potassium in reindeer (*Rangifer tarandus*) meat (Q(9) = 1970.58, *p* < 0.0001, tau2 = 6378.65, I2 = 99.44%). The 95% interval was from −164.4 to 161.95. Publication bias was The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of potassium in reindeer (*Rangifer tarandus*) meat (Q(9) = 1970.58, *p* < 0.0001, tau2 = 6378.65, I2 = 99.44%). The 95% interval was from −164.4 to 161.95. Publication bias was The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of potassium in reindeer (*Rangifer tarandus*) meat (Q(9) = 1970.58, *p* < 0.0001, tau<sup>2</sup> = 6378.65, I<sup>2</sup> = 99.44%). The 95% interval was from <sup>−</sup>164.4 to 161.95. Publication bias was explored with a visual inspection of the funnel plot (Figure 11), where the rank correlation and regression tests were *p* = 0.48 and *p* = 0.88, respectively (Table 3).

and regression tests were *p* = 0.48 and *p* = 0.88, respectively (Table 3).

and regression tests were *p* = 0.48 and *p* = 0.88, respectively (Table 3).

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explored with a visual inspection of the funnel plot (Figure 11), where the rank correlation

explored with a visual inspection of the funnel plot (Figure 11), where the rank correlation

**Figure 11.** Funnel plot of the sources, including the data on potassium content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 11.** Funnel plot of the sources, including the data on potassium content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 11.** Funnel plot of the sources, including the data on potassium content in reindeer (*Rangifer tarandus*) meat in different geographical regions.

### 3.2.6. Sodium 3.2.6. Sodium

Data on the content of sodium in reindeer meat were available in 11 studies. The standardised mean differences ranged from −27.7 to 198.6, with most ratings being negative (44%). The estimated standardised mean difference was 24.1 (95% CI: 22.31–70.5) based on a random-effects model. Thus, the mean value was significantly different from zero (z = 1.02, *p* = 0.31) (Table 2, Figure 12). Data on the content of sodium in reindeer meat were available in 11 studies. The standardised mean differences ranged from −27.7 to 198.6, with most ratings being negative (44%). The estimated standardised mean difference was 24.1 (95% CI: 22.31–70.5) based on a random-effects model. Thus, the mean value was significantly different from zero (z = 1.02, *p* = 0.31) (Table 2, Figure 12). 3.2.6. Sodium Data on the content of sodium in reindeer meat were available in 11 studies. The standardised mean differences ranged from −27.7 to 198.6, with most ratings being negative (44%). The estimated standardised mean difference was 24.1 (95% CI: 22.31–70.5) based on a random-effects model. Thus, the mean value was significantly different from zero (z = 1.02, *p* = 0.31) (Table 2, Figure 12).

**Figure 12.** Forest plot of the sources, including the data on sodium content in reindeer (*Rangifer*  **Figure 12.** Forest plot of the sources, including the data on sodium content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 12.** Forest plot of the sources, including the data on sodium content in reindeer (*Rangifer tarandus*) meat in different geographical regions.

The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of sodium in reindeer (*Rangifer tarandus*) meat (Q(8) = 8955.85, *p* < 0.0001, tau2 = The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of sodium in reindeer (*Rangifer tarandus*) meat (Q(8) = 8955.85, *p* < 0.0001, tau2 = The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of sodium in reindeer (*Rangifer tarandus*) meat (Q(8) = 8955.85, *p* < 0.0001, tau<sup>2</sup> = 5041.41, I <sup>2</sup> = 99.94%). The 95% interval was from <sup>−</sup>122.6 to 170.8. Publication bias was explored with a visual inspection of the funnel plot (Figure 13), where the rank correlation and regression tests were *p* = 0.14 and *p* = 0.46, respectively (Table 3).

*tarandus*) meat in different geographical regions.

5041.41, I2 = 99.94%). The 95% interval was from −122.6 to 170.8. Publication bias was explored with a visual inspection of the funnel plot (Figure 13), where the rank correlation

5041.41, I2 = 99.94%). The 95% interval was from −122.6 to 170.8. Publication bias was explored with a visual inspection of the funnel plot (Figure 13), where the rank correlation

and regression tests were *p* = 0.14 and *p* = 0.46, respectively (Table 3).

and regression tests were *p* = 0.14 and *p* = 0.46, respectively (Table 3).

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**Figure 13.** Funnel plot of the sources, including the data on sodium content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 13.** Funnel plot of the sources, including the data on sodium content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 13.** Funnel plot of the sources, including the data on sodium content in reindeer (*Rangifer tarandus*) meat in different geographical regions.

### 3.2.7. Phosphorus 3.2.7. Phosphorus 3.2.7. Phosphorus

The data on phosphorus content in reindeer meat was available in 11 studies. The standardised mean differences ranged from −27.7 to 198.6, with most ratings being positive (78%). The estimated standardised mean difference was 14.5 (95% CI: −22.7 to 51.7) based on a random-effects model. Thus, the mean value was significantly different from zero (z = 0.763, *p* = 0.45) (Table 2, Figure 14). The data on phosphorus content in reindeer meat was available in 11 studies. The standardised mean differences ranged from −27.7 to 198.6, with most ratings being positive (78%). The estimated standardised mean difference was 14.5 (95% CI: −22.7 to 51.7) based on a random-effects model. Thus, the mean value was significantly different from zero (z = 0.763, *p* = 0.45) (Table 2, Figure 14). The data on phosphorus content in reindeer meat was available in 11 studies. The standardised mean differences ranged from −27.7 to 198.6, with most ratings being positive (78%). The estimated standardised mean difference was 14.5 (95% CI: −22.7 to 51.7) based on a random-effects model. Thus, the mean value was significantly different from zero (z = 0.763, *p* = 0.45) (Table 2, Figure 14).

**Figure 14.** Forest plot of the sources, including the data on phosphorus content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 14.** Forest plot of the sources, including the data on phosphorus content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 14.** Forest plot of the sources, including the data on phosphorus content in reindeer (*Rangifer tarandus*) meat in different geographical regions.

The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of phosphorus in reindeer (*Rangifer tarandus*) meat (Q(8) = 2146.4, *p* < 0.0001, tau2 = The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of phosphorus in reindeer (*Rangifer tarandus*) meat (Q(8) = 2146.4, *p* < 0.0001, tau2 = The *Q*-test confirmed the heterogeneity of the sources, including the data on the content of phosphorus in reindeer (*Rangifer tarandus*) meat (Q(8) = 2146.4, *p* < 0.0001, tau<sup>2</sup> = 3227.16, I<sup>2</sup> = 99.54%). The 95% interval was from <sup>−</sup>102.9 to 131.9. Publication bias was explored with a visual inspection of the funnel plot (Figure 15), which did not present significant asymmetry: The rank correlation and regression tests were *p* = 0.34 and *p* = 0.19, respectively (Table 3).

3227.16, I2 = 99.54%). The 95% interval was from −102.9 to 131.9. Publication bias was explored with a visual inspection of the funnel plot (Figure 15), which did not present significant asymmetry: The rank correlation and regression tests were *p* = 0.34 and *p* = 0.19,

**Figure 15.** Funnel plot of the sources, including the data on phosphorus content in reindeer (*Rangifer tarandus*) meat in different geographical regions. **Figure 15.** Funnel plot of the sources, including the data on phosphorus content in reindeer (*Rangifer tarandus*) meat in different geographical regions.

### **4. Discussion 4. Discussion**

respectively (Table 3).

This meta-analysis has expanded the knowledge of the composition of reindeer meat in different Arctic regions. The main findings of our research showed that the highest concentration of macro- and microelements is present in reindeer meat of the following Arctic regions: Magnesium—in Taimyr, Yamal-Nenets Autonomous Okrug, Canada; iron—in Taimyr, Republic of Yakutia, Canada; zinc—in Taimyr, Komi Republic, Norway; calcium—in Yamal-Nenets Autonomous Okrug, Republic of Yakutia, Taimyr; potassium—in Canada, Taimyr, Yamal-Nenets Autonomous Okrug; sodium—in Taimyr, Republic of Yakutia; phosphorus—in Republic of Yakutia, Alaska, Finland. Different proportions of macro- and microelements in reindeer meat can be a pre-condition for discussing the possible correlation between the value (nutritious and biological) and price of reindeer meat in different Arctic regions. This meta-analysis has expanded the knowledge of the composition of reindeer meat in different Arctic regions. The main findings of our research showed that the highest concentration of macro- and microelements is present in reindeer meat of the following Arctic regions: Magnesium—in Taimyr, Yamal-Nenets Autonomous Okrug, Canada; iron—in Taimyr, Republic of Yakutia, Canada; zinc—in Taimyr, Komi Republic, Norway; calcium—in Yamal-Nenets Autonomous Okrug, Republic of Yakutia, Taimyr; potassium in Canada, Taimyr, Yamal-Nenets Autonomous Okrug; sodium—in Taimyr, Republic of Yakutia; phosphorus—in Republic of Yakutia, Alaska, Finland. Different proportions of macro- and microelements in reindeer meat can be a pre-condition for discussing the possible correlation between the value (nutritious and biological) and price of reindeer meat in different Arctic regions.

Different content of the macro- and microelements in the Arctic regions can be explained by ecosystems and anthropogenic (economic and industrial differentiation) factors. For example, a higher concentration of magnesium, calcium, potassium, and sodium in the Arctic regions with a harsh climate can be the outcome of a longer period of eating lichens and rags of vascular plants as a result of a long snow season. This is probably due to the higher concentration of trace elements in lichens and scrubs than green plants [110]. Higher concentrations of iron in Taimyr, the Republic of Yakutia, and Canada are probably associated with the regional features of iron accumulation in acidified soils and the high content of this trace element in the surface waters of these Arctic regions [111]. The high zinc content in Taimyr, Komi Republic, and Norway is possibly due to anthropogenic pollution caused by mining and processing polymetallic ores containing zinc [112]. However, the increased zinc content may also be of natural origin [113]. The phosphate content is probably related to the geochemical features of the soils [114]. In comparison, the soils of the Alaska and Finland regions contain more phosphates available for plants [115]. Different content of the macro- and microelements in the Arctic regions can be explained by ecosystems and anthropogenic (economic and industrial differentiation) factors. For example, a higher concentration of magnesium, calcium, potassium, and sodium in the Arctic regions with a harsh climate can be the outcome of a longer period of eating lichens and rags of vascular plants as a result of a long snow season. This is probably due to the higher concentration of trace elements in lichens and scrubs than green plants [110]. Higher concentrations of iron in Taimyr, the Republic of Yakutia, and Canada are probably associated with the regional features of iron accumulation in acidified soils and the high content of this trace element in the surface waters of these Arctic regions [111]. The high zinc content in Taimyr, Komi Republic, and Norway is possibly due to anthropogenic pollution caused by mining and processing polymetallic ores containing zinc [112]. However, the increased zinc content may also be of natural origin [113]. The phosphate content is probably related to the geochemical features of the soils [114]. In comparison, the soils of the Alaska and Finland regions contain more phosphates available for plants [115]. However, the primary source of the macro- and microelements in reindeer meat is their nutrition.

The supply of metals largely depends on their content in the surface layer of the soil [116]. Plants accumulate chemical compounds from the surface layer of the soil, which is typical for most of the territory of the Kola Peninsula, the Arkhangelsk Region, and the Nenets Autonomous Okrug and, to a lesser extent, with a decreasing trend in metal concentrations in the Yamal-Nenets Autonomous Okrug and the Republic of Yakutia [117]. Zinc belongs to the elements of strong biological accumulation [118,119], so the increase in the concentration of this element in soils is strongly associated with the processes of accumulation in plants (e.g., in Western Siberia [120,121]). Consequently, zinc entry with plant litter into the soil is very intensive.

Reindeers' diet consists of lichens, mosses, and vascular plants, accumulating significant amounts of metals and metalloids [79,122,123]. Therefore, the considerable variation of reindeers' habitat causes significant differences in the reindeer's diet. For example, the macro- and microelement composition of venison is influenced by the species composition of plants and lichens and the content of trace elements in them, the duration of grazing seasons on summer pastures, the proportion of green fodder, shrubs, lichens, mushrooms, eggs of birds, and rodents, the macro- and microelement composition of soil and water, the presence of pollution, the availability of salty seawater, cutting antlers, etc. The rich diet of *Reindeer tarandus* is also explained by specific seasonal migration to areas with different forage resources. Summer pastures are rich with herbaceous plants and shrubs. In contrast, winter pastures have many lichens.

The reindeer consumes 44 shrub willows and birches, 94 species of sedges, 52 species of cereals, 24 species of legumes, and 170 species of other plants [121]. Lichens are an essential and rich part of the reindeer's diet, especially in wet and frosty seasons (mainly in winter). So, on the territories located in the Arctic tundra zone (i.e., the northern part of the Yamal-Nenets Autonomous Okrug), as the significant part of the reindeer's ratio, lichens dominate most of the year [124–126]. In venison, it results in a high concentration of iron and zinc (important elements of antioxidant systems and cytochromes of the respiratory cell chain). The concentration of many trace elements in lichens is generally higher than in bryophytes, ferns, conifers, shrubs, and grasses [110]: Lichens accumulate more Co, Ni, Mo, Au, Mg, Ca, Zn, Cd, Sn, and Pb compared with other plants in the Arctic region [127]. Due to the lack of a root system and obtaining most minerals with precipitation (snow, rains), the concentration of trace elements in lichens highly depends on the transboundary transfer of trace elements and the amount of precipitation [128]. So, in more southern and western regions of Eurasia, less magnesium and calcium are accumulated in lichens than in the eastern and northern areas due to a large amount of precipitation during the snowless period [127]. The accumulation of trace elements by lichen also depends on its type and geographical location [129], i.e., woody lichens accumulate less zinc than bushy lichens (e.g., Cetraria, Cladonia) [130].

Moss, quickly accumulating metals, is the dominant form of vegetation in Arctic tundra ecosystems [122,131]. Sea aerosol is an additional source of elements including sodium, lead, mercury, and caesium [123,132]. Some of the elements are accumulated efficiently in mosses (e.g., Cd, Co, Cr, Cu, Fe, Mn, and Zn) [122], the Zn-Cd-Cu-Mn and Mo element correlation may be explained by their dietary intake from moss tundra. Compared to other Arctic regions and Canada, the values of most trace elements in the soils of the Yamal-Nenets Autonomous Okrug is higher (except Pb, Fe, and Mn) [133]. It can impact their transition to venison and increase the nutritious value of the reindeer meat in this Arctic region.

While mosses, lichens, and shrubs mostly accumulate cationogenic elements, herbaceous plants do it with anionic ones [111]. In the northern subarctic tundras, Zn, Nb, P, Mn, and Cu are actively accumulated [134]; in the middle and southern tundras, there are Zn, P, and Mn, and in the low northern subarctic tundras close to the coastal areas, the spectrum of elements is much more comprehensive than on the uplands of the continent [111].

Sedges and grasses and cereals (e.g., arctophile, bluegrass, arctagrostis, reed grass) dominate in the reindeer's diet (over 50% in early autumn; over 40% in early autumn) during the snowless period [135], and they actively accumulate Cu, Zn, and Pb [111]. In winter, especially with a lack of lichen forage, the rags of these plants can make up even more than 60% of the reindeer's diet.

The source of zinc, silver, lead, manganese, and barium for a reindeer is vaginal fluffy (Erióphorum vaginátum), a valuable nutritious food in winter and spring [136,137]. The accumulation of these trace elements depends not only on the composition of the substrate but also on the acidity of the soil [138]. Variegated and reed horsetails included in the reindeer's diet in early spring and autumn, as well as field horsetail, marsh horsetail, marsh horsetail, and meadow horsetail all year round [135], also contribute to enriching reindeer meat with manganese, silicon, and iron [111].

The high content of zinc and copper in reindeer meat can also result from consuming leaves of willows (gray willow, filiform willow, spear-shaped willow, ferruginous willow, Lapland willow, beautiful willow) and low and white birch. In early summer, the leaves of shrubs can provide up to 30% of the reindeer's diet (over 90% of them are willow leaves) [135]. Yernik and willow have the maximum accumulation of zinc [120].

Upon consuming blueberries, lingonberries, cloudberries, bearberries, crows, and rowan berries, a reindeer accumulates zinc, iron, and magnesium [134]. Likewise, mushrooms bring zinc, selenium, lead, copper, strontium, and mercury in a reindeer's diet [139]. While grazing, a reindeer can also eat birds' eggs, lemmings and voles, rodent nests, and frozen fish, covering the deficiency of such trace elements as calcium, potassium, phosphorus, sodium and zinc [140].

The knowledge of the macro- and microelement content of reindeer meat can help develop dietary programmes to manage the health risks of Arctic residents. The concentration of valuable trace elements necessary for adaptation in the Arctic is much higher in venison than other meat types. In north-eastern Canada, Kuhnlein H.V. et al. (1996) proved that consuming traditional food (venison) results in receiving more phosphorus, iron, zinc, and magnesium compared with imported products [20]. According to Bogdan E.G. and Turshuk E.G. (2016), S.V. Andronov, and A.A. Lobanov et al. (2017), venison is rich in macro- and microelements, has high nutritional and biological value [37,141].

Some researchers recommend widely using reindeer products to increase human resistance to unfavourable environmental factors in the diet [41,141–144] because reindeer meat is especially rich in calcium, phosphorus, potassium, sodium, magnesium, iron, and zinc. The high phosphorus, magnesium, potassium, and iron content in venison provides its high efficiency for increasing adaptation to cold stress and geomagnetic activity in the Arctic [145,146]. A diet enriched with reindeer products significantly increases the antiatherogenic fraction of blood lipids, prevents overweight, atherosclerosis, and heart disease [37,144], and improves microcirculation, tissue fluid exchange, and the body's antioxidant defence against free radicals [6]. A sufficiently large amount of trace elements (iron, zinc) contained in venison can help to prevent acute infectious diseases and provide antioxidant protection of the human body from free radicals [91,102]. This explains the high efficiency of adaptation to cold stress, as well as increased prophylactic activity during hypothermia [7,8].

The important contribution of reindeer meat and its macro-nutrients towards adaptation was acknowledged in Nordic countries. According to the Nordic nutrition recommendations, reindeer meat as game meat does not present the epidemiological evidence shown with high consumption of processed or red meat increasing the risk of colorectal cancer, type-2 diabetes, obesity, and coronary heart disease [147,148].

Our study had some limitations. First, the reindeer habitat in the Arctic is huge, therefore we had to present a less-detailed analysis for some regions. Second, a number of published studies included in the analysis are characterised by heterogeneity. In our meta-analysis, we used random effects models; so, a high level of heterogeneity (>80.0%) could impact the reliability. Third, there were a number of variations in the studies that were analysed: The quality, research methods, observation period, etc. Finally, selection bias is possible because observational studies were used in this meta-analysis.

The strengths of our study are associated with the implementation of a complex approach to systematising information on the mineral composition of reindeer meat in different Arctic regions. The meta-analysis has wide geographical coverage. A comprehensive

and robust search strategy was designed to avoid the loss of relevant research. Moreover, there were no studies excluded for linguistic reasons to avoid linguistic bias. In addition, routine tests and visual inspection of the funnel plot plots did not reveal any evidence of a risk of publication bias.

## **5. Conclusions**

The meta-analysis revealed that the indicators of the content of trace elements in reindeer meat had a high variability depending on the geographical region. The ecosystems and anthropogenic factors strongly impacted the macro- and microelements composition of reindeer meat in different Arctic regions. In the Russian Arctic regions with the most severe climatic conditions (especially, Taimyr, Yamal-Nenets Autonomous Okrug, and the Republic of Yakutia) and Canada, venison has the highest mineral saturation, and therefore, higher nutritious and biological value due to enriched biodiversity and the rich fodder base for reindeer. This makes reindeer meat an effective means of preventing obesity and adapting to cold due to the content of a complete set of essential trace elements and amino acids. The high content of iron and zinc in reindeer meat increases the body's antioxidant defence against free radicals and helps to prevent chronic non-infectious diseases. Ultimately, future research could compare the differences in the content of macro- and microelements in venison and other types of meat in the Arctic to prove its higher biological value.

A unique macro- and microelement composition of reindeer meat also proves its economic value and will be important for nutritional policy makers in the Arctic regions. This is a good pre-condition for the negotiation of fair prices for reindeer meat exported from this region based on the balance of the nutritious/biological value and price. It contributes to increasing the profitability of reindeer herding in the Arctic regions and maintaining this significant traditional livelihood of the Indigenous Peoples.

**Author Contributions:** Conceptualisation, A.L. and I.K.; methodology, A.L.; software, S.A.; validation, E.B. and A.Y.; formal analysis, S.A. and E.B.; investigation, A.L.; resources, S.A.; data curation, A.P.; writing—original draft preparation, E.B., S.A. and A.L.; writing—review and editing, A.Y.; revising, D.R. and O.S.; visualisation, S.A.; supervision, A.L.; project administration, E.B.; funding acquisition, E.B. and O.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by the Russian Science Foundation (grant № 22-28-01554) and INTERACT (grant № 871120).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** We thank the Indigenous communities of the Yamal-Nenets Autonomous Okrug and the Arctic Scientific Research Centre for their assistance. The study was also supported by the Tomsk State University Development Programme («Priority-2030») and partly carried out using the research equipment of the Unique Research Installation "System of experimental bases located along the latitudinal gradient" TSU with financial support from the Ministry of Education and Science of Russia (RF—2296.61321X0043, agreement No. 075-722 15-2021-672).

**Conflicts of Interest:** The authors declare no conflict of interest.

