2- Minimally processed food (industrialized or housewifely)
3- Industrialized food
Talens et al. ( 52 ) .
For being elaborated by health professionals, none of these classifications followed the definition of food processing as described by the Food Science, Technology and Engineering (FSTE) ( 53 ). Despite them being a processing level classification, most of these classifications were defined according to the food ingredients ( 52 ). Furthermore, only NOVA classification, whose description can be found in Moubarac et al. ( 54 ) and Monteiro et al. ( 55 ), was applied in an FBDG.
From the FSTE point of view, minimally processed food (MPF) is washed, sanitized, cut, or chopped foods, which are microbiologically safe and stable under the convenient packaging system, and are not thermally treated ( 56 ), while PF is a food product obtained by a sequence of unit operations ( 23 , 57 ), usually different from these related to MPF and with an important energy footprint ( 53 ). Overall, MPF is important because it is practical to use and can reduce cooking time and reduce FW at home, as these foods have been cleaned and cut, and their seeds and husks eliminated in the industry. And, PF is important mainly because of its long shelf-life, which means that the consumer can eat these foods several days or even months after-acquired. Nevertheless, overall, PF has also a negative appeal. However, the benefits of food processing must be recognized. For example, the benefits of food processing by thermal treatments include the inactivation of foodborne pathogens, natural toxins, or other detrimental constituents, prolongation of shelf life, improvement of digestibility and bioavailability of nutrients, improvement of palatability, taste, texture, and flavor, and enhancing functional properties, including augmented antioxidants and other defensive reactivity or increased antimicrobial effectiveness ( 58 ), besides contributing to decrease FLW. More definitions of PF foods and unit operations for food processing can be found in Jones ( 59 ), Floros et al. ( 23 ), and Aguilera ( 60 ).
Furthermore, recently, scientists from Sorbonne University (France) developed a nutritional system classification, called Nutri-Score (or 5C). Easy to understand, due to the adopted visual communication strategy, the Nutri-Score classification can be applied in food labeling ( 61 ). Nutri-Score classified food into five groups (A, B, C, D, and E) according to the food nutritional value, decreased from A to E ( 61 , 62 ). The Nutri-Score classification system is used by the French and Spanish Health Ministry ( 61 , 63 ). Botelho et al. ( 57 ) reinforced that, to identify the real source of nutrients, it is indispensable to examine food group classification. Furthermore, besides the FBDG use, food classification is also a strategic tool for epidemiological studies and health treatments.
Food-Based Dietary Guideline has been used as a tool to improve health and sustainability worldwide ( 13 , 43 , 59 , 64 – 67 ). As a strategy to achieve the UN's 17 goals, the SDGs also include clear and correct communication about nutrients and diet ( 16 , 43 ).
Proposing solutions to feeding issues is complex ( 23 , 68 ). It involves social, cultural, economic, and moral issues besides requiring a technical multidisciplinary knowledge. Generally, health professionals are involved in FBDG development ( 26 ). These professionals are experts in understanding how the ingredients and their nutrients are metabolized by the human body, representing something beneficial or not to health according to their frequency and quantity intake. This analysis is important to the success of FBDG; nonetheless, it is not enough to achieve its purpose.
Besides the nutritional point of view, these guidelines should also consider the fact that the target population, and the society as a whole, is made up of individuals who interact with each other. At most of the time, these same individuals respond to incentives and face trade-offs. Therefore, what is expected is that FBDG and/or policymakers have the knowledge of an optimal allocation of resources in the economy for consumers, producers, and the food system.
It should be noted that consumer demand for food is an important element in the formulation of several agricultural and food policies. Changes in food prices and income are the determinants of food demands. As Blundell ( 69 ) stated for some policy issues, the importance of empirical evidence on consumer behavior is indisputable. Price and income demand elasticities for food inform policymakers and researchers about how consumers make food purchasing decisions and help the design of effective nutrition policies.
It is a recurrent empirical finding, in several countries and at different historical moments that the participation of food expenditures in the family budget decreases as their income rises. In fact, this is one of the most established empirical findings and regularities in economics and is known as “Engel's law,” due to the studies by Engel ( 70 ). The reference for the validity of “Engel's law” is Houthakker ( 71 ), but Chai and Moneta ( 72 ) can be also consulted for a useful retrospective on Engel's work. Moreover, Chattopadhyay et al. ( 73 ) used Engel's law to develop a mathematical model that can be applied as a tool for economic policy formulation. In addition, Lancaster ( 74 ) proposed an alternative view on the consumer theory, that the goods are, in fact, a collection of characteristics. Sen ( 75 ), in turn, includes the functionality attributed by the person to the goods on the Lancaster consumer theory: In Sen's terminology, a “functioning” is what an individual chooses to do or to be, in contrast to a commodity, which is an instrument enabling her to achieve different functioning. Sen ( 75 ) states that it is not merely the achieved functioning that matters but the freedom that a person has in choosing from the set of feasible functioning, which is referred to as the person's “capability.” This has become the so-called capability approach. This approach has been immensely useful in the context of studying poverty, gender issues, political freedom, and the standard of living ( 76 ).
Engel's law has two broader implications for the structure of consumption expenditure ( 77 ). First, there is a tendency to food specialization of the poorer's budgets in the sense that they are less diversified than those of more affluent consumers. Within the food budget, cheaper, more starchy foods (such as rice, potatoes, and bread) are likely to be predominant for the poor, leading to less nutritious, less diversified diets ( 77 ). The second implication of Engel's law is related to the quality of consumption. The declining food share that accompanies income growth means that the quality of consumption rises. Moreover, as food is the good consumed intensively by the poor, there is a natural link between Engel's law and the measurement of quality. Based on a study for more than 150 countries, Clements and Si ( 77 ) found out some interesting relations between Engel's law, the variety of foods in the diet, and their quality. While the food share falls with higher incomes, there is a tendency for spending to be more evenly over foodstuffs reflecting a more diverse diet.
Diet, economic-social matters, and lifestyle are linked. Therefore, to achieve the purpose of FBDG, a multidisciplinary technical body is needed, and it includes social, economic, and human food chain (HFC) professionals. Health professional knowledge is part of the HFC. However, the HFC embraces soil handling, food production system, the complex and extensive food processing, filling and packaging, storage conditions at the sale point, and consumption. Thus, in addition to health professionals, the HFC must also be studied by Agronomists, Food Engineers, etc. Efficient actions toward better health standards applied in public policies demand interdisciplinary strategies, with public–private and academic support ( 5 , 14 , 23 , 68 ).
Some countries used different strategies in their FBDG, in some cases, including the target audience, such as the general population, breastfeeding, and children to the age 2, eldering, and indigenous ( 13 , 65 , 66 , 78 , 79 ). Generally, FBDGs encourage the consumption of water and a diversity of food in different proportions, always associated with regular physical activities ( 13 , 65 , 66 ). Ingredients such as sugar, fats, and salt are shown as items to be avoided or limited ( 13 , 26 , 65 , 66 ). Among all the FBDG presented in Table 1 , only those from Brazil and Canada do not recommend the regular practice of physical activities. In June 2021, the Brazilian Health Ministry released the “Physical Activity Guide for Brazilian Population” in a complement to FBDG. All FBDG can be found on the FAO website ( 26 ).
French, Chilean, and South African FBDG recommend the consumption of food rich in starch daily as a food base. According to Herforth et al. ( 65 ), more than half of the 90 FBDG analyzed in her review also encourage it. The UK FBDG, in turn, recommended several sources of carbohydrates as a food base, including bread and pasta. The South African FBDG focusing on regional foods was elaborated.
The Spanish FBDG—the healthiest country in the world, according to Bloomberg Global Health Index 2020 ( 80 )— opted for a visual communication strategy, in which combinations of physical activities and food choices were suggested, specifying quantities and frequencies. To Portuguese, Argentinian, and Chinese FBDG, healthy feeding should be complete, balanced, varied, and followed by physical activity. Italy—the second healthiest country in the world according to Bloomberg Global Health Index 2020 ( 80 )— developed a technical FBDG explaining some “true or false” food issues, clearly and straightforwardly. Chile's FBDG, in turn, recommends reducing the TV time and increasing the fast walking. USA's FBDG explains that the energy intake should be appropriated by the personal needs. In addition, the North American FBDG highlights that the food choice must respect the individual preferences and cultural habits.
Italian, French, Argentinian, Australian, Chinese, and Indian FBDG, besides the reduction of salt, sugar, and fat intake, also recommend to limit the consumption of alcohol. Brazilian, Canadian, Indian, Uruguayan, Ecuadorians, and Australian FBDG extend this recommendation to PF (Brazilian, Uruguayan, and Ecuadorian FBDG— “ultra-processed” food and, Canadian and Australian FBDG— “highly processed” food), whereas British and Indian FBDG extend to tabaco. Indian FBDG recommends limiting PF consumption, however, the distinction between industrialized food (PF) and fast food (restaurants franchise) is not clear. Ecuadorian FBDG, in turn, encouraged the reduction of PF, fast food, and sweetened beverages, as the Brazilian one. Among all FBDG presented in Table 1 , Japanese FBDG was the only one to orient their population to reduce FW. This is remarkable!
The majority of the countries adopted the visual communication strategy, except Brazil and Italy ( 13 , 65 , 66 , 79 ). Up to date, Italian visual communication has not been presented ( 26 ). Canada 1 , USA, 2 and France 3 also use a website to communicate with the population. According to Hess et al. ( 67 ), visual communication strategies do not change the FBDG efficiency and efficacy if the information is easy to understand and to follow.
To be effective, besides culturally accepted, the message must be clear, concise, practical, accessible, and easy to be remembered ( 59 , 65 , 78 , 79 ). The United Arab Emirates FBDG used a tourist/architectural-cultural landmark of the country as a visual communication strategy, the “Burj Khalifa”. This structure represents the feeding. The base of the structure is water. Each color represents a food group (cereals, vegetables, dairy, fruit, meat, and fat), and its proportion represents the quantity/frequency of the consumption ( 26 ). The Japanese FBDG applies a similar strategy (a popular toy).
There is a lack of data on the literature about the FBDG effectiveness. In the USA, according to Floros et al. ( 23 ), the FBDG implementation prompted companies to change the product's formulation and to create foods that are more nutritious. Baked products and cereals now have higher fiber content and use whole grains. Convenience-store food made of fruits, vegetables, and whole grains became available at the markets. The baby carrot, not existent as of then, was widely accepted by the target audience. After reformulations, the trans-fat content was reduced in many products ( 23 ).
All the FBDG shown in Table 1 classified food according to their nutritional composition, with an exception of the Brazilian FBDG ( 52 ). The Uruguayan FBDG classified food according to their nutritional composition and, inside each group indicated the processing level as well. In his strategy, the Brazilian FBDG classified food by their processing level based on the NOVA classification ( 50 ). Nonetheless, the main criterion on the NOVA classification is not necessarily linked to process as the action of processing food—using a sequence of unit operations—but according to the ingredients used in the formulation of the food, in other words, the product or chemical component added pre-, during, or post-processing.
In the NOVA classification, the term “ultra-processed” food stands out, which was associated with products with a low nutritional value ( 8 , 16 , 52 , 62 , 81 ). According to Monteiro et al. ( 55 ), “ultra-processed” food would be an industrial formulation with an additive not used in domestic cooking ( 8 , 81 ). Nevertheless, many Chefs are also using ingredients that are rarely used at home ( 40 ), but they are not considered as being “ultra-processed” food producers. Indeed, it is not so easy to define UPF because it can be so heterogeneous in nutritional composition, as demonstrated by Lorenzoni et al. ( 82 ), thus representing a heterogeneous group of foods with different characteristics.
Furthermore, according to Gibney ( 83 ), there was no official definition of the “ultra-processed” term, and the way that the author used it has changed over the years ( 16 , 32 , 52 ). Canada and Australia's FBDG do not use this strategy and also recommend avoiding “high processed” products, which are defined by them as products with high salt, sugar, and fat content; different from the “ultra-processed” definition.
According to the Scientific Committee of the Spanish Agency for Food Safety and Nutrition ( 52 ), there is no relation between health and the type or intensity of processing level ( 57 , 84 ). Nutritional quality and (ultra)processing are distinct concepts that can affect health in different ways by their own mechanisms ( 62 ). Nutritional value is related to the food formulation or composition ( 57 ), regardless of whether it is made at home, restaurant, or industry. Petrus et al. ( 53 ), Carretero et al. ( 16 ), and Knorr and Watzke ( 81 ) related that the argumentative basis of NOVA classification is ingredients and not process parameters. Adding ingredients is part of the formulation ( 57 ) and it is not related to process parameters. Process parameters arguments must involve temperature, pressure, time, amount or flow rate (for noncontinuous or continuous processes), and others, not ingredients. At home and in restaurants as well, homeworkers and Chefs also freeze, refrigerate, cook, ground, mold, dry, fry, and apply other unit operations ( 60 ). This made the Brazilian classification (NOVA) not comprehensible, accessible, practical, or viable ( 16 , 52 , 59 , 84 ).
Knorr and Watzke ( 81 ), Derbyshire ( 85 ), and Petrus et al. ( 53 ) considered the term “ultra-processed” more misleading than explanatory. Sadler et al. ( 84 ), Carretero et al. ( 16 ), Jones ( 59 ), and Talens et al. ( 52 ) reported that diets lacking “ultra-processed” food could also exceed the recommended amount of calories. In Brazil, salt and sugar intake is higher in food made at home than in industrialized ones ( 53 ). Ares et al. ( 8 ) described that the term “ultra-processed” is not widely understood. Galan et al. ( 62 ) showed that 21% of the ultra-processed food classified by NOVA has good nutritional quality. In addition, Petrus et al. ( 53 ) remind that the NOVA classification encourages raw or unprocessed food consumption, which cannot be safe and can increase foodborne diseases. Therefore, the NOVA classification and the “ultra-processed” term do not necessarily contribute to achieve healthily the SDGs. Consequently, the Brazilian FBDG should be revised in terms of its food classification system adopted.
Obesity is not an individual responsibility factor due to mistaken motivational choices ( 23 , 86 ). Obesity and malnutrition can also be related to sustainability issues ( 7 , 12 , 86 ). The global warming consequences in food production will affect the underdeveloped countries more intensely, especially their economically disadvantaged part. According to Kleinert and Horton ( 86 ), solving malnutrition and obesity is necessary to implement sustainable business models with a focus on health promotion. It is not only enough to produce quality food but also self-sustainable and accessible food to the population.
The COVID-19 pandemic has shown that the current accessibility and production food system have not been efficient in protecting the population against hunger and obesity. In a social, economic, and health crisis scenario, we saw—at the same time—increasing hunger, obesity, and FLW ( 87 ). Not surprisingly, the 2020 Nobel Peace Prize was awarded to the UN's World Food Program. According to Berit Reiss-Andersen, chair of the Norwegian Nobel Committee, food access cannot become a weapon of war and conflict ( 88 ).
The current global food system not only fails in fulfilling the basic nutritional needs but also intensifies the pressure on the planet's sourcing boundaries ( 15 , 44 ). According to Earth Overshoot Day ( 89 ), a metric used to identify the point (in days) when humanity's demand for ecological resources exceeds what the Earth can regenerate at the same year, the Overshoot Day 2020 happened on August 22, 2021 and on July 29, 2021. The carbon footprint increased 6.6% from 2020. In other words, almost half of the planet resource consumption in 2021 will not be recovered in the same year.
According to Springmann et al. ( 45 ), the FBDG can also play a strong role in sustainability issues, which has not been adequately explored. FBDG, by guiding what and how much to eat, indirectly influences the amount of CO 2 generated during the food supply chain ( 15 , 44 – 46 ). Nevertheless, Ritchie et al. ( 44 ) report a lack of clarity in the recommendations.
In the future decades, the increasing population, urbanization, and globalization will pressurize the world, whereas natural sources will be increasingly scarce ( 14 ). Besides sustainability issues, it is worth noting one aspect regarding eating habits. In most western countries and based on women's increasing participation in the workforce, the food away from home (FAFH) is an increasing trend component of total food consumption and nutritional intake of adults and children. Empirical evidence shows that FAFH has been associated with poor diet quality ( 90 , 91 ). Hence, policies designed to influence nutritional and healthy outcomes would be incomplete if they did not address the role of FAFH ( 92 ).
In relation to home production (unpaid domestic and care work), it is interesting to note that, mostly women, home cooking declined in the late century and in the early years of the 21st century ( 93 – 96 ). Historically, food preparation and household cooking have been assigned to women, and food at home (FAH) has been linked to female gender roles and identity.
Women have also had an important performance in the traditional food knowledge (TFK). The TFK refers to a cultural tradition of sharing food, recipes and cooking skills, and techniques and passing down that collective wisdom through generations ( 97 ). According to Kwik ( 97 ), the value of this knowledge is hidden in a global food system offering an abundance of commercial convenience foods, which is a consequence of urbanization and is intensified by a dynamic lifestyle ( 98 ). In addition, in their study with children in the Netherlands, Folkvord et al. ( 99 ) explain that food exposition as the cooking programs on TV can influence eating behaviors. On the other side, according to Contreras and Ribas ( 100 ), the “omnivore's deculturalization” is not related to food industrialization but rather to food medicalization.
Although men have increased their contribution to home cooking ( 94 ), gender division of labor remains unequal, with women doing most of the household chores. In most societies, women keep carrying the responsibility for labor of food provision—the most basic labor of care. Another interesting topic to point out is related to the elderly or the ones who retire. Based on what was noted by Becker ( 101 ), that consumption is the output of a “home production” function that uses both expenditure and time as inputs. Aguiar and Hurst ( 102 ) were the first ones to address the topic of meal preparation. They recognize the inputs of food production include not only just food (modeled by food expenditures) but also the time spent shopping and preparing meals. They also showed that despite a sharp decline in food expenditures, neither the quantity nor the quality of food intake deteriorates with the retirement status. Also, what they find is that these declining expenditures are offset by increased time spent shopping and preparing meals, suggesting that time and money are substitutes in food production. Nevertheless, these practices are not necessarily defined only by prices/expenditure (some monetary measure) and time. Then, better FBDG outcomes would require other considerations, which are multiple (nutritional, environmental, social, and also economic among others) and varied.
Thereby, while the food production system does not pay attention to environmental, nutritional, social, and economic issues, no other measure will be efficient ( 15 ). The food production system begins in the cultivation technique, passing through processing food, filling, distribution to the market, and storage to provide effects on the human body. Despite an unquestionable technological development, while ensuring the scale production of microbiologically safe, nutritious, and appealing foods, the industry must also engage consumers and its stakeholders as well ( 103 ).
The food production chain will be sustainable to the planet and to the individuals only when the public–private partnership and academia are strongly established (Agronomic Engineering, Food Engineering, Health, and Public Policies) starting with a clear and an educational FBDG elaboration. In addition, the food industry must increase its transparency. A critical review of the abovementioned issues is essential for achieving the SDGs.
Malnutrition and obesity are the consequences of imbalance and inequality diet. Currently, with a certain abundance of food as a consequence of the food production and the food industry, the accessibility of food quality and balanced food consumption emerged as a new concern, both intensified by the absence of the population knowledge. In the contemporary world, malnutrition exists because of the inefficiency of public policies, social inequality, low purchasing power, and poor industrial-governmental agreements. Obesity is a preventable biopsychosocial and environmental pandemic, resulting from unhealthy lifestyles in a technological, sedentary, and urbane system. In this contradiction, the under-management resources are evidenced by the nonsustainable food supply chain practices, with high levels of FLW, which result in overload of the planet and rising food insecurity. To aggravate this situation, the daily population habits are not sustainable, most of the time, made unconsciously, and in 2020, the world was affected by the COVID-19 pandemic that challenged the social, structural, and ecological world system.
Because of this scenario already existing and serious even before the COVID-19 pandemic, governments worldwide developed FBDGs with a goal to orient and educate the population in their food choices and, in consequence, in sustainability issues as well. The FBDG should inform the population about the current problems and orient their decision-making to mitigate them. However, this important public policy tool can and should be better used from a healthy, nutritional, social, and environmental point of view. Some FBDG, especially the Brazilian and the Uruguayan ones, choose an incorrect and misunderstood food system classification (NOVA classification) in terms of the process definition. FBDG must be clear, correct, and practical, otherwise, it will confuse the population and therefore lose its purpose and distort the economy.
With the COVID-19 pandemic, hunger, malnutrition, obesity, and FLW have been intensified. Evidence shows that we are not on the path to achieve the goals of SDGs. Moreover, we are facing a dramatic transformation in our access to and the availability of food—along with where we eat and with who. Therefore, a radical change in the feeding system is urgent and necessary.
AA contributed to conceptualization, research, writing—original draft, editing, and reviewing. AB contributed to research and writing and reviewing. PS contributed to writing and reviewing, supervision, and project administration. All authors contributed to the article and approved the submitted version.
The São Paulo State Research Foundation (FAPESP), for the grant (2013/07914-8) and the Brazilian National Council for Scientific and Technological Development (CNPq), for the grant of ALH (44.3196/2019-2), and research fellowship of PJAS (30.0799/2013-6). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES): Finance Code 001 (MS fellowship of AA).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The authors thank to Prof. Dra Silvia M. Franciscato Cozzolin (FCF-USP) for her critical review.
1 https://food-guide.canada.ca/en/
2 https://www.myplate.gov/
3 https://www.mangerbouger.fr/
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Towards the sustainable development goal of zero hunger: what role do institutions play.
2. literature review, 2.1. institutions, 2.2. relationship between institutions and the “zero hunger” goal, 2.3. analytical framework, 3. methodology, 3.2. model specification and methods of estimation, 3.2.1. principal component analysis, 3.2.2. simultaneous equation modeling, instrumental variables (iv) and the two-stage least squares (2sls) method, three-stage least squares (3sls) method, 3.2.3. pooled ordinary least squares, fixed effects, and random effects models, 4. results and discussion, 4.1. direct impact of institutions on sdg2 performance according to worldwide governance indicators, 4.2. direct impact of institutions on sdg2 performance according to political risk rating, 4.3. contrasting the direct impact of institutions on sdg2 in developing and developed countries, 4.4. indirect impact of institutions on sdg2 performance, 5. robustness check, 6. conclusions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.
Variables | CC | GE | PS | RQ | RL | VA |
---|---|---|---|---|---|---|
CC | 1.0000 | |||||
GE | 0.9471 * | 1.0000 | ||||
PS | 0.7613 * | 0.7350 * | 1.0000 | |||
RQ | 0.9149 * | 0.9468 * | 0.7357 * | 1.0000 | ||
RL | 0.9614 * | 0.9598 * | 0.7787 * | 0.9428 * | 1.0000 | |
VA | 0.8416 * | 0.8318 * | 0.7070 * | 0.8599 * | 0.8538 * | 1.0000 |
Component | Eigenvalue | Difference | Proportion | Cumulative | ||
---|---|---|---|---|---|---|
Comp1 | 5.27448 | 4.91838 | 0.8791 | 0.8791 | ||
Comp2 | 0.3561 | 0.142692 | 0.0594 | 0.9384 | ||
Comp3 | 0.213408 | 0.129079 | 0.0356 | 0.9740 | ||
Comp4 | 0.0843286 | 0.0449294 | 0.0141 | 0.9881 | ||
Comp5 | 0.0393992 | 0.00711496 | 0.0066 | 0.9946 | ||
Comp6 | 0.0322842 | 0.0054 | 1.0000 | |||
Variable | Comp1 | Comp2 | Comp3 | Comp4 | Comp5 | Comp6 |
CC | 0.4213 | −0.1263 | −0.2366 | −0.6339 | −0.3619 | 0.4666 |
GE | 0.4212 | −0.2245 | −0.3005 | 0.0958 | 0.8043 | 0.1606 |
PS | 0.3626 | 0.9265 | −0.0198 | 0.0744 | 0.0429 | 0.0478 |
RQ | 0.4194 | −0.2150 | −0.0735 | 0.7394 | −0.4339 | 0.1938 |
RL | 0.4267 | −0.1085 | −0.2097 | −0.1572 | −0.1444 | −0.8465 |
VA | 0.3945 | −0.1311 | 0.8967 | −0.1097 | 0.1057 | −0.0041 |
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | |
---|---|---|---|---|---|---|---|
Variables | CGI | CC | GE | PS | RQ | RL | VA |
CGI | 0.0246 *** | ||||||
(8.718) | |||||||
CC | 0.0564 *** | ||||||
(8.245) | |||||||
GE | 0.0531 *** | ||||||
(9.244) | |||||||
PS | 0.0535 *** | ||||||
(6.106) | |||||||
RQ | 0.0623 *** | ||||||
(8.482) | |||||||
RL | 0.0488 *** | ||||||
(9.107) | |||||||
VA | 0.0634 *** | ||||||
(6.303) | |||||||
POPG | −0.0012 | −0.0052 ** | −0.0026 | −0.0038 | −0.0009 | −0.0055 *** | 0.0024 |
(−0.561) | (−2.522) | (−1.277) | (−1.465) | (−0.406) | (−2.981) | (0.741) | |
TO | −0.0000 | 0.0001 * | 0.0001 | −0.0002 ** | −0.0000 | 0.0001 | 0.0001 |
(−0.124) | (1.856) | (1.087) | (−2.003) | (−0.399) | (0.944) | (1.480) | |
EDU | 0.0002 *** | 0.0002 *** | 0.0001 *** | 0.0002 *** | 0.0002 *** | 0.0002 *** | 0.0002 *** |
(3.494) | (3.196) | (2.869) | (3.698) | (3.962) | (4.180) | (4.211) | |
URBN | 0.0004 ** | 0.0001 | 0.0004 ** | 0.0010 *** | 0.0004 ** | 0.0006 *** | 0.0007 *** |
(2.050) | (0.275) | (2.551) | (6.346) | (2.181) | (3.434) | (3.509) | |
Constant | 0.5496 *** | 0.5619 *** | 0.5379 *** | 0.5362 *** | 0.5341 *** | 0.5372 *** | 0.5049 *** |
(44.159) | (39.474) | (49.594) | (36.293) | (46.475) | (49.019) | (46.075) | |
Observations | 860 | 860 | 860 | 873 | 860 | 860 | 873 |
Anderson canon. | 123.385 | 99.351 | 154.349 | 79.578 | 119.602 | 152.770 | 71.687 |
Corr. LM statistic | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) |
Cragg–Donald | 71.440 | 55.706 | 93.290 | 43.429 | 68.896 | 92.129 | 25.795 |
Wald F–statistic | |||||||
Sargan statistic | 0.308 | 1.854 | 0.149 | 1.607 | 0.642 | 0.016 | 3.951 |
(0.579) | (0.173) | (0.700) | (0.205) | (0.423) | (0.899) | (0.139) | |
Endogeneity test | 33.476 | 37.716 | 24.527 | 29.675 | 37.164 | 27.249 | 24.299 |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
Instruments | Latitude | Latitude | Latitude | EF | Latitude | Latitude | EF |
EF | EF | EF | GEL | EF | EF | UKL | |
GEL |
Model 8 | Model 9 | Model 10 | Model 11 | Model 12 | Model 13 | Model 14 | Model 15 | Model 16 | Model 17 | Model 18 | Model 19 | Model 20 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Variables | |||||||||||||
PRR | 0.0043 *** | ||||||||||||
(9.0651) | |||||||||||||
GVSTAB | 0.0340 *** | ||||||||||||
(3.3556) | |||||||||||||
SOECON | 0.0199 *** | ||||||||||||
(9.8632) | |||||||||||||
INVPROF | 0.0279 *** | ||||||||||||
(6.9518) | |||||||||||||
INTCON | 0.0520 *** | ||||||||||||
(5.8915) | |||||||||||||
EXTCON | 0.0604 *** | ||||||||||||
(5.9090) | |||||||||||||
CORRUP | 0.0208 *** | ||||||||||||
(8.1846) | |||||||||||||
MILPOL | 0.0253 *** | ||||||||||||
(5.2674) | |||||||||||||
RELTEN | 0.0178 *** | ||||||||||||
(3.2813) | |||||||||||||
LAWORD | 0.0012 | ||||||||||||
(0.6759) | |||||||||||||
ETHTEN | 0.0137 *** | ||||||||||||
(4.4700) | |||||||||||||
DEMACC | 0.0295 *** | ||||||||||||
(5.9796) | |||||||||||||
BUREAU | 0.0161 *** | ||||||||||||
(7.0848) | |||||||||||||
POPG | −0.0036 * | −0.0318 *** | −0.0085 *** | −0.0066 *** | 0.0029 | −0.0124 *** | −0.0098 *** | 0.0071 | −0.0054 | −0.0158 *** | −0.0109 *** | 0.0080 * | −0.0031 |
(−1.8193) | (−7.2095) | (−5.5069) | (−2.9023) | (0.7689) | (−5.6276) | (−5.5953) | (1.4936) | (−1.5271) | (−10.3885) | (−5.9343) | (1.8012) | (−1.3337) | |
EDU | 0.0001 ** | 0.0004 *** | 0.0001 * | 0.0002 *** | 0.0001 * | 0.0001 * | 0.0001 ** | 0.0001 | 0.0002 *** | 0.0002 *** | 0.0002 *** | 0.0002 *** | 0.0001 * |
(2.5510) | (3.0057) | (1.9266) | (3.0288) | (1.9126) | (1.6795) | (2.3980) | (0.6698) | (3.3189) | (5.6338) | (3.8390) | (3.0370) | (1.9188) | |
TO | −0.0001 | 0.0005 *** | 0.0001 ** | −0.0001 | −0.0007 *** | −0.0003 *** | 0.0003 *** | −0.0003 ** | 0.0002 *** | 0.0002 *** | 0.0003 *** | 0.0001 | 0.0002 *** |
(−0.8351) | (4.0116) | (2.3921) | (−0.6240) | (−3.6300) | (−3.2409) | (4.3214) | (−2.4471) | (2.6012) | (4.6234) | (5.6564) | (0.9801) | (3.2865) | |
URBN | 0.0006 *** | 0.0010 *** | 0.0005 *** | 0.0007 *** | 0.0011 *** | 0.0019 *** | 0.0004 * | 0.0006 ** | 0.0010 *** | 0.0017 *** | 0.0014 *** | 0.0003 | 0.0006 *** |
(3.3730) | (5.2787) | (3.3502) | (3.2985) | (5.6526) | (13.8210) | (1.8866) | (2.5285) | (3.6544) | (13.3972) | (9.7789) | (1.0580) | (3.3182) | |
Constant | 0.2632 *** | 0.2325 *** | 0.4368 *** | 0.3012 *** | 0.0758 | −0.0991 | 0.4353 *** | 0.3587 *** | 0.3462 *** | 0.4696 *** | 0.3769 *** | 0.2758 *** | 0.4208 *** |
(10.8782) | (2.6637) | (53.9982) | (11.4185) | (1.1137) | (−1.0158) | (44.9678) | (15.2893) | (9.0522) | (49.2509) | (17.2945) | (7.9908) | (40.4199) | |
Observations | 923 | 444 | 923 | 923 | 923 | 1395 | 923 | 937 | 937 | 1395 | 937 | 923 | 937 |
Anderson canon. Corr. LM statistic | 138.99 (0.000) | 18.33 (0.000) | 167.44 (0.000) | 70.73 (0.000) | 45.86 (0.000) | 55.69 (0.000) | 112.05 (0.000) | 34.08 (0.000) | 28.70 (0.000) | 180.85 (0.000) | 58.02 (0.000) | 45.49 (0.000) | 96.04 (0.000) |
Cragg –Donald Wald F- statistic | 81.20 | 6.20 | 101.50 | 25.31 | 23.95 | 28.86 | 42.14 | 11.68 | 14.69 | 103.38 | 30.69 | 15.81 | 53.12 |
Sargan statistic | 0.699 (0.403) | 3.86 (0.135) | 2.90 (0.087) | 2.89 (0.235) | 0.47 (0.494) | 0.01 (0.936) | 5.08 (0.079) | 1.23 (0.541) | 2.98 (0.084) | 0.744 (0.388) | 1.21 (0.272) | 3.80 (0.149) | 3.29 (0.070) |
Endogeneity test | 35.84 (0.000) | 19.98 (0.000) | 19.13 (0.000) | 62.47 (0.000) | 75.19 (0.000) | 79.66 (0.000) | 44.35 (0.000) | 39.46 (0.000) | 19.12 (0.000) | 8.76 (0.003) | 8.49 (0.004) | 63.76 (0.000) | 17.33 (0.000) |
Instruments | Latitude | Latitude | Latitude | Latitude | Latitude | Landlocked | Latitude | EF | EF | SCL | SCL | Latitude | GRL |
EF | EF | EF | EF | EF | FO | EF | UKL | SCL | UKL | EF | EF | EF | |
SM | GEL | UKL | GEL | UKL |
Model 1 | Model 1 | Model 1 | Model 1 | |
---|---|---|---|---|
Variables | 2SLS | OLS | FE | RE |
CGI | 0.0246 *** | 0.0102 *** | 0.0066 ** | 0.0151 *** |
(8.718) | (10.96) | (2.10) | (0.00191) | |
POPG | −0.0012 | −0.0106 *** | −0.0026 | −0.0051 *** |
(−0.561) | (−7.38) | (−1.14) | (0.00190) | |
TO | −0.0000 | 0.0001 *** | −0.0001 * | −0.0001 * |
(−0.124) | (2.91) | (a−1.68) | (−1.69) | |
EDU | 0.0002 *** | 0.0002 *** | 0.00003 | 0.00003 |
(3.494) | (5.09) | (1.22) | (1.20) | |
URBN | 0.0004 ** | 0.0012 *** | −0.0019 *** | 0.0004 ** |
(2.050) | (11.88) | (−4.58) | (1.94) | |
Constant | 0.5496 *** | 0.5053 *** | 0.7215 *** | 0.5684 *** |
(44.159) | (73.25) | (27.39) | (39.09) | |
LM test | 3634.51 | |||
(0.000) | ||||
F-test | 25.07 | |||
(0.000) | ||||
Hausman test | 52.62 | |||
(0.000) | ||||
Observations | 860 | 1325 | 1325 | 1325 |
R-squared | 0.456 | 0.505 | 0.177 | |
Number of codes | 97 | 97 |
Model 8 | Model 8 | Model 8 | Model 8 | |
---|---|---|---|---|
Variables | 2SLS | OLS | FE | RE |
PRR | 0.0043 *** | 0.0018 *** | 0.0003 | 0.0015 *** |
(9.0651) | (11.39) | (1.29) | (7.24) | |
POPG | −0.0036 * | −0.0115 *** | −0.0025 | −0.0063 *** |
(−1.8193) | (−8.44) | (−1.20) | (−3.28) | |
EDU | 0.0001 ** | 0.0002 *** | 0.00004 | 0.00004 |
(2.5510) | (4.85) | (1.42) | (1.49) | |
TO | −0.0001 | 0.0001 ** | −0.0001 | −0.0001 |
(−0.8351) | (2.31) | (−1.79) | (−1.39) | |
URBN | 0.0006 *** | 0.0012 *** | −0.0019 *** | 0.0008 *** |
(3.3730) | (13.19) | (−4.69) | (3.84) | |
Constant | 0.2632 *** | 0.3843 *** | 0.6992 *** | 0.4450 *** |
(10.8782) | (38.47) | (19.89) | (23.96) | |
LM test | 4230.43 | |||
(0.000) | ||||
F-test | 27.91 | |||
(0.000) | ||||
Hausman test | 77.16 | |||
(0.000) | ||||
Observations | 923 | 1395 | 1395 | 1395 |
R-squared | 0.457 | 0.507 | 0.312 | |
Number of codes | 97 | 97 |
Click here to enlarge figure
Variable | Number of Observations | Mean | Standard Deviation | Minimum | Maximum |
---|---|---|---|---|---|
SDG2 | 2160 | 0.58 | 0.083 | 0.307 | 0.835 |
CGI | 2052 | 0 | 2.297 | −5.206 | 4.751 |
PRR | 2160 | 66.866 | 12.468 | 33.5 | 97 |
POPG | 2160 | 1.48 | 1.373 | −3.848 | 15.177 |
TO | 1939 | 81.801 | 47.523 | 15.564 | 384.582 |
EDU | 1518 | 61.243 | 39.891 | 1.121 | 163.935 |
URBN | 2160 | 59.871 | 21.517 | 14.61 | 100 |
lnAP | 2111 | 8.619 | 1.503 | 5.434 | 11.636 |
lnAC | 2160 | 8.85 | 1.974 | 2.308 | 14.148 |
lnAL | 2052 | 11.184 | 1.964 | 4.5 | 15.481 |
FERT | 1980 | 158.37 | 213.251 | 0 | 2192.42 |
AEMP | 2160 | 26.176 | 22.606 | 0.68 | 82.99 |
lnPG | 2154 | 8.653 | 1.537 | 5.272 | 11.626 |
lnCF | 1883 | 23.717 | 1.995 | 17.59 | 28.999 |
NC | 2154 | 6.977 | 10.219 | 0 | 58.983 |
HC | 1959 | 3.312 | 1.851 | 0.11 | 7.74 |
FDI | 2152 | 5.79 | 20.461 | −58.323 | 449.083 |
Variables | SDG2 | CGI | PRR | POPG | TO | EDU | URBN | lnAP | lnAC | lnAL | FERT | AEMP | lnPG | lnCF | NC | HC | FDI |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
SDG2 | 1.00 | ||||||||||||||||
CGI | 0.63 * | 1.00 | |||||||||||||||
PRR | 0.62 * | 0.93 * | 1.00 | ||||||||||||||
POPG | −0.37 * | −0.41 * | −0.36 * | 1.00 | |||||||||||||
TO | 0.28 * | 0.31 * | 0.35 * | −0.07 * | 1.00 | ||||||||||||
EDU | 0.33 * | 0.28 * | 0.30 * | −0.23 * | 0.09 * | 1.00 | |||||||||||
URBN | 0.59 * | 0.62 * | 0.60 * | −0.25 * | 0.26 * | 0.33 * | 1.00 | ||||||||||
lnAP | 0.65 * | 0.82 * | 0.75 * | −0.40 * | 0.27 * | 0.33 * | 0.79 * | 1.00 | |||||||||
lnAC | 0.18 * | 0.26 * | 0.26 * | −0.15 * | −0.14 * | 0.08 * | 0.20 * | 0.31 * | 1.00 | ||||||||
lnAL | −0.36 * | −0.25 * | −0.25 * | 0.04 | −0.57 * | −0.14 * | −0.26 * | −0.31 * | 0.36 * | 1.00 | |||||||
FERT | 0.40 * | 0.33 * | 0.31 * | −0.01 | 0.07 * | 0.24 * | 0.35 * | 0.36 * | 0.13 * | −0.21 * | 1.00 | ||||||
AEMP | −0.66 * | −0.72 * | −0.69 * | 0.43 * | −0.31 * | −0.36 * | −0.82 * | −0.91 * | −0.26 * | 0.37 * | −0.37 * | 1.00 | |||||
lnPG | 0.72 * | 0.84 * | 0.80 * | −0.42 * | 0.31 * | 0.35 * | 0.82 * | 0.92 * | 0.35 * | −0.28 * | 0.38 * | −0.90 * | 1.00 | ||||
lnCF | 0.44 * | 0.51 * | 0.44 * | −0.36 * | −0.17 * | 0.20 * | 0.45 * | 0.60 * | 0.62 * | 0.34 * | 0.24 * | −0.54 * | 0.66 * | 1.00 | |||
NC | −0.25 * | −0.41 * | −0.29 * | 0.45 * | −0.02 | −0.07 * | −0.08 * | −0.30 * | −0.07 * | 0.03 | −0.05 * | 0.23 * | −0.20 * | −0.22 * | 1.00 | ||
HC | 0.56 * | 0.74 * | 0.68 * | −0.46 * | 0.26 * | 0.27 * | 0.67 * | 0.80 * | 0.25 * | −0.17 * | 0.21 * | −0.75 * | 0.82 * | 0.55 * | −0.21 * | 1.00 | |
FDI | 0.09 * | 0.10 * | 0.12 * | −0.03 | 0.28 * | 0.05 | 0.10 * | 0.09 * | −0.03 | −0.24 * | −0.01 | −0.08 * | 0.07 * | −0.12 * | −0.02 | 0.07 * | 1.00 |
Variables | CGI | PRR | ||
---|---|---|---|---|
Developing | Developed | Developing | Developed | |
CGI | 0.0535 *** | 0.0201 *** | ||
(2.8208) | (5.6399) | |||
PRR | 0.0106 *** | 0.0035 *** | ||
(4.0459) | (5.5532) | |||
POPG | −0.0069 | 0.0065 ** | −0.0122 ** | 0.0026 |
(−1.4433) | (2.1635) | (−2.2748) | (1.0806) | |
TO | 0.0000 | −0.0001 | −0.0007 ** | −0.00003 |
(0.0284) | (−1.0895) | (−2.1888) | (−0.4331) | |
EDU | 0.0001 | 0.0001 | 0.0002 | 0.0001 |
(1.1214) | (1.5664) | (1.2623) | (1.1611) | |
URBN | 0.0009 *** | −0.0003 | −0.0004 | 0.0002 |
(4.3532) | (−1.2171) | (−0.9205) | (1.1974) | |
Constant | 0.5798 *** | 0.6146 *** | −0.0186 | 0.3407 *** |
(15.5771) | (31.9198) | (−0.1424) | (7.9969) | |
Observations | 495 | 524 | 364 | 559 |
Anderson canon. | 11.923 | 72.633 | 17.672 | 72.894 |
corr. LM statistic | (0.0026) | (0.000) | (0.000) | (0.000) |
Cragg–Donald Wald F-statistic | 6.022 | 41.597 | 9.109 | 41.388 |
Sargan statistic | 1.378 | 0.190 | 3.211 | 0.201 |
(0.240) | (0.663) | (0.073) | (0.654) | |
Endogeneity test | 8.030 | 8.776 | 44.921 | 10.277 |
(0.005) | (0.003) | (0.000) | (0.001) | |
Instruments | Seacoast | Latitude | Latitude | Latitude |
Landlocked | EF | EF | EF |
Variables | Model 21 | Model 22 | ||
---|---|---|---|---|
CGI as Independent Variable | PRR as Independent Variable | |||
SDG2 | lnAP | SDG2 | lnAP | |
CGI | 0.0031 | 0.2576 *** | ||
(1.3378) | (15.6295) | |||
PRR | 0.0004 | 0.0330 *** | ||
(1.0258) | (11.0458) | |||
lnAP | 0.0127 ** | 0.0141 *** | ||
(2.5515) | (3.3164) | |||
TO | 0.0005 *** | 0.0005 *** | ||
(6.0857) | (5.9799) | |||
URBN | 0.0003 | 0.0003 | ||
(1.3102) | (1.2907) | |||
EDU | 0.0002 ** | 0.0002 ** | ||
(2.2771) | (2.1410) | |||
POPG | −0.0259 *** | −0.0259 *** | ||
(−7.7263) | (−7.8664) | |||
lnAC | 0.0317 ** | 0.0287 ** | ||
(2.4204) | (2.0449) | |||
lnAL | 0.0649 *** | 0.1000 *** | ||
(3.6272) | (5.2372) | |||
FERT | 0.0002 ** | 0.0004 *** | ||
(2.2903) | (4.5090) | |||
AEMP | −0.0377 *** | −0.0422 *** | ||
(−23.0612) | (−24.5324) | |||
Constant | 0.4469 *** | 8.4401 *** | 0.4135 *** | 5.9000 *** |
(11.2857) | (42.0340) | (16.4842) | (19.1471) | |
Observations | 397 | 397 | 425 | 425 |
R-squared | 0.5599 | 0.8872 | 0.5530 | 0.8598 |
Sobel test | 0.0034 *** | 0.0005 *** | ||
(2.5167) | (3.1796) |
Variables | Model 23 | Model 24 | ||
---|---|---|---|---|
CGI as Independent Variable | PRR as Independent Variable | |||
SDG2 | lnPG | SDG2 | lnPG | |
CGI | 0.0049 *** | 0.3324 *** | ||
(2.8267) | (26.0390) | |||
PRR | 0.0010 *** | 0.0485 *** | ||
(3.8244) | (22.1286) | |||
lnPG | 0.0232 *** | 0.0225 *** | ||
(5.4727) | (6.2975) | |||
TO | 0.0002 *** | 0.0039 *** | 0.0001 *** | 0.0036 *** |
(3.1346) | (6.2912) | (3.0445) | (5.2787) | |
URBN | 0.0000 | 0.0000 | ||
(0.2568) | (0.3346) | |||
EDU | 0.0001 ** | 0.0001 ** | ||
(2.3461) | (2.2694) | |||
POPG | −0.0015 | −0.0020 | ||
(−0.8492) | (−1.1686) | |||
lnCF | 0.2427 *** | 0.2545 *** | ||
(19.6585) | (19.8832) | |||
NC | 0.0166 *** | 0.0053 * | ||
(5.5545) | (1.7653) | |||
HC | 0.1796 *** | 0.2561 *** | ||
(12.0645) | (17.6937) | |||
FDI | 0.0006 | 0.0014 | ||
(0.5999) | (1.2365) | |||
Constant | 0.3708 *** | 1.9879 *** | 0.3119 *** | −1.6865 *** |
(11.6541) | (6.5738) | (19.6807) | (−5.6612) | |
Observations | 801 | 801 | 859 | 859 |
R-squared | 0.5606 | 0.8744 | 0.5655 | 0.8515 |
Sobel test | 0.0077 *** | 0.0011 *** | ||
(5.3532) | (6.0592) |
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Galabada, J.K. Towards the Sustainable Development Goal of Zero Hunger: What Role Do Institutions Play? Sustainability 2022 , 14 , 4598. https://doi.org/10.3390/su14084598
Galabada JK. Towards the Sustainable Development Goal of Zero Hunger: What Role Do Institutions Play? Sustainability . 2022; 14(8):4598. https://doi.org/10.3390/su14084598
Galabada, Jalini Kaushalya. 2022. "Towards the Sustainable Development Goal of Zero Hunger: What Role Do Institutions Play?" Sustainability 14, no. 8: 4598. https://doi.org/10.3390/su14084598
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Ministerial meeting on food security and climate adaptation in small island developing states.
The proposed meeting will offer SIDS Ministers and Ambassadors the opportunity to explore the implications of the SAMOA Pathway as it relates to food security and nutrition and climate change adaptation. The ultimate objective is to enhance food security, health and wellbeing in SIDS. Ministers an
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Mad science blogging, a chemical hunger – part x: what to do about it.
[ PART I – MYSTERIES ] [ PART II – CURRENT THEORIES OF OBESITY ARE INADEQUATE ] [ PART III – ENVIRONMENTAL CONTAMINANTS ] [ INTERLUDE A – CICO KILLER, QU’EST-CE QUE C’EST? ] [ PART IV – CRITERIA ] [ PART V – LIVESTOCK ANTIBIOTICS ] [ INTERLUDE B – THE NUTRIENT SLUDGE DIET ] [ PART VI – PFAS ] [ PART VII – LITHIUM ] [ INTERLUDE C – HIGHLIGHTS FROM THE REDDIT COMMENTS ] [ INTERLUDE D – GLYPHOSATE (AKA THE ACTIVE INGREDIENT IN ROUNDUP) ] [ INTERLUDE E – BAD SEEDS ] [ PART VIII – PARADOXICAL REACTIONS ] [ PART IX – ANOREXIA IN ANIMALS ] [ INTERLUDE F – DEMOGRAPHICS ] [ INTERLUDE G – Li+ ] [ INTERLUDE H – WELL WELL WELL ] [ INTERLUDE I – THE FATTEST CITIES IN THE LAND ]
Assuming you take our main thesis seriously — that obesity is the result of environmental contaminants — what should you do about it?
Our suggestions are very prosaic: Be nice to yourself. Eat mostly what you want. Trust your instincts.
Diet and exercise won’t cure obesity, but this is actually good news for diet and exercise. You don’t need to put the dream of losing weight on their shoulders, and you can focus on their actual benefits instead. You should focus on your diet — not to get thin, but to make sure that you have enough energy to do everything you want to do in life. This means eating enough and making sure you get what you need. You should exercise — not to slim down, but to gain strength and energy, and you shouldn’t get discouraged when you don’t drop 50 lbs fast.
Don’t be mean to fat people. If you’re fat, don’t be mean to yourself about it. Don’t be a dick.
And this doesn’t apply to most of our readers, of course, but just in general — we gotta stop spending money on circular nutrition research. It’s clearly not going anywhere. Other theories of obesity don’t engage with the observations that are out there about the obesity epidemic , and try to explain the wrong thing.
Most theories focus on the dynamics of individual weight loss, under the assumption that obesity is the result of the normal mechanics of eating, exercise, weight loss, and weight gain. But we think that the dynamics of individual weight loss have almost nothing to do with the real question, which is why obesity rates are so much higher now than they were in the 1970s, and the rest of human history. Individuals can gain or lose 15-20 lbs from their set point, but this is messing around within the range of control — we only care about the set point.
Let’s say it’s 50 °F outside. If your thermostat is set to 72 °F and you open the door, your house’s temperature will drop at first and then will go back up to the set point of 72 °F. If your thermostat is set to 110 °F and you open the door, your house’s temperature will drop at first and then will go back up to the set point of 110 °F (assuming your furnace is strong enough).
This is a standard feature of how homeostatic systems respond to major disturbances — the controlled value swings around for a bit until the system can get it back under control, and send it back to the set point. So all the diet and exercise studies we’ve done over the last 50 years have just been an exercise in who can create the biggest, most jarring disturbance — but the lipostat always finds a way to bring your weight back where it wants it.
So all these “punch the control system as hard as we can” studies don’t tell us anything about why the thermostat is set to 110 °F in the first place, which is what we’re really interested in.
Bestselling nutrition books usually have this part where they tell you what you should do differently to lose weight and stay lean. Many of you are probably looking forward to us making a recommendation like this. We hate to buck the trend, but we don’t think there’s much you can do to keep from becoming obese, and not much you can do to drop pounds if you’re already overweight.
We gotta emphasize just how pervasive the obesity epidemic really is. Some people do lose lots of weight on occasion, it’s true, but in pretty much every group of people everywhere in the world, obesity rates just go up, up, up. We’ll return to our favorite quote from The Lancet :
“Unlike other major causes of preventable death and disability, such as tobacco use, injuries, and infectious diseases, there are no exemplar populations in which the obesity epidemic has been reversed by public health measures.”
The nonprofit ourworldindata.org has data from the WHO covering obesity rates in almost every country in the world from 1975 to 2016 . In every country in this dataset, the obesity rate either stayed the same or increased every single year from 1975 to 2016. There is not one example of obesity rates declining for even a single country in a single year. Countries like Japan and Vietnam are some of the leanest countries in the world (about 4% and 2% obese, respectively), but in this dataset at least, even these super-lean countries don’t see even a single year where their obesity rates decline.
We see the same trend even for smaller-scale data. The Institute for Health Metrics and Evaluation (IHME) has a dataset of county-level obesity data from 2001 to 2011 , which is publicly available on their website. Using this we can look at obesity rates across the United States, and we can see how much obesity rates have changed in each county between 2001 and 2011. We see that between 2001 and 2011, obesity rates decreased in zero counties, stayed the same in zero counties, and increased in 3,143 out of 3,143 counties and county equivalents in the United States.
The smallest increase between 2001 and 2011 was in Eagle County, Colorado, where obesity rates went from 20.0% in 2001 to 21.5% in 2011, an increase of 1.5%. You’ll notice that this is Colorado once again, and it turns out that the five counties with the smallest increase from 2001 to 2011 are all in Colorado. Of the 25 counties with the smallest increase, 13 are in Colorado. The take-home here is that Colorado really is special.
If we zoom in a little further on these data, we can find ONE case of obesity rates declining — they went from 22.7% in 2009 to 22.4% in 2011 in Fairfax City, Virginia, a drop of 0.3%. There were also two counties where rates stayed the same 2009-2011. But this is one county with rates going down, two staying the same, and 3,140 going up. If population-level reversals are this tiny and this rare, it’s hard to imagine that there is much an individual can do to change their own weight.
But that said, here are a few ideas, approximately in order from least extreme to most extreme.
First off, there are a few things that won’t change how many contaminants you’re exposed to, but that may have an impact on your weight anyways.
1. — The first is that you can put on more muscle mass. This won’t affect your weight as it appears on the scale, but it does often seem to affect people’s body composition . The lipostat pays attention to how much fat you have, but it also seems to pay some attention to how much you literally weigh (see these studies in mice, and this recent extension in humans ). So if you gain muscle mass, you may lose fat mass. For advice on how to gain muscle mass, please see the internet.
2. — The second is that you could consider getting gastric bypass or a similar, related surgery . Our understanding is that these procedures are very effective at causing weight loss in many cases. However, they are pretty dangerous — this is still a surgical procedure, and so inherently comes with a risk of death and other serious complications. If you consider this option please take it very seriously, consult with your doctor, etc.
Many of you, however, are not just interested in weight loss, or are interested in weight loss along with reducing how many mystery chemicals you’re exposed to — “You stupid kids I don’t want to lose weight I want to get these contaminants out of my body!!!” So here’s a list of steps you could take to reduce your exposure and possibly lose weight, again approximately in order from least extreme to most extreme.
1. — The first thing you should consider is eating more whole foods and/or avoiding highly processed foods. This is pretty standard health advice — we think it’s relevant because it seems pretty clear that food products tend to pick up more contaminants with every step of transportation, packaging, and processing, so eating local, unpackaged, and unprocessed foods should reduce your exposure to most contaminants.
2. — The second thing you can do is try to eat fewer animal products. Vegetarians and vegans do seem to be slightly leaner than average, but the real reason we recommend this is that we expect many contaminants will bioaccumulate, and so it’s likely that whatever the contaminant, animal products will generally contain more than plants will. So this may not help, but it’s a good bet.
3. — The third thing is you can think about changing careers and switching to a leaner job. Career is a big source of variance in obesity rates, so if you have a job in a high-obesity profession like truck driver or mechanic, consider switching to a job in a low-obesity profession like teacher or surveyor. For a sense of what careers are high- and low-obesity, check out this paper about obesity by occupation in Washington State and this paper about obesity by occupation in US workers . If you are already in a pretty lean career, then ignore this one.
We think this goes double if you’re in a profession where you’re working with lithium grease directly, or even around lithium grease. Do what you can to stay away from the stuff.
4. — The fourth thing you can consider is changing where you live. The simplest is to change where you live locally — stay in the same area, but move to a different house or apartment. This one is tricky, and sort of a shot in the dark. How will you know if you are moving to a more or less-contaminated house? But if you suspect your house is high in contaminants, it might be worth moving. If you find specific contaminants especially concerning, you can try having your local water tested for them.
5. — A better option is to move to a leaner place altogether. If you’re in the United States, we recommend Colorado. Colorado is the leanest state , has exceptionally pure water sources, individual cities and counties in Colorado are extreme lean outliers , etc. Unbelievably, this comic exists:
If Colorado doesn’t suit you, you can move to some other state — Hawaii and Massachusetts are not far behind. To find your dream location, look at the CDC’s list of states , or one of the datasets of county-level data like this one or this one , and find a location with a lower rate of obesity than where you currently live. Or pick one of the places from the list of leanest communities in the US .
6. — This may not be extreme enough . After all, even Colorado is more than 20% obese. So a more radical version of the same idea is moving to a leaner country altogether.
If you live in the United States, the good news is that most countries are less obese than where you live now, even if you live in Colorado. Especially good choices seem to be Japan, South Korea, and Thailand, but there are many options — for the whole picture, check out the summary from Our World in Data .
But don’t just take our word for it, listen to these happy customers. Like this person who lost weight over five months in Vietnam , this person who moved to Vietnam and lost 112 pounds in ten months , this person who lost about 4kg (9lbs) after about two months in Japan (and similar stories in the comments) , this person who lost 5lbs on a two-week trip to Japan , or this person who lost 10lbs during a two-week trip to Japan, despite not keeping up with their exercise regimen . Most of these people attribute their weight loss to eating less and walking more, but you’ll also notice that most of them say it was easy to eat less and walk more, and that many of them report being surprised at how much weight they lost and how easily they lost it.
We’ve also gotten a number of similar stories from commenters on the blog. First up is Julius, who said :
I currently live in Seattle but have moved around a lot. I’ve made 6 separate moves between places where I drank the tap water (mostly USA/UK/Hungary) and places I haven’t (South East Asia, India, Middle East). Whenever I’ve spent significant time in bottled water countries I lost weight (up to 50 lbs), and each time, save one 3 month stretch in Western Europe, I gained it back in tap water countries. I also lost weight for the first time in the States (20 lbs) this year around the time I switched to filtered water.
There’s also a similar story from Ross:
Very thought provoking and well researched piece. How about Japan? Very low rates of obesity. Similar issues with chemical residue. Anecdotally when I moved to Japan from the West I began to lose weight involuntarily, down to a BMI of 22. When I moved back to the West I regained weight. It’s a big rich country with plenty of processed, packaged food.
And a story from Tuck about their daughter:
Yes, my daughter is going to college in Japan. They have the “Freshmen 15 lbs” over there as well, except it’s the 15 lbs the foreigners lose when they go on a Japanese diet. Got a few panicked messages about “not having anything to wear”… LOL
So before you sign up for the gastric bypass, try spending a couple months in a lean country and see how it goes.
The question “what do we do about it” also includes the question “what research comes next?” Here’s what we’re thinking.
A lot of people’s first instincts when reading this work is to propose correlational studies. (We don’t necessarily mean a literal correlation, we just mean something that’s not a controlled experiment.) But we think that correlational studies are the wrong way to go at this point.
The first reason is statistical. We covered this in Part IV but it bears repeating. Because most of the modern variation in obesity is genetic, the apparent effect of any contaminant will be quite small, probably no larger than r = 0.50 and maybe a lot smaller. In any study we could run, the range of the variable would probably be restricted, and when the range of a variable is restricted, the correlation always ends up looking smaller than it really is . Some people have proposed we do animal studies for more control — but this is also a bad choice statistically, since the obesity effects in animals seem to be smaller than the effects for humans.
The combination of these problems means that any correlational study would be searching for a pretty small effect, and that means you would need a huge sample size to even have a good chance of finding a potential relationship. So “run a quick correlational study” starts looking like “find a way to fund and organize a study with 1,000 mice”. While we love mice, this seems like an awful lot of them. And even if we have enough statistical power that we have a 90% chance to detect a relationship, that still means we have a 10% chance of missing the relationship altogether. We don’t love those odds.
Second, A Chemical Hunger already documents a lot of correlational evidence for contaminants in general, and for a few contaminants in particular, especially lithium. If you already find this evidence compelling, it’s hard to imagine that one more piece of correlational evidence will do anything for you. And if you don’t find our review convincing, it’s hard to imagine that another piece of correlational evidence will change your mind.
The contamination theory of obesity has to be possible, in the sense that we know chemicals can cause weight gain and we know various chemicals are in the environment. We hope we’ve also convinced you that it’s plausible. Now we want to figure out, is it true? More correlational evidence isn’t going to get us there.
So overall we recommend going right for the jugular. If this theory is correct, then we have a good shot at doing what we really want to do — actually curing obesity — and no result could be more convincing than that.
So in general, we approve of the idea of doing experiments to just cure obesity straight up.
Normally in public health it’s hard to do this kind of experiment, because it’s unethical to expose people to dangerous chemicals. Back when they were trying to figure out if cigarettes cause cancer, they didn’t do any studies where they assigned people to smoke 3 packs a day. But there’s nothing unethical about removing a contaminant from the environment, so we like that approach.
We call these experiments, and they are, but in many cases we can actually cheat a little by not bothering to include a control group. People almost never spontaneously stop being obese, so we can just use the general obesity rate in the population as our control group.
Generally speaking, there are two approaches. “Broad-spectrum” experiments take the overall contaminant theory seriously, and just try to reduce contaminant exposure generally, without committing to any specific contaminant. “Targeted” experiments go after one contaminant in particular, and see if controlling levels of that contaminant alone can lead to weight loss.
These have clear trade-offs. The broad-spectrum experiments are more likely to work and require less experimental control, but if they cure obesity, they don’t tell us what contaminant is responsible (curing obesity would still be pretty cool tho). The targeted experiments are less likely to work because we might go after the wrong contaminant, or we might fuck up our experimental control and let some contamination through — but if they DO work, then we have strong evidence that we’ve found the contaminant that’s responsible.
For all of these studies, the big hurdle is that we don’t know how quickly obesity can be reversed, even under the best circumstances. It might also vary a lot for different people — we have no idea. So if we try any of these experiments, we need to run them for several months at the very least, just to get a good idea of whether or not it’s working. Maybe if we’re lucky we’ll find out you can cure obesity in 2 weeks; but 3 months, 6 months, or even 1 year seems more plausible.
Below, we propose a few basic ideas for experiments. These aren’t exhaustive — as we do more research, we may come up with new and better ways to try to cure obesity. But they seem like an ok place to start.
Slime Mold Time Mold’s Excellent Adventure
The idea is simple. Some places, like Colorado, are pretty lean relative to everywhere else. We think that’s because those places are less contaminated. So we find some people who are obese, and pay for them all to take a year-long vacation to Boulder, Colorado, and see if they lose any weight.
For better effect, go a step further and send them to one of the leanest countries in the world instead. Vietnam seems to be the leanest country in the world right now, at only about 2% obese, and rent is pretty cheap there, so that would be a good option. If you want to stay in heavily industrialized nations, Japan is a good alternative; if you want to stay in the English-speaking world, maybe the Philippines. There are lots of good places to choose from.
For full effect, you would want your participants to eat the local food and drink the local water as much as possible. If they’re eating American food and drinking American beer, then you’re right back where you started.
(If you know of any study abroad or similar programs that we could piggyback on, please let us know !)
Throw Water Filters at the Problem and See What Happens
This is a broad-spectrum version of a targeted idea, below. The basic idea is simple. Contaminants might be in the water supply; filters get lots of stuff out of water; people drink water. So in this study, we find a bunch of people who are overweight or obese, send them the strongest/best water filters we can afford, and see if they lose any weight over the next several months.
For even more effect, send the filters to people who live in the most obese states, or even target some of the most obese communities directly.
This really is not a precision instrument — filters don’t get everything out of water, and water might not even be your main source of contaminants. Maybe your food or your carpets are the bigger problem. But if losing weight were as simple as throwing a water filter at the problem, that would be pretty exciting, and we would want to know.
Right now lithium is our top suspect, so we’re using lithium as our go-to example in all of these experiments. But if it turns out that lithium isn’t a good match, any of these experiments could be retrofitted to target some other contaminant instead.
To use a targeted approach, we need to be able to figure out how much exposure people are getting, and we need to know what we can do to reduce that exposure. So there are a few pre-experiment projects we need to do first.
To begin with, we need to figure out which water filters (if any!) remove lithium from drinking water. If we can find a filter that works, this will let us make sure any water source is lithium-free.
In addition, we’re worried that lithium might accumulate in food , so we need to do another study where we look at as many different types of food as we can and try to figure out if there are high levels of lithium in any of the stuff we’re all eating. Without this, any study will be hopelessly complicated because we won’t be able to control for the lithium in your food. But if we figure out what crops (if any) are concentrating lithium, maybe we can figure out a way to feed people a low-lithium diet.
Targeted Water Filters
Assuming we can find a water filter that does the job, we could do a pretty straightforward study where we send people a water filter that takes lithium out of their water, and see if they lose weight over a couple months.
For maximum effect, we would also want to make sure they weren’t getting any lithium from their food, which is why we want to do a study on how much lithium is in the food supply. It’s not clear how easy this would be — we might have to curate food sources and provide people with all their meals as well, which would make this study a hundred times more complicated.
There are a couple other things we could do to improve this study. We could focus on sending water filters to people in the most obese parts of the country, or to places where we already know the water is contaminated with lithium.
We could test the amount of lithium in people’s blood, urine, and/or saliva as they use the filter, see if it goes down, and see if the decrease in lithium in their body tracks on to weight loss. Assuming people did lose weight, this would be important because it might help us figure out more about the mechanism of lithium leaving the body. Some people will probably clear lithium faster than others, and if lithium causes obesity, we would want to be able to figure out how to help people clear it from their body as fast as possible.
We could also do a slightly bigger study, where we go to one of the fattest places in the US and install a bunch of whole-home water filtration systems for a couple randomly selected families who are overweight or obese. This would be more expensive but it would have some perks. If it turns out that showering in lithium-tainted water is really the active ingredient, and not just drinking it, then a whole-home water filtration system would take care of that.
There’s also a small chance that there’s just no filter on the market that can get lithium out of drinking water. Or maybe distillation works, but the cost is prohibitive for a whole-home system. In that case, we could rent a few water tanker trucks, fill them with water we know is low in lithium (we’ll import it from Colorado if we have to!), and take them to a cul-de-sac in one of the most obese communities in the US. If we can find a neighborhood who’d sign up for this, we could switch their houses’ water supplies over to our tanker trucks for a few months, bringing in new water as needed, and see if that did anything for their health.
Amish Obesity
This piece from the LA Times is pretty bad, but it tells an interesting story. In part of Ontario, Canada, a group of Old Order Amish have “stunningly low obesity levels, despite a diet high in fat, calories and refined sugar.” The figure they quote is an obesity rate of only 4%. But about 200 miles south, the Amish in Holmes County, Ohio have obesity rates similar to the rest of the population, closer to 30% obese.
These two groups should be genetically similar. Both groups grow most of their own food. Both of them have pretty similar lifestyles — despite what the LA Times piece and this related article say, even if “only” 40% of the Amish in Ohio do hard farm labor, their lives are still more like the Amish in Ontario than the non-Amish in Holmes Country.
This makes them almost a perfect comparison. Why are the Amish in Ohio so much more obese than the Amish in Ontario? If the contamination hypothesis is correct, then we should be able to look at the local environments of these two communities and find more contamination (of one sort or another) in Ohio than in Ontario.
Because both groups grow most of their own food (we think?), we don’t need to worry about the influence of food imported from elsewhere — whatever contaminants are in their water will also be in their plants, and they won’t be bringing in contaminated food from outside. This makes this situation a much more controlled environment to study our hypothesis.
If lithium is the contaminant that causes obesity, we might expect to see deeper wells in Ohio than in Ontario. Information about the Amish is hard to find on the internet, for obvious reasons, but we have found some information that suggests that the Amish in America do use drilled wells , some of which may be relatively recent . We can’t find anything about the wells used by the Amish in Ontario — but it would be interesting if they were still using older, shallower wells for their water.
Another thing we might expect to see, if lithium is to blame, is evidence of some kind of fossil fuel activity in Ohio and not in Ontario. Well, in our last post we did review evidence for fossil fuel contamination in a number of places in Ohio . And when we were looking for documentation on water wells in Amish Ohio, we came across articles like Fracking on Amish Land (in Ohio), Energy Companies Take Advantage of the Amish Prohibition on Lawsuits (in Ohio), this excerpt about natural gas wells (in Pennsylvania), and Tradition, temptation as Amish debate fracking (in Pennsylvania, but mostly in Ohio).
Ontario has its own problems, including thousands of abandoned gas wells , but very few of them appear to be on Amish land. Zoom in on the towns of Milverton, Millbank, Newton, Linwood, and Atwood on that map, and you’ll see that there are almost no petroleum wells around these Amish communities. And unlike in Ohio, we haven’t found any news stories about recent drilling or fracking on Amish land in Ontario.
Or we could just go test the water. It’s a simple question, how much lithium is in the water in each place, and testing for other contaminants might not be a bad idea either. If we find similar levels of lithium in both places, and there are no complicating factors like imported food, that would be a strike against lithium as an explanation. But if there’s more lithium in the food and water in Ohio than in Ontario, that would be quite a mark in favor of the lithium hypothesis. Assuming they were interested, we could then work with the Amish in Ohio to try to get the lithium (or whatever) out of their water, and see if that reduced their rates of obesity.
We don’t expect that we have many Amish readers, but if you know of a good way to get in contact with the Amish in either of these locations, we’d be interested in talking to them!
There are also a few ideas we have that we won’t be pursuing ourselves, but if someone else (or a small team) wants to go after them, we would be happy to advise.
Taking lithium out of the water supply as a whole would be pretty hard, so it’s not usually an option. But it might be an option for countries that get most of their drinking water from desalination . You could run this as an experiment — one desalination plant uses lithium-free brine while another continues with the normal procedure — but you wouldn’t have to. In this case, there’s no need for a control group. If Saudi Arabia or Kuwait changed their desalination process so that no lithium ended up in their water, and saw their obesity rate fall 10% over the next five years, that would be evidence enough. Or you could do a version of this study with some other relevant group, e.g. seafarers drinking desalinated water as suggested by commenter ugoglen . So if anyone is able to do something like this, we would be interested in being involved.
In our post on PFAS , we did a small amount of regression modeling using data from The National Health and Nutrition Examination Survey (NHANES) and found evidence of a relationship between BMI and certain PFAS in the data for 1999-200, 2003-2004, and 2005-2006. This finding is very suggestive, but we only tested some very simple models, and we only looked at three of the datasets that are available. We think that a bigger analysis could be very illuminating, but model fitting isn’t our specialty. We would love to work with a data scientist or statistician with more model fitting experience, however, to conduct a more complete analysis. So if you have those skills and you’re interested, please let us know !
We’re still pretty interested in the all-potato diet . So far all we have are anecdotes, but the anecdotes are pretty compelling. Chris Voigt famously vowed to eat nothing but 20 plain potatoes (and a small amount of cooking oil) and lost 21 pounds over 60 days, without feeling very hungry. There’s also Andrew Taylor of Australia, who lost 114 lbs over a year of eating nothing but potatoes and reports feeling “totally amazing”. Last we heard he’s still doing pretty well . Magician Penn Jillette lost over 100 lbs using a strategy that started with two weeks of a potato-only diet (h/t reader pie_flavor), and seems to be keeping it off . This also inspired at least one copycat attempt from a couple who have jointly lost over 220 lbs starting with two weeks of an all-potato diet .
There’s also this comment from u/DovesOfWar on reddit:
To complement the potatoes anecdote, at some point to save money and time I ate almost nothing but potatoes, onions and butter and I lost like 60 pounds. I stopped because everyone thought I was starving (despite not being hungry) and I chugged it off to extreme lazyness/depression (despite not being sad) so I stopped doing that and never connected it to my diet, but what I should have done is write a fad book on the diet and solve the money problem that way. I’m back to a normal healthy 29 BMI now and still relatively poor, so I see I interpreted the experiment completely wrong and now my life sucks.
Based on those examples, you can see why we’re interested. It seems pretty low-cost (potatoes are cheap) and low-risk (if you feel bad, you can stop eating potatoes). If someone wants to organize a potato-centered weight-loss study, or if people just want to get together and try it for themselves, we’d be happy to advise. You can coordinate on the subreddit u/pondgrass set up over at r/spudbud if you like, though so far there doesn’t seem to be much activity.
We’re also interested in the effect of alkali metal ions, especially potassium . Lithium, currently our prime suspect, is an alkali metal ion that appears to affect the brain. Other alkali metal ions like sodium and potassium also play an important role in the brain, and there’s evidence that these ions may compete with each other , or at least interact , in interesting ways (see also here , here , and here ). If lithium causes obesity, it may do so by messing with sodium or potassium signaling (or maybe calcium ) in the brain, so changing the amount of these ions you consume, or their ratios, might help stop it.
This is supported by some hints that potassium consumption is related to successful weight loss . Potatoes are high in potassium, so if the all-potato diet really does work, that might be part of the mechanism.
You can easily get sodium from table salt, and you can get potassium from potassium salts like this one or this one . We’ve tried them, and we find them a little gross, but to some people they taste just like regular salt. If that’s no good, there are always dietary sources like potatoes.
So trying various forms of alkali-metal diets — high-K+, high-K+/low-Na+, high-K+/high-Na+, high-K+/low-Ca2+, etc. — seems pretty easy and might prove interesting. As before, if someone wants to organize a community study around this angle, or if people want to try it for themselves, we’d be happy to advise. These salts are pretty safe, and not prescription medications, but they’re not quite as basic as potatoes — before you try seriously changing your sodium or potassium intake, please talk with your doctor.
Also, how about lithium grease ? These greases are basically the perfect slow-release form of lithium, which make them kind of concerning. Mechanics work with lithium grease and are relatively obese. But there are alternative kinds of greases that don’t use lithium, and sometimes companies intentionally switch what kind of grease they use. If a company switched out lithium grease for some other grease in one of their factories, we could compare the weights of workers at that factory to workers at other factories, and see if there was any weight loss over the next few years. And what happens when mechanics who use lithium grease every day switch to a new job? What happens if they get promoted to a desk job? What happens when they retire? If you know a group of mechanics or some other group that works with lithium grease and might be interested, please let us know !
We’re also interested in advising original ideas . We love it when you send us ideas we never would have come up with ourselves. So if you have some great idea — a review of a contaminant we didn’t cover, another idea for a related study, relevant anecdotes that might inspire something, etc. — let us know . If we like it, we’ll do what we can to help — advise you, promote it, try to help you get funding, whatever.
This is the end of A Chemical Hunger . We will still write more about obesity, and probably more about contamination, but this is the end of the series. Thank you for reading, commenting, sharing, contributing, questioning, challenging, and yes, even disputing! We’ve learned a lot from your comments and questions — and we hope you’ve learned something from reading!
Even if you still don’t find our hypothesis convincing, thank you for reading the series all the way to the end! We think it’s great that you were willing to give our wacky idea the time of day. This kind of exploration is essential, even if some of the theories turn out to be a little silly. And even if our theory is totally wrong, someday someone will figure out the answer to this thing, and we’ll send the global obesity rate back down to 2%.
As we mentioned, we want to conduct some research to follow up on the book-length literature review you just finished reading. Our near-term goal is to better understand how people get exposed to contaminants, especially lithium, so we can give advice on how to avoid exposure. Our medium-term goal is to figure out what causes obesity, probably by trying to cure it in a sample population. Our long-term goal is to try to cure it everywhere. That would be pretty cool.
If you’re interested in supporting this research, you can become a patron on patreon , or contact us if you want to help fund a larger project.
In conclusion: Be excellent to each other. Party on, dudes.
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56 thoughts on “ a chemical hunger – part x: what to do about it ”.
Thank you for this. I remember commenting earlier and thinking lithium and PFAS seem to be the strongest possibilities. This is interesting especially as we have proposed ways of falsifying the lithium/PFAS hypotheses. My takeaway is use my filter even if its a little cumbersome and get an RO filter once I can. It is also pretty alarming how hard it is to lose weight and maintain weight loss….ive lost about 40ish lbs and have another 15ish to go but I’m guessing I’ll need to try extra hard and maintain extra caution as I proceed.
Lastly I really dislike how this got basically bood off the slate star codex reddit with some people jumping to impugning motives or just being unnecessarily mean.
ISTR that low lithium can cause emotional disregulation and suicide. There’s probably a happy middle, but be careful about throwing maximally-strong anti-lithium filters at people.
Good point!
Thank you for the fantastic series. I really enjoyed how thorough and honest you were in your investigations. During the summer, someone on Lesswrong shared a pdf of a draft of this series, which is what I first read, which lead me to this blog. Do you have this series as a single document? I would love to have this in my personal archive of literature.
Regarding water filters, there must be plenty of natural experiments like this that can be studied:
https://eu.patriotledger.com/story/news/2021/09/17/hanover-tries-switching-carbon-filters-combat-pfas-water-contamination-gac/8319363002/
Maybe I said this before, but if you want to compare how obesogenic different environments are I think you should compare ethnicity to ethnicity. For example, in the US in 2004-2006 the age-adjusted obesity rate in the US for whites was 23.6%, but the obesity rate for Japanese was only 8.7% and for Vietnamese only 5.3%.
https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=55
Interesting study:
“After adjusting for other factors, compared to Whites, being Hispanic and Blacks were associated with higher odds of obesity (OR = 1.47, 95%CI = 1.31–1.65; OR = 2.04, 95%CI = 1.65–2.53, respectively); being Chinese, Korean, and Vietnamese were associated with lower odds of obesity (OR = 0.28, 95%CI = 0.18–0.45; OR = 0.14, 95%CI = 0.04–0.46; OR = 0.28, 95%CI = 0.14–0.58, respectively). Compared to Chinese, being Japanese and Filipino were associated with higher odds of obesity (OR = 2.75, 95%CI = 1.52–4.95; OR = 2.90, 95%CI = 1.87–4.49, respectively).”
https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-11612-z
Wow, nice! Yeah we’ll look into this one!
Thank you for this series! I have a not much related question, but since you’ve studied the topic you probably know something about it. What’s up with the few people in every population who go through deliberate weight loss and then actually keep it off? They make up about 5% of all “weight watchers”. That’s enough people that everyone knows at least some of them.
At first I thought that perhaps by starting the diet they gave up some specific food that contains a lot of the guilty contaminant, but looking at the two closest examples I have, one of them gave up most of the butter, milk and sausages, the other one gave up sugar and starch but keeps eating processed meat, so they don’t overlap at all.
My earlier hypothesis was that these people have a really strong will and grit allowing them force themselves to eat less food against their own preferences (which would also cause some of the genetic weight differences since grit is highly heritable). But again, it doesn’t fit observations. At least one of these people doesn’t seem to have enough grit to do unpleasant work always on time and actually identifies with the low-willpower personality type; and none of them say their new eating habit causes them high suffering that you would expect from a person who works against their lipostat by raw willpower.
So what’s their secret?
Glad you enjoyed it!
This is a good question, and we suspect that this 5% comes from multiple places. There probably are a small number of people who can keep weight off by willpower + social support alone, even though it’s impossible for most people. Other people might reduce their contaminant exposure by moving, changing jobs, changing hobbies, etc. Some people will also develop a medical condition, get a brain tumor, side effect of a prescription medication, start smoking, start a cocaine habit, etc. Biological/medical reasons to drop weight are rare, but when you add them all up, it might be close to 5%.
And we think you’re right, some people do give up a food that contains the contaminant. The important thing to keep in mind is that the same food won’t always have the same amount of contamination. So milk from one farm might be high in contaminants, while milk from another farm is low in contaminants. This means you can’t expect a reliable pattern based on what people gave up, which is part of why diet studies are so confusing.
Seriously, I counted macros and exercised and lost 50 lb and kept it off. I trained a fitness coach to encourage others to do the same. Until I read this I thought my weight loss was due to my diligence and discipline. Now I’m remembering that I happened to be transitioning off of prescription lithium at the same time, and I am wondering if my whole life is based on a lie… Great article though!😂🤷🤦♀️ But, if lithium is the proposed mechanism for this, why are we seeing a concurrent epidemic of depression? I can attest that lithium is an excellent antidepressant, even if you aren’t bipolar. At very least, wouldn’t you expect to see similar geographical trends in mental illness morbidity to the obesity morbidity?
Thanks for the series! Really loves it, and very interesting theory 🙂
Reading this sentence at the end, “And even if our theory is totally wrong, someday someone will figure out the answer to this thing, and we’ll send the global obesity rate back down to 2%.”, made me realize something: what is “obesity”? Obviously we should make sure that our food and drinking water isn’t contaminated with substances that might be having negative effects on our endocrine and nervous systems. But beyond “morbid obesity”, is being “overweight” that harmful? Is BMI even a useful tool?
[…] has another installment in their series on the epidemic of obesity, and this one deals with what can be done. They do recommend a whole-food plant-based diet (and I […]
I’ve followed the whole series and really enjoyed it! But my memory is unfortunately, lousy. Did you address the possibility that substantial weight gain may be a one-way transformation in many cases, due to essentially permanent changes in control systems? (Trying not to be lazy, so I did try a few searches before writing this.) From the above post it seems like you would bet against it, but for whatever reason, it’s something I’ve always assumed. If it turns out to be true, it would of course be much more difficult to figure out if an environmental contaminant is the cause.
This is a great question. We’re optimistic about reversals because in rare cases people do lose incredible amounts of weight and keep it off. There are plenty of anecdotes of this happening, and in this post we quote a couple of cases where weight loss happened quite rapidly when people tried a drastic diet or spent time in another country. Taken together, that makes us think that at least in some cases people who are no longer exposed to contaminants will drop large amounts of weight.
This may not happen for everyone, though — we expect major differences in how fast people lose weight, even if we can remove all contamination. For example, let’s imagine (for the sake of argument) that lithium normally leaves the body very slowly, unless you take high doses of potassium, which (again just hypothetically) forces it out of the brain. Then you might only see weight loss in the rare cases where someone was both no longer exposed to lithium AND started consuming a decent amount of potassium.
regarding the potassium thing, could I accomplish this with say a potassium gluconate supplement from Amazon? Thanks again, this whole series has been fantastic, and the first thing I’ve ever contributed to on patreon.
I certainly hope you don’t stop here. You really have the most significant weight-loss/gain hypothesis in decades. Don’t stop here!
I did notice a science article today on Lithium selective filters. Might be useful. https://www.pnas.org/content/118/37/e2022197118
Just FYI while there is a lot of fracking in Pennsylvania, due to tougher state regulations on disposal, almost all of the wastewater generated by that fracking is disposed of across the border in Ohio. (Interestingly that’s the reason for a cluster of induced earthquakes in Youngstown, Ohio — wastewater injection is known to cause earthquakes.)
Very interesting! Maybe this is part of why Pennsylvania is relatively lean even with so much fossil fuel activity!
What is your opinion on Zinc?
I’m having some success (-10lbs) with running in a sauna suit. I sweat more and lithium is water soluble so it fits the thesis The adrenaline also makes minor aches and pains disappear, making the experience less miserable overall than cold runing. The only other change I’ve made is snacking on high salt, shell-on sunflower seeds when I’m bored/stressed.
Something I noticed: If I add more salt to my food I’m quicker “full”
Your series, and particularly the part on low-dose antibiotics and gut microbiota, made me spend a lot of time researching gut bacteria and their effect on obesity. Along the way I learned of some relatively new results showing that Akkermansia Muciniphilia strains in particular produce a protein (in fact several proteins) that regulate obesity, insulin response and seem correlated with the thickness of the intestinal lining (with other implications) [0]. At the same time, the modern high-fat/high-carb western diet seems to reduce Akkermansia abundance significantly (by up to 99% in mice) for reasons that aren’t well understood. I’m curious if there is any real empirical way to explore the effect of these chemicals on the gut microbiome, rather than just looking at the end-results (weight etc.) or even better — if anyone has done some of this work. (I can’t find much for the chemicals you mention in this series.)
In a similar vein, I have been curious about emulsifiers and preservatives in food. In particular, I know that emulsifiers have an effect on gut lining and bacteria.
Likewise, I have been interested in the pro-biotic properties of fermented food. In fact, I make my own fermented hot sauce. Fermented foods can help with microbiota deficiencies by supplying new bacteria, though not the whole spectrum we need.
Thinking about the concept of “send people from westernized countries to live for a period in a different country,” there might be some interesting natural experiments. I’m thinking particularly of say, the Peace Corps. In doing some googling, I’ve seen it asserted a lot that the rule of thumb is that, within the first months of living in the site country, men tend to lose weight and women tend to gain weight, which is, er, something. https://www.reddit.com/r/peacecorps/comments/51gx08/how_much_weight_did_you_gainlose_during_service/ ; https://www.reddit.com/r/peacecorps/comments/8b6wlx/weight_loss_during_service/
I have some purely anecdotal evidence that supports that finding. I’m from the UK and worked in a remote part of Madagascar for a year on the coast. Foreign volunteers, mostly white, mostly native English speakers stayed with us for 6 week stints. A small range of edible plants was grown locally. White rice was imported. Baguettes were brought once every few days from the nearest town. The protein-rich foods, most common first, were fish, beans, and very lean domestic animal meat. People were divided on what water they would drink. Those of us who hated plastic waste religiously used the ceramic-filtered ground water, which was saltier than we were used to. Others would only drink bottled water. We kept observing that women put on weight and men lost weight.
I got very, very skinny. If we had eaten a dish that left any fat then I dumped a lot of white rice on the empty serving dishes and stirred it up to get all the fat.
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Were you one of the ones drinking bottled water or one of the ones deinkinf ceramically filtered water? 🙂
Filtered. So was my girlfriend who put weight on.
This was a really great series that I hope you guys turn into a book someday. Your lithium explanation makes more sense than high carbs and seed oil causing obesity claims. Perhaps another way of checking could be to look at rates of people with liver damage in certain regions. Long term exposure to high levels of lithium can damage your liver. My best friend is bipolar and took lithium for years and now has minor but permanent liver damage.
I enjoyed your early posts. I’m not at all sure that Lithium is the answer. I’m thinking some sort of plastic, but whatever, I think looking for some bio-chemical thing is a good idea. Don’t get stuck on one solution. When trouble-shooting, you need to keep your mind open to any and all ideas. I also want to repeat two ‘facts’ (at least pieces of data) There seems to be both a gender and class split in obesity. Poor women are fat and poor men are thin, while rich women are thin and rich men are fat. I’m not sure what this is about but I see it everywhere, and of course you can find counter examples to all the cases. Colorado is special in a few ways: Highest state, so could be air pressure or solar radiation or something else that changes with height. (oh I was going to say cleanest water, but this goes along with height.) It also has the large population of white yuppies, who are all thin, from what I’ve observed… Is it a self selected group? Oh I really like the occupation and obesity links. And for a single data point will observe, that I’ve moved from an engineering job, sitting on my ass all day. To working as a prep cook (for much less money) and am very happy with my weight loss… no more man boobs. :^)
Oh, is part of obesity the jobs we are doing?
If you run an experiment with a weird diet (e.g. just potatoes) then you will need some other weird-diet control to adjust for malnutrition. What if eating only potatoes (or eliminating gluten from your diet, or all meat/animal products or whatever broad thing you might imagine) causes reduction in obesity because hey, it’s hard to get all your nutrients and you mildly starve yourself? Or it’s just an unusual diet so your subject struggles to plan meals and can no longer access easy takeout options?
Tens of thousands of college students from around the world go and spend semesters studying abroad. Maybe just ask them what their weight is before they leave and after they get back? Easy, low-cost causative data that could probably even be done retrospectively.
Yes! If anyone knows of a source we could use to do this retrospectively, we’d love to hear about it. If not, we’re thinking of trying to put together a project where we essentially ask a bunch of American expats the same thing.
This is just an anecdote but the most interesting weight-related experience I’ve ever had was while studying abroad in France 10 years ago! I lost a significant amount of weight after living there for just four months. I never weighed myself at the time but I look strikingly different in pictures from the end of my semester abroad so it must have been at least 15-20lbs (I am short). I ate a virtually unlimited amount of bread, cheese, meat, and dessert every day. I drank wine constantly. I even ate McDonald’s more often than I had back home because it was the only place open for late night drunk food. I did walk a lot while living in Paris but I went to college in a large US city and I didn’t own a car so that was not actually a change from my usual lifestyle. I did not deliberately exercise one single time while I was in France. The only major change seemed to be a reduction in the amount of highly processed foods I was consuming, plus presumably a change in environmental exposure to whatever was in the water supply. I gained the weight back pretty quickly after returning home and any time I’ve tried to re-create those circumstances by avoiding processed foods it has failed to produce the same results…it never once occurred to me that the real difference might have been the water!
You should definitely try to find some data from American students or expats!! I can’t have been the only one to have had this kind of experience. Time to go buy some water filters I guess…
Someone commenting above mentions the Pease Corps. Would that be a good fit?
Potentially! Do you know someone in the Peace Corps?
Unfortunately not. I’m in the UK. The last time I met someone in the Peace Corps was in Madagascar in 2008.
Arizona is discussing running desalinated water from the Pacific for its rapidly growing cities around Phoenix. Assuming the Lithium Hypothesis, is there a way we can A) make the result low-lithium, instead of high-lithium like certain Middle Eastern countries? B) provide evidence of the Lithium Hypothsis C) do it cheaply, so skeptics don’t derail this.
Desalinated water needs ions added back in, so it doesn’t leach them from our own bodies. a) What ions are the most important? b) What is the easiest (low-lithium) way to get them?
This gives us the possibility of a real win-win situation for Arizona, and evidence/proof for us. How can we do this better ?
Not sure (we’re looking into some of this) but a good start would be for them to at least test the water before and after for lithium.
Regarding identifying water ions to blame: One can buy regular deionized water system (reverse osmosis + ion-exchange resin filters), and introduce exact salts you want to see there or even have them as food supplement separately from water (in tablet/capsule form). Maybe going all the way to deionized water is too far, and plain reverse osmosis system is good enough – these are getting very cheap (<1000$).
Fortunately, lithium grease is getting out of favor, and often can be replaced to synthetic grease with PTFE particles with same or better performance: https://www.super-lube.com/multi-purpose-synthetic-grease-with-syncolon-ptfe
Interesting! 🙂
I’m interested in eating only potatoes for 30 days. It seems interesting because I’ve wondered about the environmental factors on weight for a couple of years and I like data. What information would be helpful to measure and track? Can any spices be used? I’m also not sure if cooking oil is allowed. For water, I use an NSF 53 filter, but I have never found a good way to measure what’s in my water to see if the claims are accurate. Is there a way to test lithium/chemical content in water? Does it matter if we think that food is the bigger source of the contaminant? Crosspost from https://www.reddit.com/r/spudbud/comments/ok13s8/rspudbud_lounge/
>but if you know of a good way to get in contact with the Amish in either of these locations Write them a letter. Seriously. They like letters. Send it to the bishop, or a random house, or even to the local postmaster with a request to deliver it to someone in the Amish community. They’re friendly. They’ll answer your questions about their water and food sources. You could even ask them to send you water samples.
[…] I was surprised to see that they are the ones running this, since they are best known for the “Chemical Hunger” series arguing that the obesity epidemic is largely driven by environmental contaminants like Lithium. The conclusion of that series noted: […]
Have you done any research on the best possible filtration systems to remove as much lithium as possible from drinking water? It seems like there’s a lot of options out there and hard to tell what is most effective.
Yes, we are currently working on it! Results will be out in a little while, we’re nearing the end stages
Oh awesome! Yeah I would also be happy to participate in a study if you all are close to that point that you mentioned in the article.
Did you look at bromine at all? It’s pervasive as a fire retardant and could easily be related to occupational exposure.
Interesting series.
Onset of puberty has also gone down in industrialized nations in the past 150 years or so, and I know some researchers have suggested exposure to pollutants as a possible cause. This may have a complicated relationship with weight – high weight causing earlier onset of puberty? earlier onset of puberty causing high weight? exposure to pollutants causing either? All of the above, simultaneously? Absolutely none of the above, and this comment is useless?
I’m not suggesting anything I just said is true – I haven’t done the full research yet, and there’s a lot of weird social issues about this (many people are weirded out by 10 year olds with boobs), which may help amplify scarier-sounding viewpoints. It’s just worth looking into.
Not sold on the potato diet or any of the other solutions, though. If even the Amish are affected, then individual choices aren’t going to be particularly impactful… Though potatoes are delicious!
Or, and here’s an idea: Just order blood testing for a group of individuals and see if lithium content correlates with obesity?
Seems far, far more simple than any of the above research designs. Otherwise, throwing filters on the water seems effective.
The moving people to other places is a very bad methodology because far too many confounding variables
Nice series though!
Regarding the meat diet, have you checked the fish diet from a production using aquaponics?
Besides some fish meet nutritional needs at a low climate impact, it will be interesting to see if this provides a good option free of PFOAs and lithium for those ethical meat eaters like myself?
https://www.nature.com/articles/s43247-022-00516-4 https://www.mdpi.com/2071-1050/7/4/4199
I have a mutation in uncoupling protein 4 (UCP4) which keeps my metabolism in emergency overdrive. As I write, I’m in shorts only while my wife is rugged up. I enjoyed my year working in North Greenland, even at -40! Recently I was in hospital for extensive surgery and IV fed until I had gained 10kg. Back at home I’m shedding 2 kg per month without doing anything different from before. A few more months should see my weight return to its previous normal.
That’s crazy!
Hope things go well.
What about fertilizers? There has been a massive increase in fertilizer, https://en.wikipedia.org/wiki/History_of_fertilizer , as well as changes in the types of fertilizers. For potassium, there has been a shift to potassium silicate fertilizers since the 1980s. Also, Regular K fertilizer is only 60% K, the rest contains plenty of other trace elements (Li?), that might not even be tracked. It might be worthwhile to find someone who knows about the fertilizer industry and changes that might have happened in the last century.
I remember reading that the mineral content in food is way off as many of the values were determined in the 50s before changes in fertilizer usage happened. K intake is supposedly much higher today than it was in the past, but we are still using old nutrition tables. Not sure whether this is true, and I don’t remember the source. Maybe someone else knows more?
Thank you for this great series.
A dissertation or thesis is a document submitted in support of candidature for a degree or professional qualification presenting the author's research and findings. (International Standard ISO 7144: Documentation — Presentation of theses and similar documents ).
For most universities in the U.S., dissertation is the term for the required submission for the PhD, and thesis refers only to the master's degree requirement.
Carnegie Mellon theses are now ONLINE and can be searched through the ProQuest database Dissertations & Theses @ Carnegie Mellon University that enables access to citations and abstracts of all dissertations and theses, as well as the full text in PDF format. Scroll down and select Dissertations & Theses, then do a regular search. Print versions are also available in the libraries' collection.
PRIMO , the Carnegie Mellon Library catalog, uses the term THESIS to denote both masters' theses and dissertations. However, the number of master's theses is limited. Within the libraries, theses are located in designated areas and are shelved in alphabetical order by the author's last name. The catalog treats theses and dissertations like books, and they can be borrowed as such. Theses may be in print, microfiche, or microform.
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Theses/dissertations from 2024 2024.
Effects of Diminazene Aceturate on Drosophila melanogaster : A Lipidomic Analysis , Gabriela Suarez
Introductory Chemistry Student Success: Evaluating Peer-Led Team Learning and Describing Sense of Belonging , Jessica D. Young
Explorations on Non-Covalent Interactions: From Supramolecules to Drug-Like Molecules , Zhanpeng Zhang
aPKCs role in Neuroblastoma cell signaling cascades and Implications of aPKCs inhibitors as potential therapeutics , Sloan Breedy
Protein Folding Kinetics Analysis Using Fluorescence Spectroscopy , Dhanya Dhananjayan
Affordances and Limitations of Molecular Representations in General and Organic Chemistry , Ayesha Farheen
Institutional and Individual Approaches to Change in Undergraduate STEM Education: Two Framework Analyses , Stephanie B. Feola
Applications in Opioid Analysis with FAIMS Through Control of Vapor Phase Solvent Modifiers , Nathan Grimes
Synthesis, Characterization, and Separation of Loaded Liposomes for Drug Delivery , Sandra Khalife
Supramolecular Architectures Generated by Self-assembly of Guanosine and Isoguanosine Derivatives , Mengjia Liu
Syntheses, Photophysics, & Application of Porphyrinic Metal-Organic Frameworks , Zachary L. Magnuson
Integration of Algae and Biomass Processes to Synthesize Renewable Bioproducts for the Circular Economy , Jessica Martin
Considerations for curricular reform in undergraduate chemistry: Cooperative adoption factors, modeling social influence, and focusing on specific populations , Jacob D. McAlpin
Chemical Analysis of Metabolites from Mangrove Endophytic Fungus , Sefat E Munjerin
Synthesis of Small Molecule Modulators of Non-Traditional Drug Targets , Jamie Nunziata
Conformational Dynamics and Free Energy Studies of DNA and Other Biomolecules , Paul B. Orndorff
Synthetic Studies of Potential New Ketogenic Molecules , Mohammad Nazmus Sakib
Coupling Chemical and Genomic Data of Marine Sediment-Associated Bacteria for Metabolite Profiling , Stephanie P. Suarez
Enhanced Methods in Forensic Mass Spectrometry for Targeted and Untargeted Drug Analysis , Dina M. Swanson
Investigation of Challenging Transformations in Gold Catalysis , Qi Tang
Diazirines and Oxaziridines as Nitrogen Transfer Reagents in Drug Discovery , Khalilia C. Tillett
Developing New Strategy toward Ruthenium and Gold Redox Catalysis , Chenhuan Wang
Gold-Catalyzed Diyne-ene Cyclization: Synthesis of Hetero Polyaromatic Hydrocarbons and 1,2-Dihydropyridines , Jingwen Wei
Development of Antiviral Peptidomimetics , Songyi Xue
Self-Assembly of Metallo-Supramolecules Based on Terpyridine and its Derivatives , Yu Yan
Synthesis and Antibacterial Testing of Novel Thiosulfonate Compounds , Lindsay I. Blume
Investigating a Potential STING Modulator , Jaret J. Crews
Development of Lipidated Antimicrobial Polycarbonates , Ruixuan Gao
Exploring the Structure and Activity of Metallo-Tetracyclines , Shahedul Islam
Large Area Projection Sintering of Semicrystalline Polymers and Part Analysis of the Printed Specimens , Taranjot Kaur
Interfacing Computational Techniques with Synthetic and Spectroscopic Methods for Research and Education , Nicole Annette Miller
An Investigation into the Protein Dynamics and Proton Transfer Mechanism of Class-A β-lactamase (CTX-Ms) by NMR Spectroscopy , Radwan Ebna Noor
Effects of acid hydrolyzed chitosan derivatives on MHV infection , Krishna Sharma
Metabolomic Analysis, Identification and Antimicrobial Assay of Two Mangrove Endophytes , Stephen Thompson
Advanced Analytical Method Development: from Highly-Enrolled Classroom to Data-Intensive Proteomics , Laxmi Sinduri Vuppala
Measuring and Improving Student Attitude in College-level Chemistry: A Novel Survey Methodology and Social-psychological Interventions , Ying Wang
Targeting the Side-Chain Convergence of α-Helical Hot Spots to Design Small-Molecule Mimetics Disrupting Protein-Protein Interaction , Zhen Wang
Bioactivity of Suberitenones A and B , Jared G. Waters
Developing Efficient Transition Metal Catalyzed C-C & C-X Bond Construction , Chiyu Wei
Chemical Investigation and Drug Discovery Potential of Terpenoid Secondary Metabolites from Three Deep-Sea Irish Soft Corals , Joshua Thomas Welsch
Measurement in Chemistry, Mathematics, and Physics Education: Student Explanations of Organic Chemistry Reaction Mechanisms and Instructional Practices in Introductory Courses , Brandon J. Yik
Study on New Reactivity of Vinyl Gold and Its Sequential Transformations , Teng Yuan
Study on New Strategy toward Gold(I/III) Redox Catalysis , Shuyao Zhang
Design, Synthesis and Testing of Bioactive Peptidomimetics , Sami Abdulkadir
Synthesis of Small Molecules for the Treatment of Infectious Diseases , Elena Bray
Social Constructivism in Chemistry Peer Leaders and Organic Chemistry Students , Aaron M. Clark
Synthesizing Laccol Based Polymers/Copolymers and Polyurethanes; Characterization and Their Applications , Imalka Marasinghe Arachchilage
The Photophysical Studies of Transition Metal Polyimines Encapsulated in Metal Organic Frameworks (MOF’s) , Jacob M. Mayers
Light Harvesting in Photoactive Guest-Based Metal-Organic Frameworks , Christopher R. McKeithan
Using Quantitative Methods to Investigate Student Attitudes Toward Chemistry: Women of Color Deserve the Spotlight , Guizella A. Rocabado Delgadillo
Simulations of H2 Sorption in Metal-Organic Frameworks , Shanelle Suepaul
Parallel Computation of Feynman Path Integrals and Many-Body Polarization with Application to Metal-Organic Materials , Brant H. Tudor
The Development of Bioactive Peptidomimetics Based on γ-AApeptides , Minghui Wang
Investigation of Immobilized Enzymes in Confined Environment of Mesoporous Host Matrices , Xiaoliang Wang
Novel Synthetic Ketogenic Compounds , Michael Scott Williams
Biosynthetic Gene Clusters, Microbiomes, and Secondary Metabolites in Cold Water Marine Organisms , Nicole Elizabeth Avalon
Differential Mobility Spectrometry-Mass spectrometry (DMS-MS) for Forensic and Nuclear-Forensic applications , Ifeoluwa Ayodeji
Conversion from Metal Oxide to MOF Thin Films as a Platform of Chemical Sensing , Meng Chen
Asking Why : Analyzing Students' Explanations of Organic Chemistry Reaction Mechanisms using Lexical Analysis and Predictive Logistic Regression Models , Amber J. Dood
Development of Next-Generation, Fast, Accurate, Transferable, and Polarizable Force-fields for Heterogenous Material Simulations , Adam E. Hogan
Breakthroughs in Obtaining QM/MM Free Energies , Phillip S. Hudson
New Synthetic Methodology Using Base-Assisted Diazonium Salts Activation and Gold Redox Catalysis , Abiola Azeez Jimoh
Development and Application of Computational Models for Biochemical Systems , Fiona L. Kearns
Analyzing the Retention of Knowledge Among General Chemistry Students , James T. Kingsepp
A Chemical Investigation of Three Antarctic Tunicates of the Genus Synoicum , Sofia Kokkaliari
Construction of Giant 2D and 3D Metallo-Supramolecules Based on Pyrylium Salts Chemistry , Yiming Li
Assessing Many-Body van der Waals Contributions in Model Sorption Environments , Matthew K. Mostrom
Advancing Equity Amongst General Chemistry Students with Variable Preparations in Mathematics , Vanessa R. Ralph
Sustainable Non-Noble Metal based Catalysts for High Performance Oxygen Electrocatalysis , Swetha Ramani
The Role of aPKCs and aPKC Inhibitors in Cell Proliferation and Invasion in Breast and Ovarian Cancer , Tracess B. Smalley
Development of Ultrasonic-based Ambient Desorption Ionization Mass Spectrometry , Linxia Song
Covalent Organic Frameworks as an Organic Scaffold for Heterogeneous Catalysis including C-H Activation , Harsh Vardhan
Optimization of a Digital Ion Trap to Perform Isotope Ratio Analysis of Xenon for Planetary Studies , Timothy Vazquez
Multifunctional Metal-Organic Frameworks (MOFs) For Applications in Sustainability , Gaurav Verma
Design, Synthesis of Axial Chiral Triazole , Jing Wang
The Development of AApeptides , Lulu Wei
Chemical Investigation of Floridian Mangrove Endophytes and Antarctic Marine Organisms , Bingjie Yang
An Insight into the Biological Functions, the Molecular Mechanism and the Nature of Interactions of a Set of Biologically Important Proteins. , Adam A. Aboalroub
Functional Porous Materials: Applications for Environmental Sustainability , Briana Amaris Aguila
Biomimetic Light Harvesting in Metalloporphyrins Encapsulated/Incorporated within Metal Organic Frameworks (MOFs). , Abdulaziz A. Alanazi
Design and Synthesis of Novel Agents for the Treatment of Tropical Diseases , Linda Corrinne Barbeto
Effect of Atypical protein kinase C inhibitor (DNDA) on Cell Proliferation and Migration of Lung Cancer Cells , Raja Reddy Bommareddy
The Activity and Structure of Cu2+ -Biomolecules in Disease and Disease Treatment , Darrell Cole Cerrato
Simulation and Software Development to Understand Interactions of Guest Molecules inPorous Materials , Douglas M. Franz
Construction of G-quadruplexes via Self-assembly: Enhanced Stability and Unique Properties , Ying He
The Role of Atypical Protein Kinase C in Colorectal Cancer Cells Carcinogenesis , S M Anisul Islam
Chemical Tools and Treatments for Neurological Disorders and Infectious Diseases , Andrea Lemus
Antarctic Deep Sea Coral and Tropical Fungal Endophyte: Novel Chemistry for Drug Discovery , Anne-Claire D. Limon
Constituent Partitioning Consensus Docking Models and Application in Drug Discovery , Rainer Metcalf
An Investigation into the Heterogeneity of Insect Arylalkylamine N -Acyltransferases , Brian G. O'Flynn
Evaluating the Evidence Base for Evidence-Based Instructional Practices in Chemistry through Meta-Analysis , Md Tawabur Rahman
Role of Oncogenic Protein Kinase C-iota in Melanoma Progression; A Study Based on Atypical Protein Kinase-C Inhibitors , Wishrawana Sarathi Bandara Ratnayake
Formulation to Application: Thermomechanical Characterization of Flexible Polyimides and The Improvement of Their Properties Via Chain Interaction , Alejandro Rivera Nicholls
The Chemical Ecology and Drug Discovery Potential of the Antarctic Red Alga Plocamium cartilagineum and the Antarctic Sponge Dendrilla membranosa , Andrew Jason Shilling
Synthesis, Discovery and Delivery of Therapeutic Natural Products and Analogs , Zachary P. Shultz
Development of α-AA peptides as Peptidomimetics for Antimicrobial Therapeutics and The Discovery of Nanostructures , Sylvia E. Singh
Self-Assembly of 2D and 3D Metallo-Supramolecules with Increasing Complexity , Bo Song
The Potential of Marine Microbes, Flora and Fauna in Drug Discovery , Santana Alexa Lavonia Thomas
Design, Synthesis, and Self-Assembly of Supramolecular Fractals Based on Terpyridine with Different Transition Metal Ions , Lei Wang
Fatty Acid Amides and Their Biosynthetic Enzymes Found in Insect Model Systems , Ryan L. Anderson
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Praise for A CHEMICAL HUNGER. I have never found scientific writing as gripping as I have found this series. My mouth just hangs agape as I scroll and the implications hit me. A friend who has studied obesity and its causes for years remarked, "This may well be the most comprehensive literature review ever undertaken on the topic, short of the ...
PERSONAL THEORIES OF HUNGER AND EATING by Sunaina Assanand B.Sc, University of British Columbia A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in THE FACULTY OF GRADUATE STUDIES Department of Psychology We accept this thesis as conforming (PHE UNIVERSITY OF BRITISH COLUMBIA October, 1996
The aim of this paper is to provide an account of how hunger motivates us to seek food and eat. It seems that the way that it feels to be hungry must play some role in it fulfilling this function. We propose that hunger is best viewed as a complex state involving both affective (viz., hedonic) and somatic constituents, as well as, crucially, changes in the way in which the hungry agent's ...
The shift from traditional diets to a diet characterised by higher consumption of sugars, fats, processed foods and animal-source foods is often termed the nutrition transition. Although research has focused on the health outcomes of this transition, there is an increasing interest in environmental impacts. Here we investigated the potential changes in impacts driven by the nutrition ...
3.3. Conference on poverty, hunger and sustainability. Achieving sustainability requires a human decision, assumed in a conscious, responsible way, guided by man's own rationality. It is necessary to establish a new model of development that is not limited to economic development, referenced to quantitative factors.
have in turned exacerbated situations of chronic hunger, acute food insecurity, and malnutrition [14]. In sum, the challenges currently faced by the global food system are characterized by synergies, trade-offs, and feed-back mechanisms [15]. Future challenges that will affect the achievement of SDG2 in the 7 years left to 2030 are likely to
Historical perspective on the role of hunger in food intake regulation. Hunger is an internal stimulus that is influenced by food intake. Eating in response to hunger has been regarded as homeostatic and an intermediary step in the process of food regulation. 5 Workers in psychiatry, psychology, and pediatrics have independently observed that many people eat in response to stimuli that are not ...
Now that u/slimemoldtimemold has finished A Chemical Hunger, I wanted to get everyone's takes on it. Part I - Mysteries. Part II - Current Theories of Obesity are Inadequate. ... but I don't accept their main thesis. They're right that our satiation setpoint/lipostat is being raised. But I think they're way off on the cause of that higher setpoint.
Agricultural system response to additional food production for hunger eradication. In the Baseline scenario, driven by economic development, the global average calorie and protein availabilities ...
Food preservation is also a challenge for the agro-food industry, politics, hunger, and malnutrition (Wu et al., 2014). Food preservation is a multi-process and involves a vast technology from ...
Abstract. Hunger has been a concern for generations and has continued to plague hundreds of millions of people around the world. Although many efforts have been devoted to reduce hunger, challenges such as growing competitions for natural resources, emerging climate changes and natural disasters, poverty, illiteracy, and diseases are posing threats to food security and intensifying the hunger ...
Hunger, for example, produces an aversive or uncomfortable psychological state. Organisms are thus motivated to eat as a way to reduce or eliminate the aversive quality of hunger (Betley et al., 2015; Sternson & Eiselt, 2017). Anecdotally, hunger pangs, irritability, and loss of focus during prolonged periods of fasting make this particular ...
Global Hunger and Malnutrition in the World Today. Hunger, Nutrition, and the Food Security Mandate. Chemistry's Influence on the Pillars of Food Security ... References. Citing Literature. The Chemical Element: Chemistry's Contribution to Our Global Future, First Edition. Related; Information; Close Figure Viewer. Return to Figure. Previous ...
Micronutrient deficiencies (MNDs), also known as hidden hunger, affect more than a quarter of the global population. Agronomic biofortification helps to increase the concentration of a target mineral in food crops and improve human mineral dietary intake. It is a means of providing nutrient-dense foods to a larger population, especially among rural resource-poor settings, providing that they ...
Introduction. Past centuries were marked by huge population losses resulting from hunger (1, 2).Nowadays, hunger still exists. According to "The State of Security and Nutrition in the World" report, published by FAO, IFAD, UNICEF, WFP, and WHO (), around 650 million people suffered from hunger in 2019, representing an increase of 43 million people compared to 2014 and, as a result of the ...
2.3 Hunger, Nutrition, and the Food Security Mandate 75 Hunger often goes hand in hand with food security. The concept of food security goes beyond caloric intake and addresses both hunger and undernutrition [14] . Reducing levels of hunger places the emphasis on the quantity of food, and refers to ensuring a minimum caloric intake is met.
Empirical research has aimed to substantiate the institution-food security nexus. However, institutional literature has largely overlooked the relationship between institutions and the sustainable development goal of zero hunger (SDG2). SDG2 is a multidimensional goal that extends beyond food security and requires comprehensive investigation. Therefore, this study explored the role of ...
Sustainable development was the global key issue and is in the interest of human beings. Among the 17 Sustainable Development Goals, goal 2 is significant in terms of eliminating hunger, achieving ...
The chemical prevented chilling injury in stored African eggplant as reported by Chepngeno, Owino, Kinyuru, and Nenguwo (Citation 2016). Also, tomatoes treated with Calcium Chloride were stored for 21 days without spoilage and indicated little physiochemical properties changes compared to untreated tomatoes in Nigeria, hence extended shelf life ...
Zero loss or waste of food. The Sustainable Development Goal to "End hunger, achieve food security and improved nutrition and promote sustainable agriculture" (SDG2) recognizes the inter linkages among supporting sustainable agriculture, empowering small farmers, promoting gender equality, ending rural poverty, ensuring healthy lifestyles ...
This is the end of A Chemical Hunger. We will still write more about obesity, and probably more about contamination, but this is the end of the series. ... with running in a sauna suit. I sweat more and lithium is water soluble so it fits the thesis The adrenaline also makes minor aches and pains disappear, making the experience less miserable ...
A dissertation or thesis is a document submitted in support of candidature for a degree or professional qualification presenting the author's research and findings. (International Standard ISO 7144: Documentation — Presentation of theses and similar documents ). For most universities in the U.S., dissertation is the term for the required ...
Theses/Dissertations from 2021. PDF. Design, Synthesis and Testing of Bioactive Peptidomimetics, Sami Abdulkadir. PDF. Synthesis of Small Molecules for the Treatment of Infectious Diseases, Elena Bray. PDF. Social Constructivism in Chemistry Peer Leaders and Organic Chemistry Students, Aaron M. Clark.