People with obesity commonly face a pervasive, resilient form of social stigma. They are often subject to discrimination in the workplace as well as in educational and healthcare settings. Research indicates that weight stigma can cause physical and psychological harm, and that affected individuals are less likely to receive adequate care. For these reasons, weight stigma damages health, undermines human and social rights, and is unacceptable in modern societies. To inform healthcare professionals, policymakers, and the public about this issue, a multidisciplinary group of international experts, including representatives of scientific organizations, reviewed available evidence on the causes and harms of weight stigma and, using a modified Delphi process, developed a joint consensus statement with recommendations to eliminate weight bias. Academic institutions, professional organizations, media, public-health authorities, and governments should encourage education about weight stigma to facilitate a new public narrative about obesity, coherent with modern scientific knowledge. Often perceived (without evidence) as lazy, gluttonous, lacking will power and self-discipline, individuals with overweight or obesity are vulnerable to stigma and discrimination in the workplace, education, healthcare settings, and society in general. Weight stigma can cause considerable harm to affected individuals, including physical and psychological consequences. The damaging impact of weight stigma, however, extends beyond harm to individuals. The prevailing view that obesity is a choice and that it can be entirely reversed by voluntary decisions to eat less and exercise more can exert negative influences on public health policies, access to treatments, and researchAlthough raising awareness of the negative consequences of weight stigma is important, awareness alone is not sufficient to eliminate the issue. Challenging and changing widespread, deep-rooted beliefs, longstanding preconceptions, and prevailing mindsets requires a new public narrative of obesity that is coherent with modern scientific knowledge. Given the pervasiveness of societal weight bias, this goal can only be achieved through the concerted efforts of a broad group of stakeholders, including healthcare providers (HCPs), researchers, the media, policymakers, and patients.To best inform HCPs, policymakers, and the public about stigma associated with obesity, a multi-disciplinary group of international experts, including representatives of ten scientific organizations Table , revieweA specific goal—representing novelty from previous related initiatives—was to address the gap between stigmatizing narratives around obesity and current scientific knowledge regarding mechanisms of body-weight regulation. The overarching objective was to gather a broad group of experts and scientific organizations to recognize the problem and, to our knowledge for the first time, ‘speak with one voice’ about this important issue.Here we report the conclusions of this exercise and resulting joint consensus statements, with a call to action for all stakeholders to take a pledge Box  to end wBox 1 Pledge to eliminate weight bias and stigma of obesityWe recognize thatIndividuals affected by overweight and obesity face a pervasive form of social stigma based on the typically unproven assumption that their body weight derives primarily from a lack self-discipline and personal responsibility.Such portrayal is inconsistent with current scientific evidence demonstrating that body-weight regulation is not entirely under volitional control, and that biological, genetic, and environmental factors critically contribute to obesity.Weight bias and stigma can result in discrimination, and undermine human rights, social rights, and the health of afflicted individuals.Weight stigma and discrimination cannot be tolerated in modern societies.We condemnThe use of stigmatizing language, images, attitudes, policies, and weight-based discrimination, wherever they occur.We pledgeTo treat individuals with overweight and obesity with dignity and respect.To refrain from using stereotypical language, images, and narratives that unfairly and inaccurately depict individuals with overweight and obesity as lazy, gluttonous, and lacking willpower or self-discipline.To encourage and support educational initiatives aimed at eradicating weight bias through dissemination of current knowledge of obesity and body-weight regulation.To encourage and support initiatives aimed at preventing weight discrimination in the workplace, education, and healthcare settings.The consensus-development conference was convened by F.R. (conference director), jointly with the following partner organizations: American Association of Clinical Endocrinologists, American Association for Metabolic and Bariatric Surgery, American Diabetes Association, Diabetes UK, European Association for the Study of Obesity, International Federation for the Surgery of Obesity and Metabolic Disorders, Obesity Action Coalition, Obesity Canada, The Obesity Society, and World Obesity Federation.The conference director (F.R.), co-chairs , and the partner organizations appointed a multidisciplinary expert panel Table  of 36 inThe 36 members of the expert panel, who constituted voting delegates for the entire consensus-development process, were selected primarily from academics with documented expertise about the topics of the conference and relevant publication records. Each of the ten partner societies used their own criteria to choose their representatives for the expert panel. Criteria included, expertise in weight stigma, obesity, and/or previous participation in relevant committees or initiatives related to weight stigma. One independent, non-voting moderator (O.B.C.) with previous experience in Delphi methodology administered questionnaires for the modified Delphi process and chaired the face-to-face meeting of voting delegates.A subgroup of expert panel members  contributed to a review of scientific publications in Medline on a broad set of topics related to weight bias, stigma, and discrimination. Specific, preset research questions for the review of evidence included: (i) prevalence of weight bias and stigma ; (ii) psychologically and physically harmful effects on individuals; (iii) impact on access to care and research, and evidence of workplace weight discrimination; (iv) biological mechanisms of weight regulation in physiology and disease; (v) clinical evidence of uptake and barriers to access of available treatments; and (vi) mechanisms of body-weight regulation and energy homeostasis. We also searched broader data sources for references to weight discrimination in current legislation.Members of the expert panel with specific expertise  were also tasked to independently conduct a short narrative review of current knowledge on one of the above research questions, according to their specific expertise.4). We also identified papers related to these subjects in Medline published between 2011 and 2019. To assess beliefs about obesity, type 2 diabetes, and related treatment options, we used research surveys and pools of opinion conducted among HCPs and/or the general public, and reported in Medline.Given the objectives of the consensus conference, the diversity of the subjects under consideration, and the variance in quality of evidence across disciplines, we considered a broad evidence base, including previously published systematic reviews, and various types of observational studies, experimental medicine, and translational studies in animals (weight-regulation mechanisms). For evidence about media portrayal of obesity, we used a 2010 review based on PsycINFO database searches using weight bias and stigmatization-related terms and phrases to identify journal articles published in English between 1994 and 2009 . We adapted the original Delphi method5 to the scopes and nature of this exercise. Unlike other Delphi studies, in which the first round consists mainly of open-ended questions, we used agree/disagree questions designed by a subgroup of members of the expert panel  for the first round.Based on results of the review of evidence, a subgroup of expert panel members with special expertise in specific subtopics developed questionnaires, including a set of statements and recommendations that were believed to summarize and reflect available evidence. These questionnaires were then circulated among the expert panel. An online Delphi-like method was used to measure the degree of consensus for the statements and recommendations by a web-based survey tool  round of Delphi was administered after the in-person meeting. All questions also contained a box for individual, non-compulsory comments.Each survey round was conducted over two weeks: one week for response acquisition (including e-mail reminders before the closing date), and one week for data analysis and preparation of the subsequent round. A personalized email message was sent to each respondent with a URL link to the survey.Response rate for both of the first two Delphi rounds was 34/36 expert panel members, and all 36 members responded in the third Delphi round.Supporting evidence and draft conclusions generated through the Delphi process were presented at the 4th World Congress on Interventional Therapies for Type 2 Diabetes 2019 . Proceedings were open to public comment by other experts in the field (WCITD2019 faculty members) and by the entire audience through opinion polls, using real-time electronic voting (Turning Technology software). On 10 April 10, 2019, voting delegates met face-to-face to review, amend, and vote on each consensus statement.After the meeting in New York, the document with conclusions reached by the experts underwent a final review and approval through a third round of Delphi process.The final consensus document, including the ‘Pledge to Eliminate Weight Bias and Stigma of Obesity’ Box , was theAll members of the expert panel, all partner organizations, and additional organizations listed in Box Box 2 Organizations that have endorsed the statement and/or accepted to take the pledge to eliminate weight stigma as of 26 February 2020Partner organizationsAmerican Association of Clinical Endocrinologists (AACE)American Association for Metabolic and Bariatric Surgery (ASMBS)American Diabetes Association (ADA)Diabetes UKEuropean Association for the Study of Obesity (EASO)International Federation for the Surgery of Obesity and metabolic Disorders (IFSO)Obesity Action Coalition (OAC)Obesity CanadaThe Obesity Society (TOS), USAWorld Obesity Federation (WOF)Other scientific and patient societiesAmerican Academy of Sleep Medicine (AASM)American Society for Nutrition (ASN)Association of British Clinical Diabetologists (ABCD)The Australian National Association of Clinical Obesity Services (NACOS)Australian and New Zealand Metabolic and Obesity Surgery Society (ANZMOSS)Austrian Society for Obesity and Metabolic SurgeryBrazilian Society for Bariatric and Metabolic Surgery (SBCBM)British Obesity and Metabolic Surgery Society (BOMSS)Canadian Association of Cardiovascular Prevention and Rehabilitation (CACPR)Canadian Association of Occupational Therapists (Association Canadienne des ergothérapeutes)The Canadian Society of Endocrinology and Metabolism (CSEM)CIHR-SPOR Chair in Innovative, Patient-Oriented, Behavioural Clinical TrialsChilean Society for Bariatric and Metabolic SurgeryColegio Mexicano de Cirurgia Para la Obesidad y Enfermedades MetabolicasCroatian Society of ObesityDietitians of CanadaDutch Society for Metabolic and Bariatric Surgery (DSMBS)The Endocrine Society (USA)European Coalition for People Living with Obesity (ECPO)French Clinical Research Network in Obesity (FORCE)French Society for Research and Care of Obesity (AFERO)French Society of Bariatric and Metabolic Surgery (SOFFCO-MM)Hellenic Medical Association for Obesity (HMAO)Hellenic Society for Bariatric and Metabolic SurgeryHellenic Society for the Study of Obesity, Metabolism and Eating DisordersHong Kong Association for the Study of ObesityHong Kong Obesity SocietyHong Kong Society for Metabolic and Bariatric SurgeryHungarian Society for the Study of ObesityInternational Behavioural Trials Network (IBTN)International Society for the Perioperative Care of the Obese Patient (ISPCOP)Irish Society for Clinical Nutrition and Metabolism (IrSPEN)The Israeli Association for the Study of ObesityItalian Obesity Society (SIO)Korean Society for the Study of Obesity (KSSO)Latin American Federation of Obesity (FLASO)The Lithuanian Society of Bariatric SurgeryMexican Society of ObesityNational Lipid Association (USA)Norwegian Society for the Surgery of ObesityObesity AustraliaObesity Care Advocacy Network (OCAN)Obesity CollectiveObesity Medicine Association (USA)Obesity Society of NigeriaThe Obesity Surgery Society India (OSSI)Obesity UKRomanian Federation of Diabetes, Nutrition, Metabolic diseasesThe Royal College of Physicians -RCP- (UK)Russian Society of Bariatric SurgeonsSociedad Argentina de Cirugia de la Obesidad Enfermedad Metabolica y Otras Relacionados con la ObesidadSociedad Argentina de Obesidad y Trastornos AlimentariosSociedad Española de Cirugía de la Obesidad y Enfermedades Metabólicas (SECO)Society of American Gastrointestinal and Endoscopic Surgeons (SAGES)Society of Behavioral Medicine (SBM)The Society for Surgery of the Alimentary Tract (SSAT)South African Society for Surgery Obesity and MetabolismUK Association for the Study of ObesityScientific and medical journalsThe Annals of SurgeryCell Metabolism (Cell Press)Cell Reports Medicine (Cell Press)Clinical ObesityThe Lancet Diabetes & EndocrinologyMed (Cell Press)Nature Research ObesityObesity ReviewsObesity Science and PracticeObesity SurgeryPediatric ObesitySurgery for Obesity and Related Diseases (SOARD)Trends in Endocrinology and Metabolism (Cell Press)Trends in Molecular Medicine (Cell Press)Academic institutions and hospitalsBaker Heart and Diabetes Institute. Melbourne, AustraliaThe Charles Perkins Institute, University of Sydney Geisinger Obesity Institute, Geisinger Health System, Danville, PA (USA)King’s College Hospital NHS Foundation Trust (UK)King’s College London (UK)London Bridge Hospital, London (UK)Pennington Biomedical Research Center (USA)Specialized Centers for Obesity Management (GCC-CSO) (France)St Vincent Private Hospital, Dublin (Ireland)Summer M. Redstone Center, Milken Institute School of Public Health, George Washington University (USA)Technische Universität Dresden; Faculty of Medicine Carl Gustav Carus (Germany)Transcampus of Technische Universität Dresden; Faculty of Medicine Carl Gustav Carus (Germany) and King’s College LondonUniversity College London Hospitals NHS Foundation Trust (UK)University of Connecticut Rudd Center for Food Policy & Obesity (USA)The Veneto Obesity Network Parliamentary groupsThe All-Party Parliamentary Group on Obesity (APPG): a group of cross-party members of the House of Commons and House of Lords campaigning for improved prevention and treatment of obesity (UK)A supermajority rule was used to define consensus. Consensus was considered to have been reached when > 67% of the experts agreed on a given topic. However, language was iteratively modified to maximize agreement, and the degree of consensus for each statement was graded according to the following scale: grade U was 100% agreement (unanimous); grade A was 90–99% agreement; grade B was 78–89% agreement; and grade C was 67–77% agreement. This grading scale is meant to indicate statements that reflect unanimous or near-unanimous opinions (grades U and A), strong agreement with little variance (grade B), or a consensus statement that reflects an averaging of more and possibly extremely diverse opinions (grade C). All statements included in this consensus document achieved either grades U or A, which we report for each statement.The first questionnaire asked 58 questions, including six on expert panel demographic information. During the three Delphi-like rounds and the in-person voting session, the expert panel eliminated five consensus questions that were deemed to be duplicative or redundant. Our iterative changes throughout the process yielded 47 final statements Tables  and 3, aIn this document, we use the terms ‘weight stigma’, ‘weight-based stereotypes’, or ‘weight bias’ to refer to biases against individuals with overweight and obesity, not underweight. We provide definitions in Box Box 3 Executive summary(Grade of consensus (GoC): U is unanimous; A is >90% consensus)Weight stigma is reinforced by misconceived ideas about body-weight regulation and lack of awareness of current scientific evidence. Weight stigma is unacceptable in modern societies, as it undermines human rights, social rights, and the health of afflicted individuals (GoC: A).Research indicates that weight stigma can cause significant harm to affected individuals. Individuals who experience it suffer from both physical and psychological consequences, and are less likely to seek and receive adequate care (GoC: U).Despite scientific evidence to the contrary, the prevailing view in society is that obesity is a choice that can be reversed by voluntary decisions to eat less and exercise more. These assumptions mislead public health policies, confuse messages in popular media, undermine access to evidence-based treatments, and compromise advances in research (GoC: A).For the reasons above, weight stigma represents a major obstacle in efforts to effectively prevent and treat obesity and type 2 diabetes. Tackling stigma is not only a matter of human rights and social justice, but also a way to advance prevention and treatment of these diseases (GoC: A).Academic institutions, professional organizations, media, public health authorities, and government should encourage education about weight stigma and facilitate a new public narrative of obesity, coherent with modern scientific knowledge (GoC: U).Box 4 DefinitionsWeight stigma refers to social devaluation and denigration of individuals because of their excess body weight, and can lead to negative attitudes, stereotypes, prejudice, and discrimination.Weight-based stereotypes include generalizations that individuals with overweight or obesity are lazy, gluttonous, lacking in willpower and self-discipline, incompetent, unmotivated to improve their health, non-compliant with medical treatment, and are personally to blame for their higher body weight.Weight discrimination refers to overt forms of weight-based prejudice and unfair treatment (biased behaviors) toward individuals with overweight or obesity.Weight bias internalization occurs when individuals engage in self-blame and self-directed weight stigma because of their weight. Internalization includes agreement with stereotypes and application of these stereotypes to oneself and self-devaluation6.Explicit weight bias refers to overt, consciously held negative attitudes that can be measured by self-report.Implicit weight bias consists of automatic, negative attributions and stereotypes existing outside of conscious awareness.8. Weight stigma has been documented in multiple societal domains, including the workplace, education, healthcare settings, and within families10. Stigma has persisted despite the markedly increased prevalence of obesity in recent decades. Among adults with obesity, the prevalence of weight discrimination is 19–42%, with higher rates among those with higher body-mass index (BMI), and among women compared with men13.Substantial research has demonstrated that weight stigma and discrimination are pervasive, global issues14. Internalized weight bias is present in individuals across diverse body-weight categories, but especially among individuals with higher BMI who are trying to lose weight14.Estimates from a 2018 study suggest that approximately 40–50% of US adults with overweight and obesity experience internalized weight bias, and about 20% of US adults experience this at high levels15. It has been estimated that over two thirds of images accompanying US media reports of obesity contain weight stigma, and experimental studies show that viewing these types of images leads to increased weight bias16.Evidence suggests that the media is a pervasive source of weight bias and can reinforce stigma through the use of inaccurate framing of obesity and inappropriate images, language, and terminology that attribute obesity entirely to personal responsibility18.Weight bias has been reported among HCPs in the United States and around the world, including among primary care providers, endocrinologists, cardiologists, nurses, dietitians, mental health professionals, medical trainees, and professionals engaged in research and clinical management of obesity21 and are at increased risk for relational, verbal, cyber, and physical victimization22. They are also more susceptible to developing mental health disorders, especially anxiety and depression, in addition to obesity, type 2 diabetes, and cardiovascular disease in later life23.Children with overweight and obesity are frequently subject to weight-based teasing and bullying at school. Compared with students of lower body weight, adolescents with overweight or obesity are significantly more likely to experience social isolation26, unhealthy eating and weight-control behaviors, such as binge eating and emotional overeating27. Experimental studies also show, paradoxically, that exposing individuals to weight stigma can lead to increased food intake, regardless of BMI28. Correlative and randomized-controlled studies also show that experience of weight stigma is linked with lower levels of physical activity, higher exercise avoidance31, consumption of unhealthy diets, and increased sedentary behaviors3, as well as increased obesity and weight gain over time32, and increased risk of transitioning from overweight to obesity in both adults and adolescents35.Weight stigma, rather than obesity itself, may be particularly harmful to mental health and is associated with depressive symptoms, higher anxiety levels, lower self-esteem, social isolation, perceived stress, substance use36, cortisol37, long-term cardio-metabolic risk38, and increased mortality39 compared with those who do not experienced weight discrimination.Individuals with overweight and obesity who experience weight discrimination show higher levels of circulating C-reactive protein17, and patients who report having experienced weight bias in the healthcare setting have poor treatment outcomes40 and might be more likely to avoid future care41. Obesity also adversely impacts age-appropriate cancer screening, which can lead to delays in breast, gynecological, and colorectal cancer detection42.Evidence suggests that physicians spend less time in appointments and provide less education about health to patients with obesity compared with thinner patients43. Negative influences on engagement with primary care were evaluated and ten themes were identified: contemptuous, patronizing, and disrespectful treatment, lack of training, ambivalence, attribution of all health issues to excess weight, assumptions about weight gain, barriers to health care utilization, expectation of differential health care treatment, low trust and poor communication, avoidance or delay of health services, and seeking medical advice from multiple HCPs.A thematic analysis of 21 studies examined the perceptions of weight bias and its impact on engagement with primary health care services44 could explain the low level of public support for coverage of anti-obesity interventions beyond diet and exercise45, regardless of their evidence base. For example, many public and private health insurers either do not provide coverage or have substantive limitations in the coverage of metabolic surgery, including fulfilment of a number of criteria for which there is limited or no clinical evidence47. These attitudes are in stark contrast with coverage of treatment for other chronic diseases  that are not conditional to similar restrictions, and for which use of similarly arbitrary coverage criteria would be socially indefensible and ethically objectionable.The widespread, but unproven, assumption that body weight is entirely controllable by lifestyle choices and that self-directed efforts can reverse even severe forms of obesity or type 2 diabetes49. It is not surprising that many hide their surgical status49.Metabolic surgery  provides a compelling example of how weight stigma can also extend to treatments for obesity. Compared with individuals who lose weight using diet and exercise alone, those who lose weight through metabolic surgery can be at risk of stronger stigma because they are stereotyped as being lazy and being less responsible for their weight loss51 of surgical interventions for obesity, only 0.1–2% of surgical candidates who qualify worldwide currently undergo such surgery52. A research survey in the United States showed that only 19.2% of responders supported insurance coverage of metabolic operations45.Despite evidence of efficacy and cost-effectiveness53. These examples also highlight the importance of initiatives aimed at combatting stigma and social exclusion 54.Historical examples of illnesses whose social construction incorporated moral judgments about the role of individuals’ actions in contracting the disease , demonstrate that stigma can interfere with public heath efforts to control epidemics55. Both observational and randomized-controlled studies show that these strategies can result in the opposite effect, and may instead induce exercise avoidance, consumption of unhealthy diets, and increased sedentary behaviors3, leading to poor metabolic health, increased weight gain57, and reduced quality of life57.Public health efforts to date have typically neglected stigma as a barrier in efforts to address obesity. By contrast, some public health strategies openly embrace stigmatization of individuals with obesity, based on the assumption that shame will motivate them to change behavior and achieve weight loss through a self-directed diet and increased physical exerciseSome public health messages and government-supported anti-obesity campaigns also characterize the merits of prevention of obesity as a preferable alternative to treatments for established obesity, such as pharmacotherapy or surgery, which are often considered more expensive. This is a misconception, as it frames prevention and treatment as being mutually exclusive, whereas these approaches should generally be directed toward two distinct populations, with different needs.58. Individuals with obesity have reported receiving lower starting salaries, can be ranked as less qualified, and can work longer hours than do thinner employees59. Persons with obesity can be perceived to be less suitable for employment and are less likely to be invited for an interview60, or, if employed, are perceived to be less successful compared with thinner peers61. Women with obesity are the especially unlikely to be hired62.Workplace discrimination against individuals with overweight and obesity is common in high-income countries63. Similarly, a US study reported that overweight women are more likely to work in lower-paying jobs and make less money compared with average-size women and all men64.A UK study of 119,669 individuals aged 37−73 years found a strong association between higher BMI and lower socioeconomic status, especially in women62. Although some US states have recently introduced a legislation that protects against height and weight discrimination65, the Civil Rights Act of 1964 does not identify weight as a protected characteristic, and only in some instances a condition of very high BMI can meet the definition of disability under a 2008 amendment of the Americans with Disabilities Act legislation66. This amendment, however, does not cover individuals who are not disabled, even though they can also be victims of weight discrimination.For the vast majority of individuals with obesity who experience discrimination in recruitment or the workplace, there is generally no protection under current legislations67 specifically prohibits discrimination on the grounds of age, disability, gender reassignment, marriage and civil partnership, pregnancy, maternity, race, ethnicity, religion or belief, sex, and sexual orientation—but not for obesity.Similarly, in 2014 the European Court of Justice ruled that being severely overweight could be considered a disability if this condition disrupts an employee’s ability to work. However, obesity per se is generally not specified as a disabling condition in current EU employment law; hence, most anti-discrimination laws require interpretation of whether a person with obesity has a disability. The UK Equality Act (2010)Research into obesity and diabetes is underfunded compared with other diseases, relative to their burden and costs on society. For example, the US National Institutes of Health’s projected budgets for cancer, HIV/AIDS, and digestive diseases are 5–10 times greater than the budget for obesity, despite that the latter affecting substantially more Americans.44. There are also several ways in which stigma can hinder support of research and scientific advances. For instance, oversimplified notion that obesity is caused by eating too much and exercising too little, implies that the causes of obesity and its epidemic are well-understood, and not complex. In this context, research designed to elucidate etiologic mechanisms of obesity may not be perceived as a priority. Furthermore, funding could be skewed toward projects that are anticipated to be effective , reducing support for investigation of novel methods of prevention and treatment or implementation of available evidence-based therapies .Among the 5,623 participants in a recent multi-national research survey (the ASK study), higher weight stigma was associated with lower prioritization of spending on obesity research71 shows that when individuals attribute the causes of obesity primarily to internal, controllable factors or personal choices, they exhibit higher weight bias, whereas acknowledging the complex causes of obesity  is associated with lower levels of weight bias and less blame. These findings suggest that the prevailing narrative of obesity in news coverage, public health campaigns, and political discourse—centered heavily on notions of personal responsibility73—can play an important part in the expression of weight stigma and reinforce weight-based stereotypes74.Evidence from several countries76.The absence of national laws that prohibit weight discrimination can also contribute to expression of weight stigma, as it communicates a societal message that weight stigma is acceptable and tolerable. However, evidence in North America, Europe, Australia, and Iceland suggests that there might be substantial public support to enact and pass legislation to prohibit weight discriminationThe notion that the causes of overweight and obesity depend on individuals’ faults, such as laziness and gluttony, stems from the assumption that body weight is entirely under volitional control. This assumption and many of its corollaries are now at odds with a definitive body of biological and clinical evidence developed over the last few decades.This equation is often oversimplified in the public narrative of obesity, and even by HCPs, as if the two variables  were dependent only on two factors, amount of food consumed and exercise performed, therefore implying that body weight is completely controllable by voluntary decisions to eat less and exercise more.77. Thus, even when individuals expend energy via exercise, except for elite athletes the overall contribution to energy expenditure is relatively small78.However, both variables of the equation depend on factors additional to just eating and exercising. For instance, energy intake depends on the amount of food consumed, but also on the amount of food-derived energy absorbed through the gastrointestinal tract, which in turn is influenced by multiple factors, such as digestive enzymes, bile acids, microbiota, gut hormones, and neural signals, none of which are under voluntary control. Similarly, energy output is not entirely accounted for by physical activity, which only contributes to ~30% of total daily energy expenditure. Metabolic rate accounts for 60–80% of total daily energy expenditure, with the thermic effect of feeding constituting approximately 10%79, and modifications of appetite signals that increase hunger and reduce satiety. These metabolic and biologic adaptations can persist long-term after losing weight and continue even after partial weight regain80.The existence of a powerful, precise homeostatic system that maintains body weight within a relatively narrow, individualized range is supported by scientific evidence. This regulatory system can counteract voluntary efforts to reduce body weight by activating potent compensatory biologic responses  that promote weight regain. Clinical evidence shows that a 10% weight loss elicits compensatory changes in energy expenditure81. Similar findings have been observed for adults82. Despite substantially higher levels of physical activity, total daily energy expenditure among hunter-gatherers in Africa’s savannahs today is largely similar to that of adults living in modern European or US cities, where obesity prevalence is high83. These findings contrast with conventional views that primarily attribute the cause of obesity to sedentary lifestyles and suggest that compensatory metabolic adaptations maintain total energy expenditure relatively constant among human populations and across various levels of physical activities.Although this concept might appear to be a straightforward conclusion, given common personal experiences of the fluctuations of body weight during periods of excess energy intake or sedentary lifestyle, the evidence supports a more nuanced situation. For example, in a Canadian study that used accelerometers to measure physical activity, girls with obesity took more steps per day than girls within the normal weight range84 and epigenetic factors85, foodborne factors86, sleep deprivation and circadian dysrhythmia87, psychological stress, endocrine disruptors, medications, and intrauterine and intergenerational effects. These factors do not require overeating or physical inactivity to explain excess weight90. A dominant role of genetic factors in obesity pathogenesis has also been demonstrated in studies comparing the concordance of body weight among fraternal versus identical twins91, for example, as well as studies of adults adopted as infants compared with their biological and adoptive parents92. Hence, overeating and reduced physical activity, when present, might be symptoms rather than the root causes of obesity93. Finally, the frequent failure of therapeutic and public-health strategies focused on the recommendation to ‘eat less and move more’ should call into question a causal role of voluntary overeating and sedentary lifestyle as primary causes of obesity.Additional evidence is now also available indicating other possible causes and contributors to obesity, including genetic94. Given the negative effects of obesity on quality of life, the well-known risks of serious complications and reduced life expectancy associated with it, it is a misconception to define obesity as a choice.Persons with obesity typically recognize obesity as a serious health problem, rather than a conscious choice. More than two thirds of 3,008 individuals with obesity surveyed in the ACTION Study considered obesity to be as or more serious than other health conditions, including high blood pressure, diabetes, and depressionLabeling obesity as a disease, risk factor, or condition has implications for treatment and policy development and can contribute to promoting or mitigating stigmatizing views toward affected individuals. An argument often used against labeling obesity a disease is that doing so communicates a societal message that individual responsibility is not relevant in obesity, thus reducing adherence to healthier lifestyles. Defining obesity as a disease, or not, however, should be based on objective medical and biological evidence, not sociologic implications.The criteria generally used for recognition of disease status are clearly fulfilled in many individuals with obesity as commonly defined, albeit not all. These criteria include specific signs or symptoms (such as increased adiposity), reduced quality of life, and/or increased risk of further illness, complications, and deviation from normal physiology—or well-characterized pathophysiology .95, many medical societies as well as the World Health Organization, the US Food and Drug Association, the US National Institutes of Health, and the Nagoya Declaration have now defined obesity as a disease or disease process.As reviewed in a statement from the World Obesity FederationAdmittedly, however, defining obesity as a disease, but measuring it only by BMI thresholds , risks labeling as ill some individuals who, despite possibly being at risk of future illness, have no current evidence of disease—for example, in cases where high BMI results from being particularly muscular or having short stature. This potential risk of misdiagnosis underscores the inadequacy of current diagnostic criteria for obesity, and the need to identify more meaningful clinical and biological criteria than just BMI to diagnose the disease.97. When fat mass decreases, the body responds with reduced resting energy expenditure80 and changes in signals that increase hunger and reduce satiety93 98. These compensatory metabolic and biologic adaptations promote weight regain and persist for as long as persons are in the reduced-energy state, even if they gain some weight back98.This assumption is also not supported by evidence. First, body weight and fat mass are known to be regulated by numerous physiological mechanisms, beyond voluntary food intake and physical exercise. A large body of clinical evidence has shown that voluntary attempts to eat less and exercise more render only modest effects on body weight in most individuals with severe obesity99. Such mechanisms include a paradoxical decrease in appetite and increase in metabolic rate, which change adaptively in the opposite directions to those following most non-surgical weight loss77. There are also favorable post-operative alterations in gastrointestinal hormones, bile-acid signaling, gut microbiota, absorption and utilization of glucose by the gut, modulations of gastrointestinal nutrient signaling that influence insulin sensitivity, and others100.Metabolic surgery is often referred to as an easy way out, based on assumptions that these interventions mechanically restrict food intake in a manner that individuals are not sufficiently disciplined to achieve on their own. However, evidence demonstrates that surgical interventions elicit numerous metabolic effects opposite to the compensatory physiologic responses normally triggered by diet-induced weight reduction, thereby promoting major, long-term weight lossIn this initiative, we sought to inform HCPs, policymakers, and the public about the prevalence, causes, and harmful consequences of weight stigma. A novel, specific goal not formulated in prior related initiatives was to address the gap between popular, stigmatizing narratives around obesity and current scientific knowledge regarding mechanisms of body-weight regulation. We found ample evidence of pervasive weight bias and stigma in many diverse domains of society, causing serious mental and physical harm to individuals with obesity. We met our primary objective of gathering a broad group of experts and scientific organizations to appraise the problem and, to our knowledge for the first time, speak with one voice against this important issue, pledging to do what we can to end it (pledge in Box There are several limitations to our work. For example, largely owing to the nature of relevant publications, we did not perform a formal systematic review with stringent criteria for levels of evidence. Our method of literature study was closer to a structured rapid review, performed over approximately 6 months, and it only included English-language papers. Also, although our expert panel comprised representatives from ten nations spanning five continents, it was heavily weighted toward individuals from the United States and other high-income countries. Much of the evidence base is also derived from these regions. It is important to note, however, that our final report has been formally endorsed by over 100 organizations at the time of publication Box , includiWeight stigma and discrimination are pervasive and cause significant harm to affected individuals. The widespread narrative of obesity in the media, in public health campaigns, in political discourse, and even in the scientific literature attributing the cause of obesity primarily to personal responsibility has an important role in the expression of societal weight stigma, and reinforces weight-based stereotypes. Weight stigma can mislead clinical decisions, and public health messages, and could promote unproductive allocation of limited research resources. Weight bias and stigma can result in discrimination, and undermine human rights, social rights, and the health of afflicted individuals. Explaining the gap between scientific evidence, and a conventional narrative of obesity built around unproven assumptions and misconceptions might help to reduce weight bias, and its harmful effects. A concerted effort of all stakeholders is required to promote educational, regulatory, and legal initiatives designed to prevent weight stigma and discrimination.


Chronic hypoxic and hypercapnic respiratory failure and obstructive sleep apnoea (OSA) are chronic diseases associated with decreased quality of life and increased mortality. The rationale behind the set up the retrospective nationwide DISCOVERY cohort was to study several questions including disease course and risk factors for incident disease, impaired quality of life, hospitalisation risk and mortality in patients with chronic respiratory failure with long-term oxygen therapy (LTOT), long-term mechanical ventilation (LTMV) and obstructive sleep apnoea (OSA) on treatment with continuous positive airway pressure (CPAP).Data from the national quality registry for respiratory insufficiency and sleep apnoea (Swedevox) and a population-based control group from Statistics Sweden were merged with governmental registries, the Swedish Cancer Registry, the Swedish Cause of Death Registry, the Swedish Drug registry, the Swedish National Patient Registry and the Swedish Dental Health Registry and with national quality registries for diabetes, rheumatic diseases , stroke (RiksStroke), heart failure (RiksSvikt), acute heart infarction care (SwedeHeart) and intensive care (SIR) and with socioeconomic data from Statistics Sweden (SCB).2), 8111 with LTMV since 1996 , 65 809 with OSA on CPAP since 2010  and 145 224 persons in a population-based control group from same time span up to March 2018 .The cohort comprises 25 804 unique patients with LTOT since 1987  26.6±6.5 kg/mIn patients with chronic respiratory failure and sleep apnoea important questions regarding comorbidity burden, hospitalisation rate, mortality and treatment outcomes are still unexplored to a large extent. The DISCOVERY cohort will provide unique opportunities by its size and comprehensiveness to fill this clinically relevant gap of knowledge. This nationwide population-based cohort is unique internationally due to its size, its high nationwide coverage, high degree of data completeness and high validity of data.No patients are lost to follow-up due to cross-linkage with mandatory national registries.To assure a high level of completeness in the Swedevox registry, the number of requested variables to be reported into the Swedevox registry has been deliberately limited, and the data thus become less detailed.Difficulties adjusting for confounders is a limitation in all registry-based studies.This is counterbalanced, at least in part, by the size of the cohort and by cross-linkage with other registries.Chronic hypoxic and hypercapnic respiratory failure and obstructive sleep apnoea (OSA) are associated with decreased quality of life and increased mortality.In chronic hypoxic respiratory failure, two-thirds of the patients die of cardiovascular disease (CVD) and cancer.In OSA, several pertinent questions are still unresolved. Observational studies have shown that treatment with CPAP is associated with increased survival.The European Sleep Apnoea database (ESADA) study reported an increased cancer prevalence in women with OSA but not in men, but the reported cancer prevalence was low and no subanalysis for cancer subtypes was performed.Sweden offers extensive possibilities for population-based outcome studies. There are 107 national quality registries containing individualised data concerning specific diseases or medical interventions set up to monitor and to improve the quality of healthcare. The Swedish National Board of Health and Welfare  manages a series of registries relating to healthcare and social services. The Swedish system of personal identity numbers (PIN)DISCOVERY is an acronym for Course of DISease in patients reported to the Swedish CPAP Oxygen and VEntilator RegistrY. In patients with chronic respiratory failure on LTOT and LTMV and with sleep apnoea on CPAP, important questions regarding comorbidity burden, hospitalisation rate, mortality and treatment outcomes are still unexplored to a large extent. The DISCOVERY cohort will provide unique opportunities by its size and comprehensiveness to fill this clinically relevant gap of knowledge.The DISCOVERY cohort is a nation-wide population-based cohort, including patients in the Swedevox National Quality Registry for respiratory failure, with respiratory failure starting LTOT (n=25 797), LTMV , patients with OSA starting CPAP (n=65 803) and a population-based control cohort (n=1 45 224).Patients with LTOT and LTMV have been included prospectively in the Swedevox registry since 1 January 1987 and 1 January 1996, respectively, with 100% geographical coverage and a population-based coverage of approximately 85%–90%,A population-based control group was identified from Statistics Sweden’s Living Conditions Surveys (ULF/SILC), which consists of a random selection of approximately 6000 people between the ages of 16 and 74 years in Sweden, made every year since 1987. The control cohort is from the same time span as the study population. Data from the ULF/SILC regarding age, gender, length, weight, smoking, housing type, living area, civil status and year of study are collected for the study.33Within the Swedevox registry, there were patients reported to more than one registry arm and also patients that had initiated a specific treatment more than once . In the control group, 45 246 observations were from persons reported to ULF/SLIC on more than one occasion. The last registration in each arm was used in the subsequent analysis. In the control group, 1722 persons also reported to one of the arms in the Swedevox registry were excluded. The final DISCOVERY cohort consists of 25 804 unique patients with LTOT, 8111 with LTMV, 65 809 with OSA on treatment with CPAP and 145 224 persons in the control group. Characteristics of the different arms in the DISCOVERY cohort are presented in The Swedevox registry and the control cohorts were merged on individual level with governmental registries with almost complete coverage: the Swedish Cancer Registry, the Swedish Cause of Death Registry, the Swedish Drug registry, the Swedish National Patient Registry (NPR) and the Swedish Dental Health Registry. In addition, data were merged with the national quality registries for diabetes (NDR), for rheumatic diseases ), for stroke (RiksStroke), for heart failure (HF) (RiksSvikt), for acute heart infarction care (SwedeHeart) and for intensive care (SIR). Finally, socioeconomic data from Statistics Sweden (SCB) were added .The national healthcare registries approved consignment of data. The stepwise process of cross-linkage is described in The Swedish National Diabetes Registry was launched in 1996 by the Swedish Society for Diabetology for the purpose of promoting evidence-based development of diabetes care. The registry includes risk factors, diabetic complications and medications. More than 90% of all adult patients with drug-treated diabetes type 1 and 2 in Sweden are registered.34–36The SRQ was started in 1995 by the Swedish Rheumatology Society to improve the healthcare and treatment of patients with rheumatoid arthritis (RA), but over time it has been expanded to cover several other rheumatic diseases including ankylosing spondylitis and psoriatic arthritis, myositis, systemic lupus erythematosus and additional conditions.38The coverage for patients with RA on biologic treatment is 95% and patients with ankylosing spondylitis, psoriatic arthritis and other spondyloarthropathies on biologic treatment is 86%.40RiksStroke was established in 1994 and has complete geographical coverage43Patients with HF, both in primary care and in hospital-based care, are reported to the RiksSvikt registry. The registry was founded in 2000 and implemented throughout Sweden in 2003.45By 2014, 63 519 unique patients have been included into the registry and there are now approximately 10 000 registrations added every year. Coverage of prevalent HF in the inpatient setting is 54% but much lower in primary care.46Since 1995, patients with acute coronary disease are reported to the national quality registry for acute infarction care (RIKS-HIA), a registry which since 2008 is a part of the Swedeheart registry. All coronary care units in Sweden register patients suffering from myocardial infarction and patients undergoing coronary angiography/angioplasty or heart surgery for any indication. Approximately 80 000 patients are reported annually.47–49Since 2005, Swedish intensive care units have reported patient data to Swedish Intensive Care Registry. The geographical coverage is 92%. This registry contains data on diagnoses, interventions, inpatient care, patient-reported outcome measures (PROM)The Swedish Cancer Registry was founded in 1958 and has an estimated completeness of 96% for all diagnosed cancers in Sweden with approximately 60 000 new malignant cancer cases are reported annually.53Since 1952 all deaths of persons in Sweden are registered in the Swedish Cause of Death Registry.55The Swedish Prescribed Drug Registry contains data on all dispensed prescriptions in outpatient care in Sweden since 1 July 2005.58Data about comorbidities, hospitalisations, operations and other procedures were derived from the National Patient Registry (NPR) for inpatient and outpatient care.60The Swedish Dental Health Registry was initiated in 1 July 2008 and contains individual data on dental healthcare to the whole adult population of Sweden.62The Swedish Longitudinal Integrated Database for Health Insurance and Labour Market Studies (LISA) contains annual data on county and place of residence, civil status, migration, country of birth, foreign background and nationality, length of education, occupation and employment variables, incomes and allowances, sick leave and incomes. LISA covers the adult Swedish population aged ≥16 years registered on 31 December each year since 1990. The completeness is estimated at 95%.Patients in the DISCOVERY cohort were continuously followed-up when being registered in other national quality registries or in the mandatory governmental registries due to death, when receiving a cancer diagnosis, when being prescribed medications, when being hospitalised, when receiving outpatient care at a public hospital and when receiving publicly financed dental care. There is generally follow-up data available until 12 March 2018. For the DISCOVERY cohort, there is longitudinal data available up to 31 December 2017 in the national cancer and the national cause of death registries and up to 31 December 2018 in the National Patient, National Prescribed Drugs and the National Dental Health registries. The frequency the study population appeared in the other registries is presented in Several studies from the Swedevox registry have previously been published and a complete publication list can be found in the Swedevox annual report 2018.12 32 70The LTMV group is very inhomogeneous with differences in survival and mean age between groups as a consequence . A previ71Thus, the DISCOVERY database will be used to study factors associated with incidence, progression, worse quality of life, hospitalisation, comorbidity and mortality in patients with chronic respiratory failure with LTOT, LTMV and OSA on CPAP treatment. The detailed planning is ongoing.This nationwide population-based cohort is unique internationally due to its size, its high nationwide coverage and its high degree of data completeness. A recent validation study support that Swedevox data are valid, of high quality and suitable for use in research.Requests for specific research projects and collaborative work are encouraged and can be addressed to the corresponding author.A representative from the patient organisation ‘the Swedish Heart and Lung Association’ is a member of the Swedevox Registry’s steering committee and has been involved in study design discussions. Results from future studies from the DISCOVERY cohort will be submitted for publication in peer-reviewed journals and presented at relevant conferences.


Food retailers, manufacturers, and distributors exert powerful influence on our food choices through decisions about stocking, pricing, marketing, and promotional practices . Such prThis Special Issue was borne out of a convening held in January 2020, sponsored by Center for Science in the Public Interest, The Food Trust, the Johns Hopkins Bloomberg School of Public Health, and Healthy Eating Research (HER), a national program of the Robert Wood Johnson Foundation. Recognizing that sustainable interventions require cross-sector and multidisciplinary collaboration, meeting attendees included food industry representatives, non-profit organizations, and researchers spanning a wide range of disciplines relevant to healthy food retail. The objective of the convening was to develop a national healthy retail research agenda, which aimed to identify effective retail interventions to support nutritious food choices, with an intentional focus on reducing disparities in food marketing and access. This Special Issue includes research commissioned for or resulting from the meeting, including one paper describing the research agenda, one communication, three empirical papers, and four reviews. Papers are organized into four sections: (1) the retail food environment and industry practices; (2) consumer food shopping patterns; (3) effectiveness of retail interventions to support healthy eating; and (4) the future of food retail research.Relationships between the retail food environment and consumer food and beverage choices are complex. Prior conceptual frameworks do not fully capture factors influencing where retailers locate, which products they choose to sell, how products are marketed or merchandised in the store, and how these decisions interact with other factors to influence consumer selection. To fill this gap, Hecht and colleagues  review iThe categories of promotional practices catalogued by Hecht and colleagues are incorporated into two new conceptual frameworks describing the influence of the retail food environment on consumer behaviors. The Retail Food Environment and Customer Interaction Model, put forth by Winkler and colleagues , conceptThis Special Issue includes research designed to better understand consumer food shopping patterns, particularly across understudied populations. Original research by Lacko and colleagues  updates The new empirical data presented by Lacko and colleagues is complemented by a systematic review by Singleton and colleagues  that wasThis Special Issue includes two systematic reviews examining effects of interventions to support healthy eating. Karpyn and colleagues  review 6A systematic review written by Moran and colleagues  complemeThe final full-length paper in the series by Hecht and colleagues  describeThe series is then completed with a timely communication from Leone and colleagues  that refTaken together, these papers advance our understanding of the complex relationships between the retail food environment, dietary behaviors, and the public’s health. This Special Issue highlights substantial gaps in our knowledge of consumer food purchasing patterns and impacts of healthy retail interventions by race, ethnicity, class and geography—research that is critical for addressing health inequities. The culminating healthy retail research agenda synthesizes this work and details a path forward, describing partnerships, data sources, research methods, and priority questions for advancing the field.


Food scientists need to work together with agriculturists, nutritionists, civil society, and governments to develop an integrative approach to feed a growing population sustainably. Current attention on food sustainability mainly concentrates on production agriculture and on nutrition, health, and well-being. Food processing, the necessary conversion of raw materials to edible, functional, and culturally acceptable food products, is an important link between production and consumption within the food value chain. Without increased attention to the role of food processing for a maintainable food supply, we are unlikely to succeed in addressing the mounting challenges in delivering sustainable diets for all people. The objective is to draw on multidisciplinary insights to demonstrate why food processing is integral to a future food supply. We aim to exemplify the importance of essential relevant sustainability indicators and impact assessment for developing informed strategies to feed the world within planetary boundaries. We provide a brief outlook on sustainable food sources, review food processing, and recommend future directions. We highlight the challenges and suggest strategies for improving the sustainability of food systems, to hopefully provide a catalyst for considering implementable initiatives for improving food and nutrition security. Nachhaltigkeit, meaning “lastingness,” emerged  or from meat (18%), dairy (10%), fish and shellfish (6%), and other animal products (9%) . The tot3, have higher percentage of body weight (up to 80% protein), and are edible and are more digestible than regular livestock  than traditional sources , offers the opportunity for valorization of biomass commonly treated as waste (cladodes), and has the potential for cultivation in arid and semiarid areas . Aquaticivestock . Insectsivestock .Approximately a third of the food produced is wasted . An emerHistorical developments in food processing have been centered on thermal processing and utilization of natural renewable resources. These include the use of low or high temperature for processing and preservation, with William Cullen introducing the first artificial cold process in the 1750s to present-day energy-efficient refrigeration and freezing systems. Similarly, in early 1800s was the first use of heat for food preservation by Nicholas Appert in response to Napoleon Bonaparte requirements to feed the French army, which has now progressed to modern energy-efficient thermal-processing equipment.Table 1(A) and potential sources for (B) food processing.Existing natural elements used The application of nanotechnology in food has potential to contribute to sustainable food chains. Nanotechnology encompasses understanding and control of matter on the nanometer scale, which enables alteration of the material properties of products to suit purpose and improve functionality. Nanotechnological innovations in the food and agriculture include encapsulated ingredients that provide protection of sensitive bioactives  and increased nutrient delivery, nanomaterials for controlled delivery of anti-microbials, smart sensors for improved food safety management, and nanocomposites for improving barrier properties of packaging materials –56. InnoThe exploitation of by-products of insect protein production including “gut” enzymes and microbiota seems worthwhile exploring, as well as the plant-soil microorganisms' systems, plant root cultures for secondary metabolite generation, or processing-related improvements of probiotics and prebiotics . FinallyThe integration of sustainability should be an additional and new dimension of food security . A multiThere are challenges for ensuring sustainable food systems for the increasing urban population and transformation in rural–urban linkages . This reAnother challenge is to obtain consensus on sustainability indicators for food processors. There is wide variation in reporting by food processors . There sThere must be greater attention paid to the development of business models using an interdisciplinary approach to develop convincing business cases for multiple stakeholders . While tAn evidence-based nutrition and health-related contribution of processed food are urgently needed . There aIntegration of the entire food value chain, which will require concerted interdisciplinary activities , is a siFuture resource-efficient food processing will also need to concentrate and take advantage of the existing biosystems from microorganism–host  as well as food–microbiome–human interactions . This neSignificant challenges exist for ensuring sustainability across food chains given the complex nature and variability in global food systems. A key requirement is the establishment of harmonized sustainability indicators that objectivity provide relevant measures for sustainable systems. The design of sustainable processes and engineering systems should embrace the use of emerging processes, which reduces water and energy use.We recommend the following strategies for improving the sustainability of food systems:Resource management—manage food and water scarcity; reduce waste, retain, and recover/reuse nutrients within the food chain; generate a worldwide compendium of indigenous and traditional raw materials, processing, and preparation methods.http://etp.ciaa.eu).Sustainable processing and improved food delivery—develop sustainable, efficient, and responsible food packaging, storage, transportation, and delivery systems; exploit alternative energy sources and biosystem-based production/processing; build sustainable practices into food preparation and processing; create flexible, scalable, and appropriate urban food processing, preparation, delivery, and consumption models; develop food processes based on PAN  principles for consumers , practical processes, and ecological dynamics for informing policy at various scales . It mighAll authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.


University students have high rates of health risk behaviors and psychological distress. This study explores patterns of health behaviors among a sample of Australian university students, and determines whether patterns of health behaviors are associated with psychological distress and demographic characteristics. Cross-sectional data from the University of Newcastle Student Healthy Lifestyle Survey 2019 were analyzed. Fruit and vegetable intake, sugar-sweetened beverage intake, physical activity, sitting time, smoking, alcohol intake, drug use, sleep and psychological distress were assessed. Latent class analysis (LCA) was used to identify patterns of health risk behaviors, and latent class regression to explore associations between psychological distress and demographic characteristics with health behavior classes. Analysis included 1965 students . Three patterns of health behaviors were identified: healthier (48.6%), moderate (40.2%) and unhealthy (11.2%) lifestyle classes. Students in the moderate and unhealthy lifestyle classes had higher odds of moderate (OR 1.43 and 2.37) and high/very high psychological distress risk (OR 2.71 and 11.69). Students in the unhealthy and moderate lifestyle classes had a higher odds of being male, younger, enrolled in transition to university and English language courses, Aboriginal or Torres Strait Islander descent and to report some financial difficulty. Study findings may be used to inform the design of mental health interventions for university students that target key health risk behaviors. Attending university is an important life transition for individuals, often occurring during emerging adulthood. Successful transition to university, and academic performance can be impacted by mental health. The World Health Organization (WHO) World Mental Health International College Student (WMH-ICS) initiative generates global epidemiological data on the prevalence of mental disorders among university students . Data frn = 75 colleges in the US, and 50,307 students) demonstrate high rates of health risk behaviors, including 68% consuming less than the recommended servings of vegetables per day, 56% not meeting physical activity guidelines, 44% sleeping less than the recommendations and 14% being current smokers [n = 39 colleges, n = 30,093 students) via latent class analysis (LCA) identified four classes among the five behaviors explored , with evidence of clustering of the health risk behaviors  within three of the classes, which accounted for 85% of the participants [University enrolment results in individuals facing new life experiences  while also managing academic factors . These factors are known to impact student’s mental health, but they also influence students’ engagement with health risk behaviors, such as poor diet quality, physical inactivity, smoking and higher risk alcohol consumption. There is a high prevalence of health risk behaviors reported among university students globally. For example, the most recent National College Health Assessment (NCHA) surveys , health risk behaviors at enrolment predicted positive depression screen  and anxiety depression screen  at the end of the first year of university [n = 837) [Previous research has linked individual health risk factors to university student mental health, including both mental health disorders and psychological distress ,11,12. Fiversity . There hiversity ,13,14,15Therefore, the aims of this study were to explore patterns of health behaviors  among a sample of Australian university students, and determine whether the patterns of health behaviors are associated with psychological distress and demographic characteristics. Such knowledge is imperative to the design of effective mental health promotion interventions for university students, particularly the health behavior focus, and highest-risk student groups.www.surveymonkey.com.au) and allowed access on a single device to prevent multiple entries by the same individual. The survey in total included 78 questions, which were displayed across 35 pages, with an average completion time of 15 min 41 s. Questions in the survey relating to drug use, sexual health and mental health were optional to complete, where all other questions required a response. The survey was open from 9 September to 5 October 2019. These dates were chosen as they are mid-way through the second semester, and therefore do not coincide with major exams. The conduct and reporting of this work adheres to STROBE guidelines [This study is a secondary analysis of data collected from the 2019 University of Newcastle (UON) Student Healthy Lifestyle Survey (SHLS). The SHLS is an online, cross-sectional survey aiming to identify lifestyle health risk factors , mental health and overweight and obesity prevalence in UON students. The methods of the regular survey have been previously published . The suridelines . The surn = 34,924) were invited to participate. The UON is a large university with cohorts based at the main campus located in Newcastle, New South Wales (NSW), Australia and additional smaller campuses across NSW (n = 4), and Singapore (n = 1), and online/distance cohorts. Students were invited to participate via an email sent to their UON email account on the first day of the survey, with two reminder/thank you emails sent to all students on the 17 and 26 of September 2019. University of Newcastle teaching staff received an email requesting that they promote the survey in class or via the online learning management system using the recruitment materials provided by the researchers. The survey was also advertised via UON student social media accounts, digital signage across all campuses and posters at the main campus. On survey completion, participants could choose to enter a prize draw to win one of five gift vouchers valued at $AU100. All participants gave informed consent prior to completing the survey. Approval for this study was obtained from the UON Human Research Ethics Committee (H-2015-0459).All students enrolled at the UON as of 9 September 2019 ” to “6 or more serves”) . One serPhysical activity was assessed via the Active Australia survey , includiSitting time was assessed using questions from the NSW Adult Population Health Survey, including average time spent sitting on a weekend day and on a weekday . AverageParticipants’ tobacco smoking status was assessed using one question from the NSW Adult Population Health Survey . Those iParticipants’ risk from alcohol intake was assessed using the Alcohol Use Disorders Identification Test (AUDIT) . The AUDParticipants’ risk from drug use was assessed using the Drug Use Disorders Identification Test (DUDIT) . The DUDSleep was evaluated using one question from the National Center for Chronic Disease Prevention and Health Promotion . ParticiPsychological distress risk was assessed using the Kessler Psychological Distress Scale (K-10) questionnaire . The 10-Demographic data collected included age, gender , Aboriginal or Torres Strait Islander (ATSI) background, marital status, living situation, sources of financial support, income, ability to manage on their income, and hours of paid work, and were consistent with/adapted from the national census. Age was categorized as 17–24 years, 25–35 years, and >35 years of age. Living situation was categorized as living at their parents’ home, in their own home or other . Ability to manage on their income was categorized as managing (it is easy and it is not too bad), some difficulty (it is difficult sometimes) or difficult . Student-related data collected included type of degree  and whether they were a domestic or international student.n = 189), psychological distress (n = 56) or ATSI background (n = 3), due to implausible data on physical activity (n = 91), or where gender was specified as non-binary or another gender identity (n = 22). Non-binary and another gender identity participants were excluded as the sample size was too small for comparison. Health risk behaviors, psychological distress and demographic characteristics are described as number and percentage for categorical variables and mean and SD for continuous variables. Latent class analysis (LCA) was used to identify patterns of health risk behaviors. All variables were entered as categorical variables for interpretability of the findings as indicated in Data were analyzed using STATA statistical software version 14.2. In total, 2819 students consented and were eligible to participate in the survey, 2326 completed the full survey and 1965 are included for this analysis . For thiThe majority of participants were female (71%), aged 17–24 years (61%) and born in Australia (83%) . Most coLCA analysis identified a three-class model as the best fit and most interpretable model . The heaOdds of class membership for psychological distress risk and each demographic characteristic compared with the healthier lifestyle class as the referent group are presented in Relative to those in the healthier lifestyle class, those in the unhealthy lifestyle class had a higher odds of being male, younger age, enrolled in enabling  and English language courses, and reporting some financial difficulty. Relative to those in the healthier lifestyle class, those in the moderate lifestyle class had a higher odds of having an Aboriginal or Torres Strait Islander background, and being an international student.This study provides new evidence into the links between dietary intake, physical activity and sedentary behavior, sleep, smoking, and alcohol and other drug use among an Australian university student sample. Three patterns of health behaviors were identified. The highest proportion (48.6%) of students fit into the healthier lifestyle class, characterized by higher probability of healthy dietary intake, being physically active, less time spent sitting, meeting sleep duration recommendations and having low-risk drug and alcohol use. The students in the moderate (40.2%) and unhealthy lifestyle (11.2%) classes had a higher likelihood of psychological distress, and were more likely to be male, younger, enrolled in transition to university and English language courses, Aboriginal or Torres Strait Islander descent and to report some financial difficulty. The findings provide further evidence of the need to do more to support university students’ to improve their health risk behaviors, as a potential strategy to manage the high levels of psychological distress. This may be of particular importance for the students in the “unhealthy lifestyle class” characterized by hazardous drug and alcohol use, smoking, higher sugar-sweetened beverage consumption and low fruit and vegetable intake, who were found to have almost 12 times greater odds of high/very high psychological distress risk.Generally, the pattern of health behaviors in this study was that the healthier lifestyle class was characterized by healthier diet, being physically active, and obtaining sufficient sleep, and low risk substance use behaviors , where the unhealthy lifestyle class was the opposite and the moderate class was mixed across most behaviors. These patterns show some similarity to studies of university student samples internationally ,13,29, wThe odds of having high or very high risk of psychological distress were substantially higher for those students in the unhealthy and moderate lifestyle classes relative to the healthier lifestyle class. That is, the combinations of poorer dietary intake, less physical activity, more sedentary time, poor sleep, and higher risk substance use were associated with higher risk of psychological distress. This is consistent with the findings of other studies in university student and broader adult populations, where higher risk or less healthy patterns of various health behaviors have shown to be associated with poorer mental health outcomes, including more frequent experiences of mental distress, and higher levels of psychological distress and stress ,15,30. TThe key demographics found to be associated with health behavior classes in this study were gender, age, financial situation, Indigenous background, degree level and being an international versus domestic student. Of note, students experiencing financial difficulty, those enrolled in enabling  courses, and those of Aboriginal or Torres Strait Islander background had higher odds of being in the unhealthy and moderate lifestyle classes compared with the healthier lifestyle class. These findings are consistent with the known health disparities between individuals of higher versus lower advantage, in terms of socio-economic status and educational attainment, and between Indigenous and non-Indigenous persons . While tThe main strengths of this study include the robust analysis methods used to identify patterns of health behaviors, the broad range of health behaviors assessed, the use of validated tools for assessing health behaviors and psychological distress, and the relatively large sample size. Further, this study includes an Australian sample of university students, where there is limited research available relative to other western countries. The main limitation is the cross-sectional study design as this does not allow for determination of causality or exploration of the direction of associations. Due to the cross-sectional design, the data are limited in terms of the timeframe of reference for the reported health behaviors and does not capture effects of seasonality. Further limitations to acknowledge include the use of self-report data, as well as the representativeness of the sample which may limit the generalizability of the findings. Bias in self-reporting data may have occurred, for example responding in line with social norms, which may have impacted the findings. To minimize this, validated tools were used, the survey was anonymous and questions relating to sensitive topic areas, including mental health, were optional for participants to complete. The study sample was a small proportion of the total student body (6.7%). However, this is comparable with/higher than other recent online surveys in university students using convenience sampling and is reflective of the challenges of recruitment in university student and young adult samples ,41,42. TThe findings from this analysis support the need for further research to better understand the influence of health risk behaviors on student’s mental health. Future research should involve longitudinal studies to track changes in university students’ health risk behaviors and mental health throughout their enrolment and determine the associations of individual health behaviors with mental health over time, along with clustering of health risk behaviors and directionality of effects with mental health. This information could then be used to determine the opportune timing of interventions to support students’ health and wellbeing, as well as tailoring and targeting to the highest-risk student groups. Further, it is important that sustained systems of tracking health behaviors and outcomes are implemented in this population group particularly in light of the current COVID-19 pandemic, where day-to-day lives, including education, are changing, which is inevitably influencing health behaviors and health outcomes ,45, the Limited research has explored how clusters of health risk behaviors are linked with indicators of mental health among university students. This latent class analysis identified three classes of health behaviors among a sample of Australian university students, and found that those in the moderate and unhealthy lifestyle classes had a higher likelihood of psychological distress than those in the healthy lifestyle class. The findings evidence the need for more action to support university students’ to improve their health risk behaviors, as a potential strategy to manage the high levels of psychological distress.


The use of model systems that are capable of robust, spontaneous retina regeneration has allowed for the identification of genetic pathways and components that are required for retina regeneration. Complemented by mouse models in which retina regeneration can be induced after forced expression of key factors, altered chromatin accessibility, or inhibition of kinase/signaling cascades, a clearer picture of the key regulatory events that control retina regeneration is emerging. In all cases, Müller glia (MG) serve as an adult retinal stem cell that must be reprogrammed to allow for regeneration, with the end goal being to understand why regenerative pathways are blocked in mammals, but spontaneous in other vertebrates such as zebrafish. miRNAs have emerged as key gene regulatory molecules that control both development and regeneration in vertebrates. Here, we focus on a small subset of miRNAs that control MG reprogramming during retina regeneration and have the potential to serve as therapeutic targets for treatment of visual disorders and damage. In mammals and humans, the extent of spontaneous repair after retina injury or disease is either non-existent or extremely limited  and develops into a three-layered structure consisting of seven main types of cells and numerous other cell types identified by single cell RNAseq , inner nuclear layer (INL), and ganglion cell layer (GCL). The INL and ONL are separated by a thin synaptic layer called the outer plexiform layer (OPL), and the INL and GCL are separated by a thick synaptic layer called the inner plexiform layer (IPL). The ONL contains rod and cone photoreceptors. The INL contains three types of interneurons: bipolar cells (BCs), horizontal cells (HCs), and amacrine cells (ACs). Ganglion cells (GCs) populate the GCL, collect information from BCs, and send signals to the brain for higher order visual processing. In addition to these neuronal cell types, Müller glia (MG) constitute the main glial cell type spanning all three layers of the retina.The unique behavior and placement of MG following damage led to hypotheses that they play an integral role in retina regeneration. Multiple lines of evidence, largely from zebrafish, strongly support MG as the source of retina progenitors after damage. First, new retinal progenitor cells (RPCs) formed in the INL migrate along MG processes to the ONL  RNAs that post-transcriptionally regulate gene expression   can convert fibroblasts into a neuronal cell type fate  is the Wnt inhibitor Dickkopf 3 (Dkk3)  gene , insulin-like growth factor (IGF), Hedgehog, Notch, and the mammalian target of rapamycin (mTOR) pathways, as well as being a key regulator of pax6a in mice  expression, which increases mitochondrial membrane potential  that block miRNA function for therapeutic purposes. The challenges for such experiments are at least three-fold: (1) how to target injected miRNAs to MG; (2) whether single injections will be sufficient to induce a regenerative response; and (3), avoidance of off-target effects if high concentrations are required. While direct injections of miRNAs or antagomirs are possible, the discovery that extracellular vesicles (EVs) can be used to deliver therapeutic cargo opens an exciting possibility for cell-specific delivery (Mead and Tomarev, GK made the figures. All authors contributed to the writing.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.


As a member of the 3D Team, the bilingual, bicultural Community Health Educator (CHE) addresses needs expressed by study participants related to social determinants of health. Clinical triggers generated by nurse practitioners (NPs) that lead to CHE referral include: social isolation and loneliness; lack of transportation access; lack of resources to sustain nutritional adequacy, purchase medications, and purchase assistive devices; and cultural and linguistic barriers that lead to lack of knowledge about community resources. To date, 50% of study participants randomized to receive 3D Team care have triggered referral to the CHE. In this presentation, the team CHE will provide details on the frequency of different needs expressed by study participants, how she utilizes an ever-growing community resource directory, and specific types of information and guidance she provides to address their expressed needs. Case studies will help illustrate ways in which CHE services have successfully provided assistance to study participants.


Background: Drug interactions represent a major issue in clinical settings, especially for critically ill patients such as those with cardiovascular disease (CVD) who require cardiothoracic surgery (CTS) and receive a high number of different medications. Methods: A cross-sectional study aimed at evaluating the exposure and clinical significance of drug–drug (DDIs) and drug–dietary supplement interactions (DDSIs) in patients admitted for CTS in the University Hospital of Crete Greece. DDIs were evaluated regarding underlying pharmacological mechanisms upon admission, preoperation, postoperation, and discharge from CTS clinic. Additionally, upon admission, the use of dietary supplements (DSs) and if patients had informed their treating physician that they were using these were recorded with subsequent analysis of potential DDSIs with prescribed medications. Results: The study employed 76 patients who were admitted for CTS and accepted to participate. Overall, 166 unique DDIs were identified, with 32% of them being related to pharmacokinetic (PK) processes and the rest (68%) were related to possible alterations of pharmacodynamic (PD) action. CVD medications and drugs for central nervous system disorders were the most frequently interacting medications. In total, 12% of the identified DDIs were of serious clinical significance. The frequency of PK-DDIs was higher during admission and discharge, whereas PD-DDIs were mainly recorded during pre- and postoperation periods. Regarding DS usage, 60% of patients were using DSs and perceived them as safe, and the majority had not informed their treating physician of this or sought out medical advice. Analysis of medical records showed 30 potential combinations with prescribed medications that could lead in DDSIs due to modulation of PK or PD processes, and grapefruit juice consumption was involved in 38% of them. Conclusions: An increased burden of DDIs and DDSIs was identified mostly upon admission for patients in CTS clinics in Greece. Healthcare providers, especially prescribing physicians in Greece, should always take into consideration the possibility of DDIs and the likely use of DS products by patients to promote their well-being; this should only be undertaken after receiving medical advice and an evidenced-based evaluation. Drug–drug interactions (DDIs) can modulate the clinical outcome of coadministered drugs by altering their main or secondary pharmacological actions and ultimately lead to the development of adverse drug reactions (ADRs). This may result in impaired health in patient, complicating the treatment of a pathological condition and possible prolonging hospitalization ,2. The uPatients admitted for cardiothoracic surgery (CTS) represent a special patient population group that receives an increased number of medications and needs advanced healthcare provision during hospitalization . Most ofPrior to any CTS procedure, these patients are often advised to made essential changes in their dietary and lifestyle habits to improve their well-being and to prevent disease progression . In thisUntil today, several studies regarding CVD patients and DDIs or DDSIs focused on potential ADRs related to administered medication during hospitalization or with DS consumption. It is estimated that approximately 3–5% of DDIs among CVD patients in hospital units are serious while 60–70% of them are of major clinical significance and most of them can result in ADRs ,18. RegaThe cross-sectional study was conducted over a 6-month period in the division of Cardiothoracic Surgery Division of University Hospital of Heraklion in Greece. The study was carried out following the rules of the Declaration of Helsinki of 1975, as revised in 2013, and it was complied with the General Data Protection Regulation (GDPR) and approved by the hospital’s ethics committee. Moreover, the study followed guidelines for reporting observational studies  and evidence-based medicine approaches . The STRAll patients admitted in the CTS clinic were given an information brochure of the study and included to participate after they singed the informed consent form. All participants were adults (18–65 years old) or elderly (>65 years old) who were admitted for CTS in the clinic, administered more than two medications, and hospitalized for more than two days. Patients on short-term hospitalization or with <2 drugs, those who did not understand the terms of participation and consent, and cases of readmission causally related to previous cardiac surgery were reasons for patients to be excluded from the study. For each participant, upon admission and during hospitalization, the primary investigator completed a questionnaire regarding demographics, comorbidities, clinical data during hospitalization, and medication regimens at four discrete time points: (i) admission (patients enter the clinic), (ii) preoperation (patients enter the preoperation phase to be prepared for surgery), (iii) postoperation (patients are monitored to recover from surgery), (iv) discharge (patients are stable and exit the clinic). Moreover, for the positive answers of DS usage, the participants state the DSs that they are using, if they had informed their treating physician (YES/NO), if they use it as part of self-medication habits (YES/NO), if medical advice was requested (or provided) prior to use (YES/NO), and the reasons of using DSs (recording the statement verbatim). All data were collected and analyzed anonymously, and no interventions were made regarding healthcare provision during hospitalization.“Serious–Use alternative”, “Use with caution-Monitor” and “Moderate-Minor”. A similar analysis regarding the clinical significance from drug interactions with dietary supplements was followed [Drug combinations that may result in DDIs and DDSIs were detected using available online drug interaction checker tools (Medscape and Drugs.com). The categorization and the clinical significance of the identified interactions were further assessed based on the availability of scientific data from the literature which provide sufficient evidence of the underlying biological mechanisms and the outcomes. The evaluation was based on different levels of evidence that describe significance, such as theoretical mechanisms, in silico/ in vitro/in vivo data, clinical studies, expert opinion reviews, meta-analysis, information encompassed in the drug’s label, summary of product characteristics (SmPC) or in reports from regulation between interacting drugs or drugs from the same pharmacological group. The clinical significance of the interactions in this study is represented as followed ,25.n = 65) or urgent  surgery, met the study criteria and were accepted for participation in the study. n = 38, 50%)  of the total 80 patients admitted in the CTS unit for scheduled  A. The av38, 50%) B. Regard38, 50%) .+), sodium (Na+), international normalized ratio (INR), and blood glucose seem to follow the expected clinical course with small variations from the anticipated values, which are related to the surgery and modification in medications  are presented in Based on the number of medications administered, there was an important number of DDIs during all time points A. OveralThe involved pharmacological mechanisms of the identified DDIs were related to either PK (32%) or PD (68%) processes A. Regardn = 46). As for food products, 56% of the patients stated that they drink mixed commercial fruit juices while 90% of the cohort drink coffee daily. The consumption of DSs refers to aloe or senna laxative products , green tea , sage/salvia/chamomile , grapefruit juice , and fish oil/vitamins . From the 46 patients, only seven  stated that they used them after medical advice or informed their treating physician. The medical advice regarded consumption of tea and sage (n = 3), vitamin products (n = 3), and fish oil (n = 1). Most of the DS users stated that they did not seek medical advice  or inform their treating physician  and they use them as part of their self-medication habits  , “it is food-beneficial and not harmless” , and “not questioned for DS use”   A. The re11, 28%) B.The analysis of data regarding prescribed medications, as recorded upon admission, along with DS usage revealed 30 unique cases of potential DDSIs . The 30 combinations and their significances are presented in DDIs represent a serious clinical issue in healthcare provision, especially for critically ill patients. Assessment of potential DDIs not only reveals prescription errors but also assists the healthcare provision by informing medical teams on what precautions should be made for administration of specific medications. Polypharmacy, comorbidities, DS usage, hospitalization period, and patients’ health statuses are contributing factors for the appearance of DDIs which could lead to ADRs, prolonged hospitalization, increased healthcare costs, and reduction in patients’ quality of life . This stIn this study, a positive trend was observed between number of medications and prevalence of DDIs . Althoug2+ blockers, such as amlodipine, can interact with statins or metformin, which could lead to increased PD action, whereas combinations of amlodipine with β-blockers or ARBs  should be used only under risk–benefit analysis and potassium level monitoring [2+ blockers, statins, or NSAIDs as well as clinically relevant DDIs of coumarin analogues with drugs that inhibit CYP-mediated metabolism  or NSAIDs and SSRIs that can increase risk of bleeding have been well described and these combinations should always be used with caution and monitored [The analysis of the medication regimens during hospitalization revealed that most of the medications related to DDIs were drugs for CVD such as metoprolol, amiodarone, ranolazine, perindopril, furosemide; drugs acting on CNS such as alprazolam, bromazepam, haloperidol, morphine, and quetiapine; antithrombotic agents such as acenocoumarol, enoxaparin, and clopidogrel; antilipidemic agents and especially statins; insulin, PPIs such as esonemprazole; antibiotics such as ciprofloxacin or ceftriaxone; aspirin. Our results indicate that these drugs should be administered under constant consideration of potential and clinically significant DDIs. Antiarrhythmics such as amiodarone, a known CYP2D6 inhibitor, were mostly involved in PK-DDIs with β-blockers, statins, and analgesics such as tramadol and morphine. Diuretics  were related to potential PD-DDIs and synergistic or antagonistic effects regarding potassium levels that should be monitored . Drugs fnitoring . Concernonitored ,37. The onitored .In this work, the issue of drug interactions was also approached from another point of view that of the potential interaction of prescribed medications with dietary products (DDSIs). Many of the patients in our cohort were habitually using DSs without considering the risks of potential interactions with their medications and were neglecting informing their treating physicians , Table 4The evaluation of patients’ clinical statuses is important to help minimize and prevent any adverse events such as DDIs . The labIn hospital settings, patient safety issues are a major concern. To minimize the risk of medication errors, tools such as electronic decision support systems that allow the prospective evaluation of interactions should be used in clinical practice when making decisions to optimize patient safety . Some ofThe strengths of this work as an observational study are that it tried to record and analyze all types of interactions for a high-risk group such as CTS patients, and approached the prevalence of DDIs and DDSIs using more than one drug interaction checker as well as available literature. The use of additional checkers for analysis of DDIs retracts previous reported limitations regarding the number of DDIs that could be recognized. Moreover, available checkers provide different methods of DDI characterization or different clinical severity classifications, complicating the comparison among studies ,17,18,22The present cross-sectional study explored the prevalence of DDIs among patients during hospitalization in a CTS hospital clinic in Greece and their habitually use of DS products that could lead to DDSIs. Our results indicate a high exposure of DDIs and DDSIs for patients that undergo CTS surgery in Greece, with 12% of the identified DDIs to be of serious clinical significance, and that alternative combinations should be considered. PK-DDIs mostly occurred during admission and discharge, indicating that most of the identified clinically significant DDIs could be avoided by simple improvements in prescription practices. Patients were often exposed to PD-DDIs during hospitalization, but under a risk–benefit approach from the treating physicians. Drug categories of CVD , CNS , antithrombotic agents , PPIs, and antibiotics often contributed as interacting combinations with other drugs, posing the need for evidence-based evaluations prior to any administration of these drugs. In addition, the study recorded the use of DSs among patients to analyze their daily dietary habits for promoting well-being. The main finding is the regular consumption of grapefruit juice among CTS patients. Although there are numerous data revealing potential conflicts of DS use with medications (such as statins or other CVD drugs), it was also evident that patients did not consider it necessary to inform their treating physicians under the belief that the natural origin of DSs makes them safe to use. The need for improvements regarding prescription practices, continuous vigilance, and constant evaluation for better clinical considerations as well as, communication among patients and healthcare providers is evident from our results. Especially for Greece, healthcare providers and treating physicians should be aware of potential and clinically significant DDIs during hospitalization; additionally, they should often query their patients regarding the potential use of DSs in order for clinically significant drug interactions and related ADRs to be avoided.


Age-Friendly Public Health is an emerging concept and practice aiming to equip the public health infrastructure to more readily address aging issues. In 2019, the Washington State Department of Health (WA DOH), began operationalizing Age-Friendly Public Health within the agency. Work entailed bringing together a cross-agency workgroup on healthy aging and drawing on expertise from agency programs including environmental health, immunizations, falls prevention, emergency preparedness, chronic disease and many others. The workgroup conducted an internal assessment of ‘touch points’ regarding aging work within WA DOH. The Life Course Perspective was used as a theoretical framework to categorize current efforts, and existing research on social determinants of health in aging were used to identify potential areas for future impact. In addition, the Healthy Brain Initiative (HBI) Road Map for public health was used to identify current and potential approaches to addressing Alzheimer’s Disease and Related Dementias. This workgroup inventoried WA DOH’s current efforts that align with the HBI agenda, and identified 88 opportunities to promote healthy aging and brain health. This work contributes to the emerging field of Age-Friendly Public Health by demonstrating strengths and challenges of advancing healthy aging through state public health agencies.