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26 Conclusion: Obesity and its prevention in the 21st century

• Published: September 2010
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The case for a preventative approach to the obesity epidemic is compelling. Obesity poses what is arguably one of the most significant threats to population health that is currently faced. The data presented in this book highlight just how common obesity has become in children and in adults across the globe, and how it impacts disproportionately on the poor. This chapter presents a summary of the discussions in the preceding chapters.

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Practical Approaches to Treating Obesity: Patient and Healthcare Professional Perspectives

Donal o’shea.

1 University College Dublin, Dublin, Ireland

Scott Kahan

2 George Washington University, Washington, D.C, USA

Cathy Breen

3 St Columcille’s Hospital, Dublin, Ireland

Obesity is a chronic and treatable disease carrying risk for numerous health complications, including cardiovascular disease, respiratory disease, type 2 diabetes mellitus and certain cancers. While there is a great need to address the topic in clinical practice, healthcare professionals (HCPs) often struggle to initiate conversations about weight. In this paper, guidance on how to raise and address the subject of weight with individuals is provided from an HCP and patient perspective using the 5As framework. This model facilitates advising individuals on the benefits of weight loss and supports them to develop achievable and sustainable weight management plans. With obesity rates still rising across the globe, it is imperative that more HCPs become skilled in raising and addressing the issue.

Key Summary Points

Digital Features

This article is published with digital features, including a summary slide, to facilitate understanding of the article. To view digital features for this article go to 10.6084/m9.figshare.14407043

Introduction

The need to address obesity is now undisputed and pressing. Characterised by excess weight gain [ 1 ], obesity is a modern-day health epidemic that requires long-term, individualised care. While the majority of healthcare professionals (HCPs) claim they are confident discussing weight and tailoring management strategies for individuals with overweight and obesity, they also believe that the proportion of patients successfully achieving weight loss goals remains comparatively low [ 2 ]. This disconnect between HCPs’ assessment of their competency and their effectiveness in counselling individuals to achieve treatment goals represents a central issue in supporting more effective obesity management. It is essential, therefore, that practitioners consider the complex drivers of weight gain across the physiological, environmental and psychosocial spectra. This article provides guidance on how to raise and address weight, and is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.

Obesity is a Treatable Disease

With its numerous compounding factors, obesity is increasingly being recognised as a treatable disease, and health bodies across the globe have begun reclassifying it in acknowledgement [ 3 – 5 ]. Multiple physiological mechanisms interplay to add to the challenge of losing and maintaining weight loss.

Weight loss is associated with marked and persistent alterations in the levels of hormones involved in the regulation of satiety [ 6 ]. For example, weight loss in individuals with obesity has been shown to produce sustained reductions in levels of leptin, glucagon-like peptide-1, peptide YY, cholecystokinin, insulin and increased ghrelin [ 7 , 8 ], all of which reduce satiety and may contribute to weight regain. Furthermore, weight loss is associated with changes in neural function and activity, with increased activity in areas associated with processing of food-related stimuli and decreased activity in areas associated with restraining responses to food [ 9 ]. Studies also show that people on weight loss diets experience more food cravings than non-dieters, and that these are stronger and more difficult to resist [ 10 ]. Finally, diet-induced weight loss is associated with reduction in total and resting energy expenditure [ 11 ].

As the biological mechanisms of weight regulation become more clearly understood, it is important that HCPs acknowledge the processes underlying weight regain to enable development of management strategies that not only promote initial weight loss but also maintain that weight change in the long term.

Another key aspect that categorises obesity as a disease is its association with numerous metabolic, mechanical and psychological comorbidities. Excess weight has now been linked to more than 200 diseases [ 12 ], and weight loss is associated with clinically meaningful improvements in many of these. Table ​ Table1 1 summarises some of the common complications of obesity. The importance of treating obesity is further illustrated by accumulating evidence of its association with more severe COVID-19 infection, leading to increased risk of hospitalization, admission to intensive care units and death [ 13 ].

Summary of common obesity-related complications

CVD cardiovascular disease, GORD gastroesophageal reflux disorder, NAFLD non-alcoholic fatty liver disease, OA osteoarthritis, OSA obstructive sleep apnoea, T2D type 2 diabetes.

The 5 As Approach to Obesity Counselling

There are a number of frameworks aimed at helping HCPs better support people to manage weight; the ‘4 Ms’ was designed to help time-restricted physicians assess for obesity [ 24 ], and the newer ‘ABCDEF’ approach provides guidance on taking a weight history, previous weight loss attempts, evaluation for weight-related morbidities, treatment and long-term follow-up [ 25 ]. The 5 As model was developed by the Canadian Obesity Network to aid the delivery of meaningful weight management consultations, and has proven effective in improving physician–patient communication, patient motivation [ 26 , 27 ], and HCPs’ confidence in discussing weight loss interventions [ 28 ]. This paper shares practical solutions to common challenges faced by HCPs, using the flow of the 5 As framework (ask, assess, advise, agree, assist) and incorporating the views and experiences of a person living with obesity.

Ask Permission: Starting the Conversation

Individuals living with obesity experience body weight stigma in multiple aspects of their life, which can lead to feelings of guilt, shame and self-criticism [ 29 ]. With this in mind, it is important to be aware of behaviours that could trigger such negative feelings and result in a reluctance to discuss weight.

As part of the management approach, HCPs should consider why the individual has visited the clinic; it may not always be appropriate to begin with a conversation about weight. Addressing the presenting complaint first can help to reduce feelings of stigmatisation and set up the consultation for success.

While it can be difficult to raise the topic of weight, there are several strategies that can help. Being non-judgmental and empathetic positively opens up the conversation [ 30 ], reduces fear of stigma and criticism and helps to form a strong patient–provider partnership.

Evidence and experience suggest that a vital step in the intervention process is asking for permission to raise the sensitive issue of weight [ 28 ]. A respectful example of such a question might be ‘Can we talk about your weight today?’ However, language and wording may vary depending on individual HCP preference, language and culture. If the individual wishes to talk about weight, the next step is to acknowledge and advise that weight management is challenging and express willingness to provide ongoing support. However, it is important to note that not all people will feel ready to discuss their weight. Instead of persisting, the optimal course of action may be to ask whether they would be open to revisiting the topic in the future; again, putting the decision into the hands of the individual.

An obvious but frequently omitted step in the assessment process is asking the patient to check their weight. A recent survey found that more than half of primary care physicians (PCPs) were unable to visually assess body mass index (BMI), with the majority underestimating it [ 31 ]. Visual assessment is a poor way of determining an individual’s weight; as with any other condition, e.g. hypertension, an accurate assessment is essential for informing subsequent management steps. Routinely asking all patients if weight can be checked as part of a consultation eliminates the possibility of under-estimation, thus reducing the risk of continued, undetected weight gain [ 32 ]. Measuring waist circumference in addition to BMI can also be helpful in evaluating cardiometabolic risk in some individuals [ 33 ].

A key component in delivering individualised weight loss interventions is the identification of the root causes and drivers of weight gain. Taking a weight history assessing the individual’s weight onset, triggering factors, impact of weight on quality of life, previous weight loss attempts, life events during previous weight loss attempts and pattern of weight gain should be explored [ 25 ]. The use of open questions by the HCP enables active listening and encourages the individual to share their experience. Determining such factors can enable both the HCP and the person living with obesity to gain a clear picture of what the drivers of weight gain might be, facilitating the development of tailored care plans. Furthermore, this step could disclose whether a new patient has come to the clinic having already lost weight, informing subsequent management steps.

A recent survey found that many healthcare professionals see limited clinical value in a 5–10% weight loss [ 2 ]. However, there is good evidence that modest weight losses can reduce the risk and improve the management of obesity-related complications (Fig.  1 ).

Health benefits of a 5–10% weight loss. a Detailed summary of health benefits that can be achieved with a 5–10% weight loss from baseline including quantitative/measurable benefits on cardiometabolic and glycaemic parameters, and life expectancy. b Simplified version of the health benefits that are achievable with a 5–10% weight loss, in patient-friendly language . HDL high-density lipoprotein, LDL low-density lipoprotein, BP blood pressure, HbA1C haemoglobin A1c, T2DM type 2 diabetes mellitus; *with 10 kg weight loss; † in individuals who have undergone bariatric surgery . References: 1 Wing RR, et al. Diabetes Care. 2011; 34: 1481–6. 2 Van Gaal L, et al . Eur Heart J 2005; 7: 21–6. 3 Aucott L, et al . Hypertension . 2005; 45: 1035–41. 4 Lindstrom J, et al . Diabetes Care 2003; 26: 3230–6. 5 Tuomilehto et al . N Engl J Med 2001; 344: 1343–50. 6 Luo J, et al . Oncotarget 2017; 8: 81,719–720. 7 Tee MC, et al . Surg Endosc . 2013; 24: 4449–56. 8 National Cancer Institute: Obesity and Cancer Available at: https://www.cancer.gov/about-cancer/causes-prevention/risk/obesity/obesity-fact-sheet , accessed 25 September 2019. 9 Kritchevsky SB, et al. PLoS ONE 2015; 10: e0121993

Advising individuals with overweight/obesity of the health benefits of 5–10% weight loss can help to centre the consultation on realistic expectations. Delivery is key, as avoiding jargon and using patient-friendly terminology has proven more impactful than using unfamiliar medical terminology [ 34 ], e.g. ‘increased risk for T2D’ versus ‘hyperglycaemia’.

During weight loss counselling, HCPs should keep in mind the identified root causes for each individual and explore the modifiable aspects with them to incorporate into a tailored weight management plan. Advice may centre around eating well (which foods to incorporate in each meal and how much; caloric density of specific foods and alcoholic beverages), being physically active (to support weight loss and for general health), and addressing psychological aspects related to weight (e.g. binge eating, emotional eating), behavioural therapy, medications and referral to bariatric surgery services in some cases [ 35 ].

Having given advice on the relevant aspects of weight, a plan should be agreed, which is a common pitfall in trying to achieve too much too fast. Goals should be specific, measurable, achievable, relevant and timely (SMART) to increase the likelihood of success [ 36 ]. An example of a SMART goal is ‘I will walk for 20 min at lunchtime on Monday, Wednesday and Friday’, which is more constructive than agreeing a vague plan to ‘start exercising’.

While it is important to consider the health benefits of a 5–10% weight reduction when setting targets, losing 5–10% of baseline body weight as a first attempt might be unrealistic for some individuals. Setting unrealistic weight loss goals is not only common among HCPs, as people often visit practitioners hoping to reduce weight at a rate that is unlikely to be sustainable in the long term [ 37 ]. Thus, setting clear HCP and patient expectations from the very beginning is key for avoiding disappointment if goals are not met. In addition, HCPs should work collaboratively with individuals to create plans that aim to reduce weight at a comfortable pace for each person [ 38 ]. For example, this might involve setting smaller targets as opposed to setting the first target as the total desired weight loss. Unrealistic goals may result in failure [ 39 ], which could deter individuals from re-attempting weight loss. A weight loss of 5 kg may be clinically meaningful in terms of risk factor reduction for some individuals, and in other individuals may improve the ability to carry out an important daily task that was not possible before. Small accomplishments such as these should be recognised as successes, and attitudes must be shifted in alignment with each individual’s capabilities.

Having established the importance of setting realistic, personalised goals, the next step is to agree the specific elements of a helpful plan. Eating behaviours are a key target of all weight-loss interventions and, in order to elicit changes, individuals must assess their typical eating patterns and identify aspects they wish to change [ 40 ]. Individuals may have endured stigmatising conversations about their dietary habits in the past, therefore it is important to ask permission to discuss it. An appropriate question might be, ‘Would it be helpful to look in a bit more detail at how food or activity is fitting into your typical day?’ Following identification of dietary habits, through use of, e.g., a food diary, the HCP and patient can explore together any the specific dietary changes that might be helpful, such as increasing fibre and protein intake, and reducing intake of energy-dense foods and drinks [ 5 ].

Alongside tracking eating behaviours, HCPs may also encourage individuals to self-monitor physical activity patterns and weight. Care providers should encourage individuals to set goals relative to their current mobility and to slowly increase the amount of time the person is active [ 38 ]. In parallel with reducing the burden of obesity-related complications, physical activity and associated weight loss should aim to improve an individual’s quality of life.

Although possible according to trials such as the DiRECT trial of an intensive primary care-led weight management intervention using total diet replacement for those with T2D [ 41 ], weight loss of greater than 10% is unlikely with behavioural changes alone, due to metabolic adaptations. Consequently, while behavioural interventions are the basis of all weight management interventions, pharmacotherapy and/or bariatric surgery should be considered for additional benefits [ 40 ].

Eligibility for pharmacotherapy or bariatric surgery is based on degree of obesity (usually determined by BMI and/or waist circumference) and obesity-related comorbidities. Eligibility criteria are similar across regions [ 5 , 40 , 42 – 44 ], although BMI cutoff values are lower in Asian populations [ 45 ] (Fig.  2 ). Rather than offering behavioural, pharmacotherapy and bariatric surgery sequentially, all relevant options should be discussed with individuals as early as possible (Table ​ (Table2 2 ).

Guidance on level of intervention to consider [ 5 , 40 – 45 , 59 ]. BMI body mass index (reported in kg/m 2 ). Consult local guidelines for specific recommendations. *In some non-Asian regions, people with type 2 diabetes can be considered

Summary of dosing regimens of available and investigational weight management pharmacotherapies

Weight management agents should always be used in conjunction with appropriate behavioural interventions

a Semaglutide is an investigational product and has not been approved by the FDA or EMA at the time of writing

The treatment approach should be agreed between the individual and HCP following consideration of risks, benefits and individual circumstances. When initiating pharmacotherapy, the HCP should stress the importance of ongoing behavioural changes, as all weight-management pharmacotherapies are indicated in conjunction with a reduced calorie diet and/or increased physical activity [ 47 , 49 , 51 , 53 ].

Regular follow-up is imperative for identifying less successful approaches early, yet, remarkably, only 24% of people with obesity in the US report having follow-up appointments in place after their initial weight-loss consultation [ 56 ]. HCPs should agree a suitable timeframe for regular weight-related consultations to ensure ongoing support.

Given the complex and multifactorial nature of obesity, it is unsurprising that HCPs may need to refer onward to more specific services. Depending on healthcare systems and available resources, HCPs may need to signpost or refer individuals to more specialist providers such as psychologists, dietitians, physiotherapists, endocrinologists, commercial weight loss programmes, or bariatric centres to ensure all elements of weight gain are managed adequately and appropriately where needed [ 57 ]. Indeed, a recent study conducted in the UK found that providing support through referral to specialist services was more effective than solely advising individuals to lose weight [ 58 ].

Obesity rates are rising across the world, and HCPs can play an important role in supporting individuals to manage this disease. Management should focus on improving health, including assessing for and addressing the drivers and complications of obesity. Although PCPs lie at the centre of this approach, efforts to manage overweight and obesity should be shared among all care providers to ensure the delivery of interventions that consider the environmental and psychosocial influences that impact obesity at a behavioural level, while addressing the physiological and psychological challenges of long-term weight management.

Acknowledgements

The journal’s Rapid Service fee for this manuscript has been supported by an educational grant from Novo Nordisk A/S.

Editorial Assistance

The authors are grateful to Ruth Wills and Fay Pickering of International Medical Press for editorial support in the development of this manuscript.

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Author contributions

Professor Donal O’Shea, Dr. Cathy Breen, Dr. Scott Kahan, Miss Lorna Lennon were involved in all aspects of manuscript development.

Disclosures

Professor Donal O’Shea has received honoraria/consultation fees from Novo Nordisk, GlaxoSmithKline and Menarini. Dr. Cathy Breen has received honoraria and had conference attendance sponsored by MSD, AstraZeneca, Sanofi-Aventis, Roche and Eli-Lily. Ms Lorna Lennon and Dr. Scott Kahan have nothing to disclose.

Compliance with Ethics Guidelines

This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.

The original online version of this article was revised due to update in Table 2.

Change history

A Correction to this paper has been published: 10.1007/s12325-021-01802-x

Assessing Prevalence and Trends in Obesity: Navigating the Evidence (2016)

Chapter: 7 conclusions and recommendations, 7 conclusions and recommendations.

At the heart of obesity prevalence and trends analyses are seemingly basic questions— How many people have obesity? Are any groups disproportionately affected? How has this changed over time? These questions, however, encompass tremendous methodological and interpretive complexity. Investigators have assessed obesity prevalence and trends from different perspectives, using a range of data sources and various analytic approaches. The inconsistencies across published reports have created barriers to interpreting and using such statistics. To understand how best to extract meaning from recent reports, to determine what data gaps need to be filled, and to consider how the future of data collection can be improved, a thorough evaluation of existing differences and why they exist is crucial. In providing such information, this report serves as an initial step toward obesity prevalence and trends estimates that are more transparent, aligned, and comprehensive.

To address its task (see Box 1-2 ), the committee assessed common data sources, extracted information from recent published reports, and reviewed associated protocols and data collection instruments. The committee considered other information sources, but primarily relied on the methodologic approaches to data collection and analysis presented in peer-reviewed published reports when formulating its conclusions and recommendations.

CONCLUSIONS

The interpretation of obesity prevalence and trends estimates is contingent on considerations specific to the assessment of obesity status, principles that are founded in epidemiology, and concepts that are fundamental to

statistics. This interplay of these elements, which span from general to specific, is reflected in the committee’s conclusions. Much of the available evidence indicates that at the core of several of the current limitations are seemingly basic challenges faced by any population-based prevalence or trends evaluation. The fact that these issues exist, however, underscores the challenges of finding viable solutions. The committee’s conclusions focus on key domains that cut across the broad literature base. These include current sources of data, data for specific population groups, measured versus reported data, estimates of changes and trends over time, and interpretations of estimates.

Current Sources of Data

A wide variety of data sources capture height and weight data. Population surveys serve as a primary source of nationally representative estimates, but often differ from each other in terms of overall design, sample size, target population(s), geographic representation (e.g., national, multiple states, and localities), and method for collecting height and weight data. Few data sources are designed to generate estimates of multiple states and those that do tend to describe select population groups (e.g., Youth Risk Behavior Surveillance System: high school students). School-based body mass index (BMI) assessments have emerged as a prominent source of data used to describe obesity prevalence and trends among school-aged children within states and smaller geographic areas (e.g., counties, school districts, individual schools). Such data, however, can be limited by issues related to data quality, data privacy, and sample representativeness and can be difficult to compare across states due to differing protocols. Clinical and public health administrative data also have been used as a source of data. Although such data sources often contain an enormous number of records with directly measured heights and weights, they can be limited for the purposes of obesity prevalence and trends estimation as they do not necessarily represent populations outside of those who use the services. Finally, cohort studies have been used in published reports to describe cross-sectional prevalence, longitudinal trends, and intrapersonal changes in obesity status. Cohort studies, however, are not as common in the obesity prevalence and trends literature as cross-sectional or repeated cross-sectional assessments, because they can be challenging and expensive to properly design and implement.

Conclusion 1: The committee concludes that existing data sources used to estimate prevalence and trends in obesity vary by factors, including study design, geographic representation, data collection methodologies, and overall intent. Each offers specific and distinct information about the state of obesity. The differences between data sources, however, can limit the comparability of reports.

Data for Specific Population Groups

Investigators divide analytic samples into subgroups to determine the extent to which obesity prevalence and trends vary within a broader population. Although participants are commonly grouped by geographic level (e.g., state, region, county) and demographic characteristics (e.g., age, race, ethnicity, socioeconomic status), the factors defining the groups (e.g., span of ages, race and ethnicity categories) can vary widely across reports, even those that analyze data from the same data source. Groupings do not always represent the level of detail captured during data collection, but rather often reflect decisions investigators make to best answer specific research question(s) given limitations of the data. Published reports often cite inadequate sample size as the reason for omitting one or multiples subgroups from the analyses or for combining heterogeneous groups into a single category. Advanced statistical techniques, such as small area estimation, are one means to generate model-based estimates for smaller geographic areas and population groups for which reliable direct survey estimates cannot be generated. These techniques are contingent on the quality and quantity of data used to develop such models and require a fair degree of statistical sophistication in order to provide meaningful results.

Conclusion 2: The committee concludes that inclusion of subgroups in data sources provides essential insight into how obesity prevalence and trends estimates vary within and between population groups. However, insufficient sample size is a primary limitation to generating reliable estimates.

Measured Versus Reported Data

Comparing BMI to a population reference, typically the 2000 Centers for Disease Control and Prevention (CDC) sex-specific BMI-for-age growth charts and associated cut point, is the prevailing approach for classifying obesity status among children, adolescents, and young adults. Height and weight data used to calculate BMI are collected through direct measurement, proxy-report, and self-report. Within each collection approach, variability exists in the specific data collection protocol. Proxy- and self-reported height and weight questions have been incorporated into various population surveys in which factors such as the overall design and mode of delivery (e.g., phone interview) do not allow for direct measurement. These surveys often have large sample sizes and some have been used to generate estimates that are compared across states and select localities. Factors such as sex, age, and weight status can affect the degree to which reported height and weight values differ from directly measured values. Evidence indicates

that use of proxy-reported data for young and school-aged children generally does not lead to accurate estimates of prevalence. As such, some population surveys (i.e., National Health Interview Survey, previous cycles of the National Survey of Children’s Health) have discontinued collecting proxy-reported height and weight data and/or generating obesity prevalence estimates from such data for these age groups (children younger than ages 10 to 12 years). Limited evidence, based on different nationally representative surveys, suggests that trends in obesity estimated from self-reported and directly measured heights and weights among high school-aged individuals exhibit similar patterns, albeit at different values.

Conclusion 3: The committee concludes that although all measures have limitations, directly measured height and weight data collected using a standardized protocol provide the best estimates of obesity prevalence. Self- and proxy-reported height and weight data can be used to fill data gaps and provide insight into overall obesity trends, although these data collection methods do not produce prevalence estimates comparable to those based on direct measure.

Estimates of Changes and Trends Over Time

Published reports assessing obesity prevalence over time have presented findings as change (the difference between two time points) or trends (the difference over three or more time points). Such estimates pertain only to the specific time points included in the analyses. Trend estimates typically become more precise and nuanced as the number of time points increases. However, the number of time points is dependent, in part, on the reliability of the prevalence estimates. Investigators often combine multiple years or cycles of data to increase the reliability of the estimates used to determine the trend, thereby reducing the number of data points. Changes to the time interval included in the trend analyses directly affect the estimate and its meaning.

Conclusion 4: The committee concludes that comparability of trend reports is enhanced when analyses use similar start and end dates and time intervals to define the trend.

Interpretation of Estimates

Factors that affect the interpretation of obesity prevalence and trends estimates not only include characteristics of a data source, but also encompass decisions made during analysis. Data sources differ with respect to who the sample is designed to represent and who contributes data. Changes to the sampling or data collection procedures over time affect what data

are available for trend analyses. The portion of the overall sample that is used for analysis varies across published reports for a number of reasons, including: what question(s) is being asked of the data, how the data were prepared for analysis, and whether the samples size led to reliable estimates of prevalence. Differences exist in data collection methodologies, with the options height and weight data collection leading to estimates that are generally not equivalent. The statistical analyses are varied and are guided by the intent of the specific report, the quality control measures taken during data collection, the study design from which the data were derived, and the amount of data available.

Conclusion 5: The committee concludes that appropriate interpretation of estimates of obesity prevalence and trends requires consideration of the population in the sample, the data collection methodologies, and the analytic procedures together in a guided way.

RECOMMENDATIONS

Data sources that capture height and weight largely operate in isolation or within a single surveillance system, resulting in designs and protocols that differ from each other. Although these differences often limit comparability of prevalence and trends estimates, their existence underscores the diverse context in which decisions and compromises have to be made in the design, collection, and analysis of the data. Given this landscape, the committee offers recommendations in three areas: assessing published reports on obesity prevalence and trends; improving future data collection efforts; and conducting research to address data gaps.

Assessing Obesity Prevalence and Trends Reports

Because understanding and appropriately applying estimates of obesity prevalence and trends is a complex process, the committee provides the Assessing Prevalence and Trends (APT) Framework as a conceptual guide for stakeholders who seek to better understand and use reports. The framework draws on the committee’s synthesis of key considerations related to inconsistencies that exist in the literature while simultaneously drawing on fundamental principles of epidemiology and statistics. The framework emphasizes that the population assessed, methods used, and analyses performed are not simply discrete characteristics of a published report, but interconnected elements that inform each other and the interpretation of obesity prevalence and trends estimates. The committee considers determining the utility of estimates presented in published reports a highly individualized process, determined by the end user’s overall goal and specific data needs.

Recommendation 1: The committee recommends that stakeholders who use or seek to use estimates of obesity prevalence and trends to inform policy making, program planning, and decision making follow the Assessing Prevalence and Trends (APT) Framework to guide their interpretation of published reports.

The committee recognizes that end users who operate at the national, state, and local levels often have different information needs. The extent to which available analyses meet those needs varies considerably. Individual end users are therefore likely to have different priorities when it comes to the strengths and weaknesses of published reports. In order to be adaptable to a range of possible applications, the APT Framework integrates consideration of the end user’s context to guide the assessment.

The relevance or importance of the framework elements and guiding questions will vary by end users. As the framework is disseminated, used, and evaluated, opportunities to refine and adapt its various components will emerge. The committee foresees application of the framework beyond evaluating existing published reports. The concepts presented in the framework have the potential to guide the design of new prevalence and trends studies and to better align reporting practices of investigators publishing their research.

Future Data Collection

By evaluating the methodological approaches presented in published literature on obesity prevalence and trends, this report serves as an important starting point for moving toward comparable, more unified data collection, analysis, and reporting practices. Current practices, however, are determined by more than just the analytic and scientific rationale presented in a published report. Factors such as cost, existing infrastructure, and available resources play a role in the selection of a study design and the success of its implementation. As such, the committee recognizes that it would be premature to offer explicit, prescriptive guidance on specific methodologies to be used by the research and public health surveillance communities. Such a determination requires consideration of the experiential knowledge of those who fund, develop, and carry out such activities.

Recommendation 2: The committee recommends that an organization with a track record of cross-sector leadership in the field of obesity, such as the National Collaborative on Childhood Obesity Research or the Robert Wood Johnson Foundation, convene relevant stakeholders to examine and identify feasible and practical approaches to stan

dardizing methodologies for data collection and reporting, appropriate for application at the national, state, and local levels to enhance comparability of obesity prevalence and trend analyses.

The committee envisions the proposed convening of stakeholders as a vital next step needed to inform the decision of which methodologies should be used to generate comparable estimates of obesity prevalence and trends nationally. Guided by the APT Framework and methodologic considerations presented throughout this report, the proposed convening would serve as an opportunity to discuss challenges to implementation that exist and consider opportunities for innovation. The committee recommends a range of relevant topics be considered, including

• Determining ways to leverage existing infrastructure and surveillance systems, and improve and sustain capacity.
• Harmonizing time periods used to determine trends.
• Defining the level of detail of information that should be presented in published reports and ways in which it should be presented (e.g., relative versus absolute change).
• Considering opportunities to overcome sample size limitations so that reliable estimates can be determined and trends can be assessed for a broad range of population subgroups.

To include a range of participants, the committee recommends that the organization(s) sponsoring the proposed activity not only have a national prominence, but also strong ties to stakeholders who operate at the state and local levels. The organizations included in the recommendation—the National Collaborative on Childhood Obesity Research (NCCOR) and the Robert Wood Johnson Foundation (RWJF)—have missions and experience that are well aligned with the recommendation goals. With a goal of improving childhood obesity surveillance at the national, state, and local levels, NCCOR represents one potential sponsor. The funding partners of NCCOR are the CDC, the National Institutes of Health, RWJF, and the U.S. Department of Agriculture (USDA). Similarly, RWJF has a documented history of cross-sector collaboration and, as the sponsor of this study, may seek to continue building on the work of this committee. These are two examples of potential conveners. However, the committee notes that other conveners or collaborators may enrich the proposed activity as well.

The committee further recommends that a broad range of stakeholders who operate at the national, state, and local levels be involved in this activity, including, but not limited to

• Local and state public health agencies;
• Federal governmental agencies (e.g., CDC, USDA, Agency for Healthcare Research and Quality);
• Community-based organizations;
• School officials (e.g., state Departments of Education, superintendents, school nurses, and physical education teachers);
• Academic researchers;
• Research organizations;
• Research funders;
• Obesity oriented and public health professional organizations; and
• Other decision makers at who operate at the national, state, and local levels.

Research to Address Gaps

The assessment of obesity prevalence and trends estimates continues to change with technological, methodological, and statistical advancements. Some of the inconsistencies and limitations that currently exist in the literature represent prime opportunities for improvement and progress.

Recommendation 3: The committee recommends that the research community design and conduct studies to strengthen the evidence base and improve methodological approaches to assessing obesity.

Opportunities for improvement encompass a wide range disciplines. The research community described in the recommendation includes, but is not limited to, federally funded researchers, clinical researchers, social scientists, and engineers.

Specific research initiatives could include

• Evaluating how the 2000 CDC BMI-for-age growth charts can better provide continuity to obesity classification across the life course. The committee acknowledges that the 2000 CDC BMI-forage growth charts are the predominant reference currently in use in the United States for children ages 2 years and older and supports its continued use as a platform for comparability of estimates of obesity among children and adolescents. The committee anticipates findings from current and future initiatives (e.g., the Dietary Guidance Development Project for Birth to 24 Months and Pregnancy [B-24/P], the INTERGROWTH-21st Fetal and Newborn Growth Consortium, and the Environmental Influences on Child Health Outcomes [ECHO] Program) will inform an evidence-based consensus on how weight status should be classified for children

younger than age 2 years. Additionally, opportunities exist to clarify when and how best to transition young adults to the adult criterion for obesity classification.

• Identifying appropriate measures of core demographic variables—including but not limited to race and ethnicity, socioeconomic status, and rurality—that can be captured in a consistent manner across various data collection efforts at the national, state, and local levels. As the demographic landscape of the country continues to change, it will become increasingly vital to characterize populations in ways that capture the diversity that exists.
• Developing innovative, practical, and accurate tools for assessing adiposity. Although BMI is the predominant measure of relative weight used to classify obesity status, it is not without limitations. For the purposes of population-based assessments, a new measure of obesity will need to be a simple alternative that provides comparable or improved predictive ability, that can be measured in a variety of settings, and that is relevant to diverse population groups across the life course.
• Preventing the misclassification of data from individuals with severe obesity as biologically implausible values. Technology-based systems that are used for direct data entry often have features that automatically detect extreme values in height and weight. Identification at the time of measurements allows for the values to be corrected or properly documented. Data collection procedures that first record measurements on paper or in systems without automatic detection of extreme values often have limited ability to check the quality of data until entry into a database or analysis. Opportunities exist to expand the use of technology in data collection to enhance the accuracy of recorded measurements.
• Identifying innovative opportunities to capture longitudinal data throughout childhood. A variety of data sources—including electronic health records and school-based BMI assessments—are primed to be used in novel ways to serve as the basis for or supplement longitudinal evaluations.

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Obesity has come to the forefront of the American public health agenda. The increased attention has led to a growing interest in quantifying obesity prevalence and determining how the prevalence has changed over time. Estimates of obesity prevalence and trends are fundamental to understanding and describing the scope of issue. Policy makers, program planners, and other stakeholders at the national, state, and local levels are among those who search for estimates relevant to their population(s) of interest to inform their decision-making. The differences in the collection, analysis, and interpretation of data have given rise to a body of evidence that is inconsistent and has created barriers to interpreting and applying published reports. As such, there is a need to provide guidance to those who seek to better understand and use estimates of obesity prevalence and trends.

Assessing Prevalence and Trends in Obesity examines the approaches to data collection, analysis, and interpretation that have been used in recent reports on obesity prevalence and trends at the national, state, and local level, particularly among U.S. children, adolescents, and young adults. This report offers a framework for assessing studies on trends in obesity, principally among children and young adults, for policy making and program planning purposes, and recommends ways decision makers and others can move forward in assessing and interpreting reports on obesity trends.

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Draw an Informed Conclusion: Simple Steps Can Prevent Obesity

The number of obese adults in the United States has doubled since the early 1960s.

Today, more than two-thirds of adults in the country are considered overweight or obese, putting them at risk for coronary heart disease, high blood pressure, stroke, joint problems, some forms of cancer, and type 2 diabetes. The most recent five-year survey found that more adult women in the United States are obese than men, a trend that Women's Health Research at Yale hopes to understand and help to remedy.

There are many reasons for the obesity epidemic, including sedentary lifestyles with people watching TV or playing video games for many hours and sitting in cars for long commutes to a job where many spend hours at a desk, unnecessarily large portions for meals served at restaurants and at home, the ubiquity of fast food advertising, the popularity of soft drinks and other food and beverages with added sugar, and lack of access to healthy foods due to convenience or price.

But it is not necessary to redesign modern society or completely overhaul diets to make significant improvements in health.

“Small changes can make a big difference,” said Dr. Margaret Grey, the Annie Goodrich Professor of Nursing at Yale School of Medicine and the Deputy Director of the Yale Center for Clinical Investigation.

Studies have shown that eating a little healthier and exercising for about 30 minutes five days a week can reduce weight and delay and maybe even prevent type 2 diabetes, which involves higher risks of heart disease and depression for women.

“People should aim for losing about 5 percent of their total body weight to start,” Grey said. “Every little bit counts, and it’s never too late.”

Grey recommends that people seeking to control their weight set small, attainable goals such as taking three 10-minute walks each day. It’s helpful to actually set the time aside in a calendar or daily planner, she said.

“Any type of exercise will do,” Grey said. “The idea is to get your heart rate up. Take the stairs instead of taking an elevator. Dance at home with your kids. It can be fun.”

Small changes can make a big difference. Every little bit counts, and it’s never too late. Dr. Margaret Grey

In addition, Grey recommends that people pay attention to portion sizes when they eat. Use smaller plates instead of filling up a big one with food. Make sure to eat breakfast so the day starts with a full stomach. Indulge in small, healthy snacks over the course of the day instead of holding out for big meals.

Making small changes when shopping and cooking can also lead to better health, she said. Buy low fat instead of whole milk. Bake or grill food instead of frying it.

The idea isn’t to starve yourself or deprive you of the things you like, Grey said. But to make a tweak here and there to gradually improve overall eating habits. And so that treats become something you actually treat yourself to and not an everyday habit.

“Obesity is a big problem that requires serious attention,” she said. “But we can all get to a healthier place while taking small steps.”

This video was supported in part by The Grace J. Fippinger Foundation.

For more news from Women's Health Research at Yale, connect with us on Facebook and Twitter , or visit our website .

• Women's Health

Featured in this article

• Margaret Grey, DrPH, RN, FAAN Annie Goodrich Professor Emeritus and Dean Emeritus of Nursing; Affiliated Faculty, Yale Institute for Global Health; Deputy Director, Yale Center for Clinical Investigation; Professor of Pediatrics (secondary)

Related Links

• Preventing Osteoporosis
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Conclusion: Obesity and its prevention in the 21st century

• Epidemiology & Community Health

Research output : Chapter in Book/Report/Conference proceeding › Chapter

The case for a preventative approach to the obesity epidemic is compelling. Obesity poses what is arguably one of the most significant threats to population health that is currently faced. The data presented in this book highlight just how common obesity has become in children and in adults across the globe, and how it impacts disproportionately on the poor. This chapter presents a summary of the discussions in the preceding chapters.

Bibliographical note

• Obesity control
• Obesity epidemic
• Obesity prevention
• Population health

This output contributes to the following UN Sustainable Development Goals (SDGs)

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• 10.1093/acprof:oso/9780199571512.003.0026

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• Link to publication in Scopus
• Link to the citations in Scopus

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• 21st Century Keyphrases 100%
• Obesity Keyphrases 100%
• Population Health Biochemistry, Genetics and Molecular Biology 100%
• Obesity Epidemic Keyphrases 50%
• Common Obesity Keyphrases 50%
• Preceding Chapter Keyphrases 50%
• Preventative Approach Keyphrases 50%

T1 - Conclusion

T2 - Obesity and its prevention in the 21st century

AU - Crawford, David

AU - Ball, Kylie

AU - Jeffery, Robert W.

AU - Brug, Johannes

N1 - Publisher Copyright: © Oxford University Press 2010. All rights reserved.

PY - 2010/9/9

Y1 - 2010/9/9

N2 - The case for a preventative approach to the obesity epidemic is compelling. Obesity poses what is arguably one of the most significant threats to population health that is currently faced. The data presented in this book highlight just how common obesity has become in children and in adults across the globe, and how it impacts disproportionately on the poor. This chapter presents a summary of the discussions in the preceding chapters.

AB - The case for a preventative approach to the obesity epidemic is compelling. Obesity poses what is arguably one of the most significant threats to population health that is currently faced. The data presented in this book highlight just how common obesity has become in children and in adults across the globe, and how it impacts disproportionately on the poor. This chapter presents a summary of the discussions in the preceding chapters.

KW - Obesity control

KW - Obesity epidemic

KW - Obesity prevention

KW - Population health

UR - http://www.scopus.com/inward/record.url?scp=84921259914&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84921259914&partnerID=8YFLogxK

U2 - 10.1093/acprof:oso/9780199571512.003.0026

DO - 10.1093/acprof:oso/9780199571512.003.0026

M3 - Chapter

AN - SCOPUS:84921259914

SN - 9780199571512

BT - Obesity Epidemiology

PB - Oxford University Press

470 Obesity Essay Topic Ideas & Examples

Looking for obesity essay topics? Being a serious problem, obesity is definitely worth writing about.

Essay Examples on Obesity

Hook examples for obesity essays, "the silent epidemic among us" hook.

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"Obesity is a public health crisis with wide-ranging effects. Investigate the strain on healthcare systems, the rise of related diseases, and the economic impact of obesity."

"The Cultural Shift: Food, Technology, and Sedentary Lifestyles" Hook

"Examine how cultural factors, including dietary habits, technology use, and sedentary lifestyles, have contributed to the obesity epidemic. What can we learn from these trends?"

"Breaking the Cycle: Strategies for Prevention" Hook

"Prevention is key to combating obesity. Discuss effective strategies for preventing obesity in children and adults, from education to policy changes."

"The Psychological Battle: Obesity and Mental Health" Hook

"Obesity often intersects with mental health challenges. Explore the complex relationship between obesity and mental well-being, as well as the stigma attached to it."

"Shifting Perspectives: Celebrating Body Positivity" Hook

"In the midst of the obesity crisis, the body positivity movement is gaining ground. Discuss the importance of promoting self-acceptance and diverse body images."

Obesity in America: a Growing Epidemic

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Obesity is a condition in which excess body fat has accumulated to such an extent that it may have a negative effect on health. Medical organizations tend to classify people as obese based on body mass index (BMI) – a ratio of a person's weight in kilograms to the square of their height in meters.

There are three types of obesity: Class 1 (low-risk) obesity, if BMI is 30.0 to 34.9; Class 2 (moderate-risk) obesity, if BMI is 35.0 to 39.9; Class 3 (high-risk) obesity, if BMI is equal to or greater than 40.0.

The major contributors to obesity are: diet, sedentary lifestyle, genetics, other illnesses, social determinants, gut bacteria, and other factors.

Excessive body weight has a strong link to many diseases and conditions, particularly cardiovascular diseases, diabetes mellitus type 2, obstructive sleep apnea, certain types of cancer, osteoarthritis, and asthma. As a result, obesity has been found to reduce life expectancy.

Most of the world's population live in countries where overweight and obesity kills more people than underweight. 39 million children under the age of 5 were overweight or obese in 2020. Worldwide obesity has nearly tripled since 1975. From 1999-2000 through 2017-March 2020, US obesity prevalence increased from 30.5% to 41.9%.

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• Human milk oligosaccharide 2’-fucosyllactose protects against high-fat diet-induced obesity by changing intestinal mucus production, composition and degradation linked to changes in gut microbiota and faecal proteome profiles in mice
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• http://orcid.org/0009-0000-9385-9890 Paola Paone 1 ,
• http://orcid.org/0000-0002-2472-9328 Dimitris Latousakis 2 ,
• Romano Terrasi 3 ,
• http://orcid.org/0000-0001-7648-8282 Didier Vertommen 4 ,
• http://orcid.org/0000-0003-0577-8834 Ching Jian 5 ,
• http://orcid.org/0000-0002-5003-8678 Valentina Borlandelli 6 ,
• http://orcid.org/0000-0002-1343-5500 Francesco Suriano 7 ,
• http://orcid.org/0000-0002-4237-6677 Malin E V Johansson 7 ,
• Anthony Puel 1 , 8 ,
• http://orcid.org/0000-0003-0694-1947 Caroline Bouzin 9 ,
• http://orcid.org/0000-0003-2115-6082 Nathalie M Delzenne 1 ,
• http://orcid.org/0000-0002-6960-7447 Anne Salonen 5 ,
• http://orcid.org/0000-0001-8515-1315 Nathalie Juge 2 ,
• http://orcid.org/0000-0001-7114-2266 Bogdan I Florea 6 ,
• http://orcid.org/0000-0002-1600-9259 Giulio G Muccioli 3 ,
• http://orcid.org/0000-0001-6976-7005 Herman Overkleeft 6 ,
• http://orcid.org/0000-0002-5503-107X Matthias Van Hul 1 , 8 ,
• http://orcid.org/0000-0003-2040-2448 Patrice D Cani 1 , 8 , 10
• 1 Louvain Drug Research Institute (LDRI), Metabolism and Nutrition research group (MNUT) , UCLouvain, Université catholique de Louvain , Brussels , Belgium
• 2 The Gut Microbiome and Health and Food Safety Institute Strategic Programme, Norwich Research Park , Quadram Institute Bioscience , Norwich , UK
• 3 Louvain Drug Research Institute (LDRI), Bioanalysis and Pharmacology of Bioactive Lipids Research Group (BPBL) , UCLouvain, Université catholique de Louvain , Brussels , Belgium
• 4 de Duve Institute, MASSPROT platform , UCLouvain, Université catholique de Louvain , Brussels , Belgium
• 5 Human Microbiome Research Program, Faculty of Medicine , University of Helsinki , Helsinki , Finland
• 6 Department Bio-organic Synthesis, Leids Instituut voor Chemisch Onderzoek , Leiden University , Leiden , The Netherlands
• 7 Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine , University of Gothenburg , Gothenburg , Sweden
• 8 Walloon Excellence in Life Sciences and BIOtechnology (WELBIO) Department , WEL Research Institute , Wavre , Belgium
• 9 Institute of Experimental and Clinical Research (IREC), IREC Imaging Platform (2IP RRID:SCR_023378) , UCLouvain, Université catholique de Louvain , Brussels , Belgium
• 10 Institute of Experimental and Clinical Research (IREC) , UCLouvain, Université catholique de Louvain , Brussels , Belgium
• Correspondence to Professor Patrice D Cani, Louvain Drug Research Institute (LDRI), Metabolism and Nutrition research group (MNUT), UCLouvain, Université catholique de Louvain, Avenue E. Mounier, 73 B1.73.11 B-1200, Brussels, Belgium; patrice.cani{at}uclouvain.be

Objective To decipher the mechanisms by which the major human milk oligosaccharide (HMO), 2’-fucosyllactose (2’FL), can affect body weight and fat mass gain on high-fat diet (HFD) feeding in mice. We wanted to elucidate whether 2’FL metabolic effects are linked with changes in intestinal mucus production and secretion, mucin glycosylation and degradation, as well as with the modulation of the gut microbiota, faecal proteome and endocannabinoid (eCB) system.

Results 2’FL supplementation reduced HFD-induced obesity and glucose intolerance. These effects were accompanied by several changes in the intestinal mucus layer, including mucus production and composition, and gene expression of secreted and transmembrane mucins, glycosyltransferases and genes involved in mucus secretion. In addition, 2’FL increased bacterial glycosyl hydrolases involved in mucin glycan degradation. These changes were linked to a significant increase and predominance of bacterial genera Akkermansia and Bacteroides , different faecal proteome profile (with an upregulation of proteins involved in carbon, amino acids and fat metabolism and a downregulation of proteins involved in protein digestion and absorption) and, finally, to changes in the eCB system. We also investigated faecal proteomes from lean and obese humans and found similar changes observed comparing lean and obese mice.

Conclusion Our results show that the HMO 2’FL influences host metabolism by modulating the mucus layer, gut microbiota and eCB system and propose the mucus layer as a new potential target for the prevention of obesity and related disorders.

• INTESTINAL MICROBIOLOGY
• MUCOSAL BARRIER

Data availability statement

All data relevant to the study are included in the article or uploaded as online supplemental information.

All data generated or analysed during this study are included in this published article and its online supplemental information files. The raw amplicon sequencing data analysed in this study have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB72192. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [62] partner repository with the dataset identifier PXD049406.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/gutjnl-2023-330301

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WHAT IS ALREADY KNOWN ON THIS TOPIC

High-fat diet-induced obesity and metabolic disorders is associated with alterations in microbiota profile and gut barrier function.

The intestinal mucus layer is altered during high-fat diet (HFD), western-style diet, low-fibre diet, emulsifier treatments and in genetically obese ( ob/ob ) mice and human with dysglycaemia. The alterations observed include increased penetrability, decreased thickness, reduced growth and different mucin glycans composition.

Prebiotic treatments such as 2’-fucosyllactose (2’FL) improved gut barrier integrity in an in vitro model by affecting the mucus layer, but no studies have investigated whether the mucus is involved in the protection against obesity in vivo.

WHAT THIS STUDY ADDS

This study shows that supplementing 2’FL to HFD reduces the increase in body weight and fat mass, attenuates glucose intolerance and affects hormones involved in appetite regulation and energy homeostasis.

2’FL supplementation affects the mucus layer in vivo in the context of obesity, by increasing mucus production, secreted and transmembrane mucins, glycosyltransferases and glycosyl hydrolases, and mucin glycosylation.

Bacterial communities in mice fed with HFD plus 2’FL are remarkably enriched in Akkermansia and Bacteroides genera.

2’FL supplementation changes faecal proteome profiles, increasing proteins involved in carbon, amino acids and fat metabolism and decreasing those involved in protein digestion and absorption.

Supplementing 2’FL affects the intestinal endocannabinoid system.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

2‘FL is a prebiotic found naturally in the breast milk of about 80% of mothers. Excluding water, human milk oligosaccharides are the third most abundant ingredient in breast milk after fat and carbohydrates. Understanding their mechanism of actions and effects is vital information.

There is increased interest in 2’FL to be used as a supplement, not only in infant formula but also for subjects with 2’FL synthesis deficiency (ie, Fut2 genetic polymorphisms inducing fucosyltransferase inactivity).

This study shows that the mechanisms by which 2’FL counteracts obesity and metabolic disorders are associated with changes in the intestinal mucus layer and points towards the mucus as a new potential therapeutic target for the prevention and/or treatment of obesity and metabolic disorders.

Introduction

Obesity is associated with several metabolic alterations like type 2 diabetes, cardiovascular diseases and changes in the gut microbiota composition and gut barrier disruption. 1 Among the components of the gut barrier, it has been shown that the mucus layer is altered when the mice are fed a high-fat diet (HFD), western-style diet (WSD) or low-fibre diet and in ob/ob mice, as well as in patients with dysglycaemia. Among the alterations, it has been observed a reduced thickness, increased penetrability and altered mucin glycan composition. 2–9 The mucus exerts important roles in gut barrier protection and represents the interface of communication between bacteria and host. It is produced by the goblet cells (GCs) and constituted of glycoproteins called mucins, among which the main component is the secreted Muc2. The transmembrane mucins, involved in glycocalyx formation, are other important components of the gut barrier, conferring cell protection and mediating host–microbe interactions. 10 Mucins are glycosylated thanks to glycosyltransferases and mucin glycans supply attachment sites and allow bacterial growth and colonisation. Indeed, bacteria are able to produce glycosyl hydrolases (GHs) to degrade mucin glycans and use them as energy source. 10

α−1,2-fucosyltransferase, encoded by the FUT2 gene, is one of the glycosyltransferases responsible for the presence of histo-blood group antigens on multiple organs and on the gastrointestinal mucosa. 11 In recent years, genome-wide association studies have underlined the importance of FUT2 biology and showed that different polymorphisms may result in distinct secretor status, associated with the development of pathophysiology such as intestinal inflammation. 12 Furthermore, FUT2 has been shown to have significant effects on the intestinal bacterial community composition. 12–15 One of the major prototypical secretor-type oligosaccharides is the human milk oligosaccharide (HMO) 2’-fucosyllactose (2’FL). 16 17 In vivo and in vitro studies showed that 2’FL exerts biological properties as prebiotic, antibacterial, antiviral and immunomodulating effects and modifies the host’s epithelial cell-surface glycome. 18 This has prompted an increased interest in 2’FL as HMO source in infant formula and, more recently, 2’FL is also being investigated in pathological contexts. 19 For example, in mice fed HFD, it was observed that 2’FL reduced body weight and fat mass gain. 20 21 In addition, 2’FL protected against gut barrier disruptions induced by inflammatory stimuli, by increasing GCs number and Muc2 expression. 22 23 Further in vivo studies exploring the role of 2’FL on the mucus layer in the context of obesity induced by HFD feeding are still lacking.

To fill this gap, we designed a study aimed at deciphering whether the impact of 2’FL on metabolism could be linked to changes in the intestinal mucus production, glycosylation, secretion and degradation. In addition, we explored whether the effects on mucus layer and metabolism might be associated with modifications in gut microbiota composition, faecal proteome and endocannabinoid (eCB) system.

We believe that a comprehensive investigation into the intricate mechanisms of the mucus layer, including its biosynthesis, turnover and degradation, may offer novel insights into developing efficacious interventions for mitigating or preventing obesity and related metabolic disorders.

2’FL counteracts metabolic alterations induced by HFD

Mice fed an HFD diet supplemented with 2’FL showed significantly lower body weight and fat mass gain (subcutaneous, epididymal, visceral and brown adipose tissues) compared with mice fed HFD alone ( figure 1A–E ). This could not be explained by food intake or lean/muscle mass since there were no differences between HFD and HFD+2’FL groups ( online supplemental figure 1A-C ). Additionally, 2’FL supplementation reduced glucose intolerance, as evidenced by the shape of the glycaemia curve during the oral glucose tolerance test and by the lower insulin levels in fasting state ( figure 1F–I ). These effects coincide with changes in hormones involved in metabolic pathways, since 2’FL significantly increased the concentration of glucagon-like peptide-1 (GLP-1) and peptide YY (PYY) and decreased leptin and glucagon (for the latter not significantly) while ghrelin was significantly reduced by the HFD, with no effects of 2’FL ( figure 1J–N ).

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2’FL supplementation counteracts diet-induced obesity and glucose intolerance. (A) Body weight gain evolution and (C) fat mass gain evolution. (B) Final body weight gain and (D) fat mass gain. (E) Adipose tissue weights of subcutaneous (SAT), epidydimal (EAT), visceral (VAT) and brown (BAT) adipose tissue. (F) Plasma glucose (mg/dL) profile before and after 2 g/kg of glucose oral challenge measured during the oral glucose tolerance test (OGTT) and (G) the mean area under the curve (AUC) (mg/dL×min). (H) Plasma insulin (µg/L) measured 30 min before and 15 min after the glucose administration during the OGTT. (I) Insulin resistance index determined by multiplying the area under the curve (from −30 to 15 min) of blood glucose and plasma insulin obtained during the OGTT. (J–N) Plasma levels from the portal vein of glucagon-like peptide-1 (GLP-1), peptide YY (PYY), ghrelin, leptin and glucagon. Data are means±SEM (n=7–10/group). One-way ANOVA followed by Tukey post hoc test was applied to figure B, D, E, G–K, N while Kruskal-Wallis followed by Dunn’s test was applied to figure L,M, based on data distribution. Two-way ANOVA followed by Tukey post hoc test was applied to figure A, C, F. Data with different subscript letters are significantly different (p<0.05). *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. 2’FL, 2’-fucosyllactose; ANOVA, analysis of variance; HFD, high-fat diet.

2’FL increases intestinal cells proliferation and markers involved in gut barrier function

2’FL supplementation significantly increased full caecum and its content weight by about 80% and 150% compared with control and HFD, respectively ( figure 2A–C ). 2’FL supplementation also increased the length of the jejunum by almost 15% compared with CT and HFD ( figure 2D ).

2’FL increases microbiota fermentation, intestinal cell proliferation and markers of the gut barrier. (A) Full caecum, (B) empty caecum and (C) caecal content weight. (D) Jejunum length. (E–J) mRNA relative expression of markers of the gut barrier function measured in the jejunum, ileum, caecum and colon. Antimicrobial peptides mRNA expression: (E) Lysozyme C ( Lyz1 ), (F) Regenerating islet-derived 3-gamma ( Reg3g ), (G) Phospholipase A2 group II ( Pla2g2a ); (H) Proglucagon ; (I) Trefoil factor 3 ( Tff3 ); (J) Intectin . Data are means±SEM (n=7–12/group). Data were analysed using one-way ANOVA followed by Tukey post hoc test. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. 2’FL, 2’-fucosyllactose; ANOVA, analysis of variance; ND, not detectable.

Analysing the expression of genes involved in gut barrier function by qPCR, we found that 2’FL significantly increased the antimicrobial peptides Lyz1 and Reg3g in the caecum, and proglucagon in the caecum and colon while it induced the expression of Reg3g in the jejunum and colon, Pla2g2a in the colon and intectin in the ileum, without reaching significance ( figure 2E–J ).

2’FL affects GCs differentiation and mucus production and secretion

We next determined whether the effects of 2’FL supplementation on metabolism and gut barrier function were linked to changes in intestinal mucus. We showed that 2’FL significantly affected the expression of genes involved in GCs differentiation at different sites, with increased expression of Elf3 in caecum and Hes1 in colon, and decreased Math1 and Spdef in caecum ( figure 3A–E ). In order to determine if the mucus inside the GCs was affected by the dietary treatments, we measured the proportion of the blue area (representing the mucins) over the total mucosal area, in histological sections using an Alcian blue staining. We found 22% more blue area in HFD+2’FL compared with HFD, though this difference did not reach significance ( figure 3F,G ).

2’FL supplementation impacts on goblet cells and mucins production. (A–E) mRNA relative expression of transcriptional factors involved in the goblet cells differentiation, in the jejunum, ileum, caecum and colon: (A) atonal bHLH transcription factor 1 ( Math1 ), (B) SAM pointed domain containing ETS transcription factor ( Spdef ), (C) E74 like ETS transcription factor 3 ( Elf3 ), (D) kruppel like factor 4 ( Klf4 ), hes family basic helix-loop-helix (bHLH) transcription factor 1 ( Hes1 ). (F) Percentage of blue area on the total mucosal area in the proximal colon and (G) representative images for each group. (H–M) mRNA relative expression of markers involved in mucin production, in the jejunum, ileum, caecum and colon: (H) anterior gradient 2 ( Agr2 ), (I) mucin 2 ( Muc2 ), (J–M) mucin 1/4/13/17 ( Muc1 , Muc4 , Muc13, Muc17 ). Data are means±SEM (n=6–12/group). One-way ANOVA followed by Tukey post hoc test or Kruskal-Wallis followed by Dunn’s test were applied based on data distribution. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. 2’FL, 2’-fucosyllactose; ANOVA, analysis of variance; HFD, high-fat diet.

Next, we set out to assess whether 2’FL treatment impacts intestinal mucins. We found that 2’FL significantly affected Agr2 expression, required for the post-transcriptional synthesis and secretion of Muc2, which was decreased in caecum and increased in colon. In accordance with this observation, we also found a significant increase in Muc2 expression ( figure 3H,I ). With regard to transmembrane mucins, 2’FL supplementation led to increased Muc4 in jejunum, caecum and colon, Muc13 in jejunum and caecum, and Muc17 in caecum and colon ( figure 3J–M ). Furthermore, Muc1 and Muc13 expressions in the colon were negatively correlated with body weight and fat mass gain ( online supplemental figure 2A,B ).

Finally, we observed that dietary treatments differentially affected the expression of genes involved in intestinal mucus secretion and stabilisation. In particular, 2’FL supplementation tended to increase Retnlb in jejunum but decreased in the other intestinal segments. 2’FL supplementation increased two other key markers, Nlrp6 in caecum and colon and Fcgbp in colon while slightly counteracting the effects of the HFD on the expression of Atg5 and Atg7 ( figure 4A–E ).

2’FL increases markers of mucus secretion. (A–E) mRNA relative expression of markers involved in the secretion of the mucus layer: (A) resistin-like beta ( Retnlb ), (B) autophagy protein 5 ( Atg5 ), (C) autophagy protein 7 ( Atg7 ), (D) NOD‐like receptor family pyrin domain containing 6 ( Nlrp6 ), (E) Fc gamma binding protein ( Fcgbp ). (F) Weight of the mucus in the colon after scraping in milligrams. (G) Mucus thickness in the proximal colon measured by ImageJ (in micrometre) and (H) representative images for each group. Data are means±SEM (n=6–12/group). One-way ANOVA followed by Tukey post hoc test or Kruskal-Wallis followed by Dunn’s test were applied based on data distribution. *p<0.05, **p<0.01, ***p<0.001. 2’FL, 2’-fucosyllactose; ANOVA, analysis of variance; HFD, high-fat diet.

Although there was no difference in mucus thickness as assessed on histological sections ( figure 4G,H ), we found a significant higher weight of the mucus collected by scraping the colon in mice supplemented with 2’FL, suggesting a potential increase in mucus production ( figure 4F ).

Using a fluorescence in situ hybridisation (FISH) approach against 16S RNA to detect bacteria combined with a Muc2C3-specific staining of the mucus on colon sections, we observed an abrupt change from the inner to outer mucus layer with bacterial concentrations jumping from almost virtually free of bacteria to a high density without any perceptible gradient. The bacterial front was found to be morphologically intact in all groups. The thickness of the bacteria-free mucus was not statistically different between CT and HFD groups, though we observed a significant increase in the 2’FL treated group compared with the control group (p=0,01 Kruskal-Wallis test) ( figure 5A ).

Pictures representative of the bacterial penetration assessed by measuring the distance between the bacterial front and the epithelial cells (A, B) and the bacterial density in the inner mucus layer (C, D). (A) Mouse distal colon section in which Muc2C3 immunostaining shows the Muc2-positive mucus layer on the epithelium. The inner mucus layer (M) is almost completely devoid of bacteria, which are visualised by a FISH approach using a general bacterial probe conjugated with C3 (red), whereas the outer mucus layer contains large concentrations of bacteria with a clearly delineated bacterial front (BF). The sections are counterstained with DAPI to visualise nuclei (blue). Epithelial cells (EC) emit some autofluorescence making them visible (Scale bar: 50 µm). (B) Quantitative measurement of the spatial separation between the epithelial cells and the bacterial front. (C) Magnification (×20) of the inner mucus layer and of penetrating bacteria. Epithelial cells are on the left, while the bacterial front is on the right (scale bar: 10 µm). (D) Quantification of the bacterial density in the inner mucus layer (number of bacterial cells counted divided by the surface area of mucus). Data are means±SEM (n=7–8/group). Arrow heads show bacteria in red. Data were analysed using Kruskal-Wallis test followed by Dunn’s test. **p<0.01. FISH, fluorescence in situ hybridisation; HFD, high-fat diet.

When focusing on the apparent virtually free of bacteria inner mucus layer, we found that some bacteria, though very few, were able to penetrate it. We quantified the density by counting these cells and normalising to the area of mucus, but we found no differences between groups ( figure 5B ).

2’FL affects mucin glycan profile

To determine whether HFD and 2’FL supplementation affected mucin glycosylation, we first measured the expression of glycosyltransferases involved in elongation, branching and termination of the mucin glycan chain. We found that 2’FL significantly increased Gcnt4 , B3gnt6 and C1galt1 in colon, C1galt1c1 in caecum and colon, Fut1 in jejunum and colon, Fut8 and St3gal1 in colon, St3gal3 in jejunum and colon and St3gal6 in colon ( figure 6A–M ). Interestingly, Fut2 was decreased by the HFD in caecum and colon, but not affected by 2’FL supplementation. All the data from the mRNA expression described in the colon are schematised in figure 7 .

2’FL increases the expression of glycosyltransferases involved in mucin glycosylation. mRNA relative expression of glycosyltransferases in the jejunum, ileum, caecum and colon: (A) glucosaminyl ( N -acetyl) transferase 1 ( Gcnt1 ), (B) glucosaminyl ( N -acetyl) transferase 4 ( Gcnt4 ), (C) UDP-GlcNAc:betaGal beta-1,3- N -acetylglucosaminyltransferase 6 ( B3gnt6 ), (D) core one synthase, glycoprotein- N -acetylgalactosamine 3-beta-galactosyltransferase 1 ( C1galt1 ), (E) C1GALT1 specific chaperone 1 ( C1galt1c1 ), (F–H) fucosyltransferase 1/2/8 ( Fut1 , Fut2 , Fut8 ), (I–M) ST3 b-galactoside a-2,3-sialyltransferase 1/3/4/6 ( St3gal1 , St3gal3 , St4gal4 , St3gal6 ), (O) ST6 N -acetylgalactosaminide a-2,6-sialyltransferase 2 ( St6galnac2 ). Data are means±SEM. (n=7–12/group). Data were analysed using one-way ANOVA followed by Tukey post hoc test. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. 2’FL, 2’-fucosyllactose; ANOVA, analysis of variance; ND, not detectable.

Schematic figure summarising the expression analysis of 35 genes in the jejunum, ileum, caecum and colon. Markers involved in gut barrier function and mucins production, glycosylation and secretion measured by RT-qPCR. Markers are enclosed in small grey boxes. Purple arrows indicate those that significantly changed due to 2’FL supplementation in the colon. 2’FL, 2’-fucosyllactose.

We next analysed mucin glycosylation by tandem mass spectrometry (MS/MS) and found that two of them were significantly higher in HFD, compared with the CT and/or HFD+2’FL group ( figure 8A–D ). Figure 8E shows that 10 glycans were present in all the mice, 8 had a lower prevalence in the HFD group only or were restored following supplementation with 2’FL, and 3 were less prevalent in the HFD+2’FL group.

High-fat diet and 2’FL supplementation affects mucin glycans composition in the colon. (A) Glycan relative abundance in percentage; relative abundance of (B) sialylated glycans, (C) fucosylated glycans and (D) sulfated glycans. (E) Glycan prevalence calculated by dividing the number of mice for which the glycan was present for the total number of mice in the group. Only glycans present in at least 3 mice and in at least one group are shown. Data are means±SEM (n=4–5/group). Data were analysed using Kruskal-Wallis followed by Dunn’s test. *p<0.05. 2’FL, 2’-fucosyllactose; HFD, high-fat diet; ND, not detectable.

2’FL affects the endocannabinoid system

We previously discovered that different bioactive lipids belonging to the eCB system are able to exert control over the gut microbiota and the gut barrier function. 2 24–27 Hence, we measured caecal levels of eCBs (arachidonoylglycerol (AG) and anandamide (NAE 20:4)) and related N -acylethanolamines, and found that HFD+2’FL mice had significant lower levels of NAEs (16:1, 18:3, 20:0), LEA, OEA, PEA, DHEA and HEA, compared with CT and/or HFD mice. While, they had significant higher levels of mono-oleoylglycerol (OG) and mono-palmitoylglycerol (PG) ( figure 9A ). 2’FL affected the expression of genes involved in the biosynthesis and degradation of eCBs, by significantly upregulating Daglb and Abdh6 , and downregulating Abdh4 , Faah and Mgl ( figure 9B ).

Different caecal eCBome tone in 2’FL supplemented mice. (A) Concentrations of the eCBome-related mediators in the caecal tissue (pmol/g wet tissue weight) measured by ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS). (B) mRNA relative expression of receptors and metabolic enzymes for monoacylglycerols and N -acylethanolamines measured by RT-qPCR. Data are means±SEM (n=9–10/group). Data were analysed using one-way ANOVA followed by Tukey post hoc test. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. 2’FL, 2’-fucosyllactose; Abdh4, alpha/beta-hydrolase 4; Abdh6, α/β-Hydrolase domain-containing 6; AEA, N -arachidonoylethanolamine; AG, 2-arachidonoylglycerol; ANOVA, analysis of variance; Cb1/Cb2, cannabinoid type 1/2 receptors; DEA, N -docosatetraenylethanolamine; Dagla, diacylglycerol lipase-alpha; Daglb, diacylglycerol lipase beta; DHEA, N -docosahexaenoylethanolamine; Faah, fatty-acid amide hydrolase; Gpr119, G-protein-coupled receptor 119; HEA, N -homo-linolenylethanolamine; LEA, N -linoleylethanolamine; Mgl, monoacylglycerol lipase; Naaa, N -acylethanolamine acid amidase; NAE, N -acylethanolamine; Napepld, N-acyl phosphatidylethanolamine phospholipase D; Nat1, N-acetyltransferase 1; OEA, N- oleoylethanolamine; OG, mono-oleoylglycerol; PEA, N -palmitoylethanolamine; PG, mono-palmitoylglycerol; Pparg, peroxisome proliferator-activated receptor gamma SEA, N -stearoylethanolamine.

2’FL changes gut microbiota composition

Before the treatment all mice shared a similar faecal microbiota composition, while in the end both the faecal and caecal microbiota profiles were significantly clustered based on the diets ( figure 10A–C ). The results shown below refer to changes observed in both relative and absolute abundance.

2’FL induces changes in the caecal and faecal gut microbiota composition. Principal coordinates analysis (PCoA) plot of the gut microbiota, in which mice are grouped by treatment, based on the Bray-Curtis dissimilarity in (A) faeces before the treatment (B) faeces at the end of the treatment and (C) caecum at the end of the treatment (n=9–10/group). (D–G) Bar graphs showing grouped taxonomic profiles of the gut bacteria at the genus level: (D, E) relative and absolute abundance in the caecum, at the end of the treatment; (F, G) relative and absolute abundance in the faeces, before and at the end of the treatment (n=9–10/group). Only the bacterial genera with >1% relative abundance are shown; the rest are indicated as ‘others (<1%)’. 2’FL, 2’-fucosyllactose; HFD, high-fat diet.

At the phylum level, the caecal gut microbiota of CT and HFD groups was dominated by Desulfobacterota while HFD+2’FL by Bacteroidota and Verrucomicrobiota. In the faeces, the CT group was dominated by Bacteroidota, HFD by Desulfobacterota and HFD+2’FL by Bacteroidota and Verrucomicrobiota ( online supplemental figure 3A–D and online supplemental tables 1 and 2 ).

At the genus level, the caecal gut microbiota was enriched in uncultured Desulfovibrionaceae in the CT and HFD groups (35.1 and 51.7%, respectively), whereas Akkermansia and Bacteroides were the dominant genera in the HFD+2’FL group (39% and 24.8%, respectively) ( figure 10D,E ). Similarly, the faecal gut microbiota was dominated by uncultured Desulfovibrionaceae , Rikenellaceae RC9 gut group and Akkermansia in the CT group (19.4%, 17.3% and 18.4%, respectively), by uncultured Desulfovibrionaceae in the HFD group (38.8%), and Bacteroides and Akkermansia in the HFD+2’FL group (37.1% and 29.8%, respectively) ( figure 10F,G ) .

Notably, HFD-fed mice had significant lower levels of Akkermansia , Parasutterella , unclassified Tannerellaceae , Muribaculaceae and Rikenellaceae RC9 gut group compared with CT mice while 2’FL treatment significantly increased Akkermansia , Parasutterella , unclassified Tannerellaceae and Bacteroides compared with HFD only, in the faeces ( figure 11A–C , online supplemental table 3 ).

Bacterial genera significantly differed in absolute abundance (FDR-corrected p<0.05) in (A) HFD compared with CT (log2 fold change values calculated relative to CT), (B) HFD+2’FL compared with CT (log2 fold change values calculated relative to CT) and (C) HFD+2’FL compared with HFD (log2 fold change values calculated relative to HFD). Bar colour and bottom legend denote family-level taxonomic classification. See online supplemental table 3 for full results. 2’FL, 2’-fucosyllactose; HFD, high-fat diet.

2’FL affects bacterial glycosidases and faecal proteome

To evaluate the mucus degradation by the gut microbiota, we investigated bacterial GHs alpha-L-fucosidase and alpha-D-galactosidase by in-gel fluorescent activity-based probes (ABP) labelling. 28 29 We found ABP-labelling for alpha-L-fucosidase only in the HFD+2’FL group, with mice within this group displaying different profiles. While, alpha-D-galactosidase labelling was present in CT and HFD+2’FL, without any signals in HFD ( figure 12A ).

High-fat diet and 2’FL supplementation affects faecal proteome. (A) Cy5-ABP-labelling of alpha-L-fucosidase and alpha-D-galactosidase from mouse faecal extract (1 µg of proteins; 1 µM α-L-fucosidase and 0.5 µM α-galactosidase). Principal component analysis (PCA) of (B) faecal mouse proteins and GHs together, of (C) mouse proteins only and of (D) GHs separately. (E–G) Volcano plot comparing the different groups together, including mouse proteins and GHs. PCA and volcano plot were done with MetaboAnalyst (n=9–10/group). 2’FL, 2’-fucosyllactose; GHs, glycosyl hydrolases; HFD, high-fat diet.

To further confirm the presence of GHs, we analysed the total faecal proteome, using a bespoke database containing mouse proteins and GHs involved in mucin glycan degradation: fucosidases, galactosidases, hexosaminidases and sialidases. The principal component analysis (PCA) showed different clustering between HFD and HFD+2’FL ( figure 12B–D ). Particularly, when taking only GHs into account, CT and HFD displayed overlapping clusters, while HFD+2’FL cluster was completely separated. The volcano plot showed that HFD feeding significantly changed the abundance of 17 proteins, while 2’FL supplementation changed 12 proteins compared with CT and 30 compared with HFD ( figure 12E–G ).

Interestingly, 2’FL supplementation significantly upregulated beta-galactosidase, alpha-L-fucosidase, beta-hexosaminidase and beta- N -acetylhexosaminidase, belonging to Bacteroidales and Lachnospiraceae bacterial families ( figure 13A–H ).

2’FL supplementation affects faecal bacterial GHs and mucins. Bacterial GHs and (H) their relative protein ID and taxonomy (family). (I–L) Mucins (Muc2, Muc5ac, Muc13, Muc17). One-way ANOVA followed by Tukey post hoc test or Kruskal-Wallis followed by Dunn’s test was applied based on data distribution. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. 2’FL, 2’-fucosyllactose; ANOVA, analysis of variance; GHs, glycosyl hydrolases; HFD, high-fat diet; ND, not detectable.

In addition to changes in GHs, dietary treatments affected several faecal mucins. Indeed, Muc2 was significantly lower in HFD and HFD+2’FL groups compared with the CT group, and Muc13 and Muc17 were significantly lower in HFD+2’FL compared with CT; while Muc5ac was significantly higher in HFD+2’FL compared with HFD ( figure 13I–L ).

Taking into account the results from the Wilcoxon rank-sum test, we found that HFD significantly changed 78 proteins compared with CT, while supplementing 2’FL changed 90 proteins compared with HFD ( online supplemental table 4 ).

By executing KEGG pathway enrichment of mouse proteins, we observed that HFD feeding significantly upregulated proteins involved in protein digestion and absorption while it significantly downregulated proteins involved in carbon metabolism, biosynthesis of amino acids, metabolic pathways, fat digestion and absorption, and others ( figure 14A ). Notably, 2’FL supplementation reversed all the changes induced by HFD ( figure 14B , online supplemental figure 4 ).

KEGG pathway enrichment analysis performed by using the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. Only significant upregulated and downregulated terms (p<0.05) are shown. 2’FL, 2’-fucosyllactose; HFD, high-fat diet.

Humans proteomic

The results observed in rodents let us wondering if similar changes could be found in humans. We analysed the faecal proteome of lean and obese subjects and found that, among 133 proteins, 17 were significantly changed and increased in obese subjects ( figure 15A ; online supplemental tables 5 and 6 ). By doing functional annotation clustering, we observed that these proteins were linked to the enrichment of terms that were also enriched in HFD-fed mice ( online supplemental figure 5 , online supplemental table 7 ). Interestingly, by investigating the molecular function, biological process, KEGG pathway and disease, we found that they were involved in metabolic processes and diseases, such as type 2 diabetes ( figure 15B , online supplemental table 8 ).

(A) Results of the principal component analysis and permutational multivariate analysis of variance (PERMANOVA) for normal (n=9) and obese (n=16) human subject’s proteomes. (B) Enrichment of molecular function, biological process, KEGG pathway and disease in terms of gene ontology (GO) categories. GO categories were determined using DAVID.

Material and methods

See online supplemental materials and methods .

In this study, we found that 2’FL counteracted diet-induced obesity and metabolic alterations together with affecting mucus production, secretion and glycosylation, as well as gut microbiota composition, bacterial GHs, faecal proteome and eCB system.

Previous studies showed that 2’FL reduced energy intake, body weight or fat mass, in mice fed HFD, without affecting plasma glucose. 20 21 Here, we found that HFD-fed mice supplemented with 2’FL had significantly lower body weight gain, fat mass gain, plasma glucose and insulin levels. These effects could partially be explained by a change in different hormones involved in appetite regulation and energy metabolism. Indeed, GLP-1 and PYY were significantly higher and leptin and glucagon were significantly lower in mice supplemented with 2’FL.

HFD feeding and obesity have been associated with gut barrier disruption, increased lipopolysaccharide translocation and metabolic endotoxaemia. 1 Supplementing 2’FL to HFD has shown protective effects on markers of the gut barrier, but the mechanisms were not explored. 20 21 In this study 2’FL supplementation led to higher expression of antimicrobial peptides Lyz1 and Reg3g , and proglucagon , the precursor of GLP-1 and GLP-2, involved in improved gut barrier function, in specific sites of the gastrointestinal tract.

To further explore the mechanisms involved in gut barrier regulation, we focused on the mucus layer. In vitro studies reported that 2’FL led to enhanced MUC2 expression and secretion on human GCs during inflammatory conditions. 23 30 While, in vivo, 2′FL ameliorated colitis by recovering GC numbers and improving Muc2 expression in mice. 22 23 However, no studies investigated the effect of 2’FL supplementation on the intestinal mucus in the context of HFD feeding and obesity. Our data showed that 2’FL supplementation led to increased expression of several markers involved in GCs differentiation (eg, Elf3 and Hes1 ) and synthesis and secretion of the main component of the mucus layer (ie, Agr2 , Muc2 ). In addition, several markers related to mucus secretion and stabilisation were also increased (eg, Retnlb , Nlrp6 and Fcgbp ). These effects were linked to a higher quantity of mucus collected in the colon of mice receiving 2’FL, and with GCs more filled with mucus. The mucus penetrability to bacteria assessed by two parameters (ie, the distance from the bacterial front to the epithelial cells and the density of bacterial cells within the inner mucus layer) did not show significant differences between CT and HFD groups, though we observed an increased mucus layer thickness in the 2’FL treated group compared with the CT, suggesting that 2’FL protects the epithelium against bacteria penetration.

In addition to the secreted mucins, other important components of the gut barrier are transmembrane mucins. We found that 2’FL supplementation significantly upregulated the expression of Muc4 , Muc13 and Muc17 in different intestinal compartments. Interestingly, Muc1 and Muc13 expressions in the colon were negatively correlated with body weight and fat mass gain ( online supplemental figure 2A,B ), suggesting their potential involvement in metabolic processes. However, their role in the context of obesity and metabolic disorders is still unknown, as only a few studies focused on these aspects. Two studies showed that 2′FL impacted glycocalyx average thickness and increased mRNA levels of Muc1 in the presence of Escherichia coli challenge. 31 32 To our knowledge, no other data on transmembrane mucins and 2’FL are available.

The composition of mucin glycans in the intestine has been shown to be important for microbial colonisation. 10 Glycosyltransferases are the enzymes responsible for mucin glycosylation and it has been suggested that they are affected by dietary treatments, which could impact Muc2 glycosylation. 33 Here, we found that 2’FL supplementation significantly affected the expression of glycosyltransferases, mainly in the colon where 9 out of 13 of them were upregulated. Such changes were also observed in a previous study following supplementation of mice with fructooligosaccharides. 34 Interestingly, we found that Fut2 expression in the colon negatively correlated with body weight gain and fat mass gain ( online supplemental figure 2C ). Together with previous studies showing that Fut2 mutation led to liver disease, these findings suggest that it could probably be involved in metabolic processes. 35 36

Based on these results, we asked whether mucin glycan composition could be affected by dietary intervention. Previous studies showed that HFD alone altered mucin glycosylation, by increasing the sialo/sulfomucin ratio, altering lectin-binding pattern and overexpressing galβ1,3galnac terminal dimers. 5 Here, we observed that 2 out of 24 mucin glycans identified were significantly higher in HFD compared with the CT and/or HFD+2’FL groups while others did not reach statistical significance, probably due to the limited number of mice analysed. Notably, we found that among the 10 mucin glycans present in all mice, 8 had a lower prevalence only in HFD or were restored by 2’FL supplementation and 3 were less prevalent in the HFD+2’FL group. The ‘restoration’ of glycans prevalence by 2’FL suggests that the prebiotic treatment can be used to counteract alterations induced by HFD. In healthy humans, MUC2 O-glycosylation is uniform while it is altered in patients with active ulcerative colitis and associated with increased inflammation. 37 38 A different profile was also observed in human colon cancer, linked to tumour metastatic potential and poor prognosis. 39 40 Understanding the pattern of mucin glycosylation in patients with obesity and metabolic disorders, and how these could be modulated by nutritional treatments, could be useful in inducing the colonisation of specific bacteria associated with beneficial effects. To date, progress has been impeded by the scarcity of research in humans, which is hampered by the need for invasive methods such as biopsy collection. 41 Surprisingly, a recent study showed that the mucus structure on freshly excreted faecal pellets was identical to that of the faecal pellets in the corresponding colon tissue, suggesting that faecal-associated mucus may support noninvasive strategies for disease diagnosis in humans. 42

In addition to proteins and enzymes, lipid mediators also play an important role in regulating energy homeostasis. Among them, the eCB system has been shown to regulate energy, glucose and lipid metabolism, gut barrier function and microbiota–host interactions. 43 Here, we found that mice receiving 2’FL had significantly lower levels of NAEs and decreased expression of Abdh4 and Mgl . In contrast, mice receiving 2’FL showed increased levels of OG and PG compared with the CT and HFD groups. These bioactive lipids were previously reported to be significantly increased in the colon of mice and in the blood of obese humans treated with Akkermansia muciniphila , both exhibiting an improved gut barrier, lower inflammation and improved glucose metabolism. 2 44–46 In addition, mono-OG has been shown to stimulate GLP-1 secretion and improve glucose metabolism. 47 Short-chain fatty acids (SCFAs) have also been shown to stimulate the secretion of GLP-1 and PYY. However, we did not find any increase in butyrate, propionate and acetate in the caecal content but rather a significant decrease of several SCFAs and branched SCFA after 2’FL treatment ( online supplemental figure 6 ). These findings indicate that 2’FL could affect the metabolism and the mucus layer by acting through the eCB system.

While in vitro studies have demonstrated that alterations of the mucus layer may be directly mediated by 2’FL, we cannot exclude the possibility that, in vivo, they are part of a more complex system involving the gut microbiota. As a prebiotic compound, 2’FL can be metabolised by gut bacteria, stimulating, therefore, the proliferation of specific bacterial groups. By analysing the gut microbiota composition, we observed significant clustering according to the diet, with 2’FL supplementation inducing the most significant changes. Specifically, CT and HFD-fed mice were dominated by uncultured Desulfovibrionaceae , while Akkermansia and Bacteroides were the main bacterial genera in mice receiving 2’FL supplementation. Both genera have been shown to be able to repurpose their mucin degradation machinery for the breakdown of HMOs, reflecting the structural and compositional similarities between HMOs and mucin oligosaccharides. 48 49 It is, therefore, plausible that the effects of 2’FL on metabolism and mucus could be mediated by bacteria that were significantly affected by the treatment. For example, A. muciniphila , a species belonging to the genus Akkermansia , is a mucin-degrading specialist residing and proliferating in the mucus layer and affecting metabolism in mice and humans. 46 50 51 In mice, A. muciniphila was previously reported to counteract HFD-induced obesity and prevent the decrease of mucus layer thickness associated with HFD. 2 On the other hand, Bacteroides spp, like B. uniformis and B. acidifaciens , have been found to be protective against obesity 52–54 and are highly enriched in colonic mucus layer where they can use mucin glycans as energy source. 55 Moreover, WSD-fed rodents display an impaired mucus layer associated with a lower abundance of Bacteroidetes 3 and B. thetaiotaomicron , increased GC differentiation, increased expression of mucus-related genes and sialylated/sulfated mucins ratio. 56

Since mice supplemented with 2’FL showed increased mucus production and secretion but no changes in mucus thickness, we next assessed whether 2’FL may have stimulated mucus degradation through enhanced bacterial GH activity. Using in-gel fluorescent ABP labelling, we found increased a-L-fucosidase and a-D-galactosidase activity in 2’FL supplemented mice. The PCA for GHs involved in mucus degradation showed distinct clustering patterns between mice fed HFD+2’FL and those fed CT and HFD diet. Among GHs, two b-D-galactosidases and two a-L-fucosidases, assigned to Bacteroidales and Lachnospiraceae families, were increased in 2’FL supplemented mice while no differences were observed between CT and HFD groups, where these enzymes were either scarce or absent. Furthermore, we found that 2’FL supplementation significantly increased the levels of b-hexosaminidase and b- N -acetylhexosaminidase compared with the CT diet. These results suggest that 2’FL stimulates the production of bacterial GHs involved in mucin glycan degradation, perhaps prompted by their involvement in 2’FL degradation.

Further analysis of the mouse proteome showed that HFD had a profound impact on many proteins participating in metabolic processes. Specifically, HFD upregulated proteins involved in protein digestion and absorption while downregulated those involved in different metabolic pathways, among others. Interestingly, 2’FL supplementation had opposite effects, suggesting its ability to counteract the alteration of metabolic processes induced by the HFD. To gain further insights into the mouse results, we analysed the faecal proteome from obese and lean individuals. We showed that there were significant differences, as previously observed, 57 with some of these changes being similar between HFD-fed mice and obese humans. In addition to obese and lean individuals, even lifestyle-induced weight loss affects proteome. 57 58 In other clinical contexts, faecal proteome has been used to discriminate patients with adenomas and colorectal cancer and novel stool biomarkers have been proposed for early detection, aiming at reducing their incidence and mortality. 59–61 This methodology may be used to define new clinical biomarkers capable of detecting the onset of metabolic disorders, enabling their prevention and monitoring the effectiveness of prebiotic/probiotic treatments or personalised dietary interventions in humans for improving individualised patient care and public health outcomes.

In conclusion, our study demonstrates that 2’FL supplementation in the context of HFD-feeding can counteract obesity and metabolic alterations and it is associated with alterations in the intestinal mucus layer through increased expression of secreted and transmembrane mucins, glycosyltransferases and alterations in mucin glycans composition. These changes were accompanied with different profiles of gut microbiota, faecal proteome and eCB system. Our findings suggest that 2’FL has the potential to improve metabolic outcomes in overweight/obese individuals and highlights the importance of investigating the interaction between mucus and gut microbiota. Together these data pave the way for further research on novel strategies and targets for the prevention and/or treatment of obesity and related disorders.

Ethics statements

Patient consent for publication.

Consent obtained directly from patient(s).

Ethics approval

The mouse experiments were approved by and performed following the guidelines of the local ethics committee for animal care of the Health Sector of the Université catholique de Louvain under the specific agreement number 2017/UCL/MD/005. Animal housing conditions were as specified by the Belgian Law of 29 May 2013 regarding the protection of laboratory animals (Agreement number LA 1230314). This study involves human participants and human faecal samples were obtained from a previous study (Kolmeder et al, ref 57) and this observational study was approved by the Medical Ethics Committee of the Atrium Medical Center (Heerlen, the Netherlands; registration number NL30502.096.09). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

We thank H. Danthinne, B. Es Saadi, L. Gesche and R. M. Goebbels (at UCLouvain, Université catholique de Louvain) for their excellent technical support and assistance. We thank A. Daumerie from the IREC imagery platform (2IP) from the Institut de Recherche Expérimentale et Clinique (IREC) for the excellent help. We thank NK Nguyen for performing PCA in figure 15A. Figure 7 was created using BioRender.com. F. Suriano is currently employee at European Food Safety Authority, Via Carlo Magno 1A, 43126, Parma, Italy.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1
• Data supplement 2
• Data supplement 3
• Data supplement 4
• Data supplement 5
• Data supplement 6
• Data supplement 7
• Data supplement 8
• Data supplement 9
• Data supplement 10
• Data supplement 11

AS, NJ, BIF, GGM, HO and MVH are joint senior authors.

X @Paola_9229, @JugeLab, @matthias_vanhul

Contributors PP and PDC conceived and designed the study. PP performed the experiments and data analysis. AP performed analysis. PP and PDC performed the interpretation. PP prepared the samples for sequencing. PP and CJ processed the sequences and performed the bioinformatics and statistical analysis for the gut microbiota. DL and NJ performed the mucin glycans composition analysis and PP and PDC interpreted the results. RT and GGM performed the analysis of lipids and endocannabinoids. MEVJ and FS helped for the mucus protocol. CB, MVH and AP helped for all the histological analysis. VB helped for ABP-labelling. BIF contributed for proteomic, Nano-LC-MS settings for pulldown samples and MaxQuant processing. DV contributed for processing MS/MS data from MASCOT Generic Format files from human faecal proteomes. PP performed proteomic analysis and interpretation in Perseus, MetaboAnalysist and DAVID. PDC and HO contributed to financial resources. PDC and MVH supervised the lab work. PP and PDC wrote the first version of the paper. All authors critically revised the manuscript and approved the final version before submission. PDC is the guarantor of this study.

Funding European Union’s Horizon 2020 research and innovation program (H2020 Marie Skłodowska-Curie Actions under the agreement no: 814102 ITN H2020 MSCA Sweet Crosstalk). PDC is honorary research director at Fonds de la Recherche Scientifique (FNRS) and is recipients of grants from FNRS (Projet de Recherche PDR-convention: FNRS T.0030.21, CDR-convention: J.0027.22, FRFS-WELBIO: WELBIO-CR-2019C-02R, WELBIO-CR-2022A-02, EOS: program no. 40007505). Fédération Wallonie-BruxellesARC19/24-096. La Caixa foundation (NeuroGut).

Competing interests PDC is an editor of the journal. PDC is inventor on patent applications dealing with the use bacteria on metabolic disorders. PDC was cofounders of The Akkermansia company SA and Enterosys.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Perioperative Management of Patients Taking Direct Oral Anticoagulants : A Review

• 1 Department of Medicine, St. Joseph’s Healthcare Hamilton, and McMaster University, Thrombosis and Atherosclerosis Research Institute, Hamilton, Ontario, Canada
• 2 Department of Medicine, Anticoagulation and Clinical Thrombosis Service, Northwell Health at Lenox Hill Hospital, New York, New York
• 3 The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
• 4 Institute of Health System Science at the Feinstein Institutes for Medical Research, Manhasset, New York

Importance   Direct oral anticoagulants (DOACs), comprising apixaban, rivaroxaban, edoxaban, and dabigatran, are commonly used medications to treat patients with atrial fibrillation and venous thromboembolism. Decisions about how to manage DOACs in patients undergoing a surgical or nonsurgical procedure are important to decrease the risks of bleeding and thromboembolism.

Observations   For elective surgical or nonsurgical procedures, a standardized approach to perioperative DOAC management involves classifying the risk of procedure-related bleeding as minimal (eg, minor dental or skin procedures), low to moderate (eg, cholecystectomy, inguinal hernia repair), or high risk (eg, major cancer or joint replacement procedures). For patients undergoing minimal bleeding risk procedures, DOACs may be continued, or if there is concern about excessive bleeding, DOACs may be discontinued on the day of the procedure. Patients undergoing a low to moderate bleeding risk procedure should typically discontinue DOACs 1 day before the operation and restart DOACs 1 day after. Patients undergoing a high bleeding risk procedure should stop DOACs 2 days prior to the operation and restart DOACs 2 days after. With this perioperative DOAC management strategy, rates of thromboembolism (0.2%-0.4%) and major bleeding (1%-2%) are low and delays or cancellations of surgical and nonsurgical procedures are infrequent. Patients taking DOACs who need emergent (<6 hours after presentation) or urgent surgical procedures (6-24 hours after presentation) experience bleeding rates up to 23% and thromboembolism as high as 11%. Laboratory testing to measure preoperative DOAC levels may be useful to determine whether patients should receive a DOAC reversal agent (eg, prothrombin complex concentrates, idarucizumab, or andexanet-α) prior to an emergent or urgent procedure.

Conclusions and Relevance   When patients who are taking a DOAC require an elective surgical or nonsurgical procedure, standardized management protocols can be applied that do not require testing DOAC levels or heparin bridging. When patients taking a DOAC require an emergent, urgent, or semiurgent surgical procedure, anticoagulant reversal agents may be appropriate when DOAC levels are elevated or not available.

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Douketis JD , Spyropoulos AC. Perioperative Management of Patients Taking Direct Oral Anticoagulants : A Review . JAMA. Published online August 12, 2024. doi:10.1001/jama.2024.12708

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• Published: 12 August 2024

Micropapillary breast carcinoma in comparison with invasive duct carcinoma. Does it have an aggressive clinical presentation and an unfavorable prognosis?

• Yasmine Hany Abdel Moamen Elzohery 1 , 5 ,
• Amira H. Radwan 2 , 5 ,
• Sherihan W. Y. Gareer 2 , 5 ,
• Mona M. Mamdouh 3 , 5 ,
• Inas Moaz 4 , 5 ,
• Abdelrahman Mohammad Khalifa 5 ,
• Osama Abdel Mohen 5 ,
• Mohamed Fathy Abdelfattah Abdelrahman Elithy 5   nAff6 &
• Mahmoud Hassaan 5   nAff7  

BMC Cancer volume  24 , Article number:  992 ( 2024 ) Cite this article

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Metrics details

Invasive micropapillary carcinoma (IMPC) was first proposed as an entity by Fisher et al. In the 2003 World Health Organization (WHO) guidelines for histologic classification of the breast tumors. IMPC was recognized as a distinct, rare histological subtype of breast cancer.

IMPC is emerging as a surgical and oncological challenge due to its tendency to manifest as a palpable mass, larger in size and higher in grade than IDC with more rate of lymphovascular invasion (LVI) and lymph node (LN) involvement, which changes the surgical and adjuvant management plans to more aggressive, with comparative prognosis still being a point of ongoing debate.

Aim of the study

In this study, we compared the clinicopathological characteristics, survival and surgical management of breast cancer patients having invasive micropapillary carcinoma pathological subtype in comparison to those having invasive duct carcinoma.

This is a comparative study on female patients presented to Baheya center for early detection and treatment of breast cancer, in the period from 2015 to 2022 diagnosed with breast cancer of IMPC subtype in one group compared with another group of invasive duct carcinoma. we analyzed 138 cases of IMPC and 500 cases of IDC.

The incidence of LVI in the IMPC group was 88.3% in comparison to 47.0% in the IDC group (p < 0.001). IMPC had a higher incidence of lymph node involvement than the IDC group (68.8% and 56% respectively). IMPC had a lower rate of breast conserving surgery (26% vs.37.8%) compared with IDC.

The survival analysis indicated that IMPC patients had no significant difference in overall survival compared with IDC patients and no differences were noted in locoregional recurrence rate and distant metastasis rate comparing IMPCs with IDCs.

The results from our PSM analysis suggested that there was no statistically significant difference in prognosis between IMPC and IDC patients after matching them with similar clinical characteristics. However, IMPC was found to be more aggressive, had larger tumor size, greater lymph node metastasis rate and an advanced tumor stage.

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Introduction

Breast cancer is the most common cancer in women. In the 2012 World Health Organization (WHO) classification of breast cancer. Breast Cancer is classified into up to 21 different histological types depending on cell growth, morphology and architecture patterns [ 1 ]. The invasive carcinoma of no special type (IBC-NST), which is known as invasive ductal carcinoma (IDC), is the most frequently occurring histological type, which constitutes around 75% of invasive breast carcinoma [ 2 ].

Invasive micropapillary carcinoma (IMPC) was first proposed as an entity by Fisher et al. in 1980 [ 3 ] and first described as the term “invasive micropapillary carcinoma” by Siriaunkgul et al. [ 4 ] in 1993.

In the 2003 World Health Organization (WHO) guidelines for histologic classification of the breast tumors [ 5 ]. IMPC was recognized as a distinct, rare histological subtype of breast cancer. While micropapillary histological architecture is present in 2–8% of breast carcinomas, pure micropapillary carcinoma is uncommon and accounts for 0.9–2% of all breast cancers [ 6 ].

IMPC exhibits more distinct morphologic architecture than the IDC, characterized by pseudopapillary and tubuloalveolar arrangements of tumor cell clusters in clear empty sponge-like spaces that resemble extensive lymphatic invasion [ 7 ]. The neoplastic cell exhibits an “inside-out” pattern, known as the reverse polarity pattern [ 2 ].

Most studies demonstrate that the radiological findings of IMPC are irregular-shaped masses with an angular or spiculated margin on ultrasound, mammography and MRI with heterogeneous enhancement and washout kinetics on MRI [ 8 ].

IMPC had tendency to manifest as a palpable mass, larger in size and higher in grade than IDC with more rate of lymphovascular invasion (LVI) and lymph node (LN) involvement, which changes the surgical and adjuvant management plans to more aggressive, with comparative prognosis still being a point of ongoing debate [ 9 ].

In this study, we compared the clinicopathological characteristics, survival and surgical management of breast cancer patients having invasive micropapillary carcinoma pathological subtype in comparison to those having invasive ductal carcinoma.

Patient and method

This is a comparative study on female patients presented to Baheya center for early detection and treatment of breast cancer, in the period from 2015 to 2022 diagnosed with breast cancer of IMPC subtype in one group compared with another group of invasive duct carcinoma.

This retrospective study analyzed 138 cases of IMPC and 500 cases of IDC. Informed consent was obtained from all patients. Ethical approval is obtained from Baheya center for early detection and treatment of breast cancer and National research center ethics committee. Baheya IRB protocol number:202305150022.

The following clinical-pathological features were analyzed for each case: patient age at diagnosis, clinical presentation, laterality, imaging findings, histopathological examination, treatment plan with either primary surgical intervention or other treatment protocol according to tumor stage and biological subtypes.

A breast pathologist evaluated the tumor size, type, grade, lymphovascular invasion, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) receptor and the axillary lymph node involvement.

According to the ASCO/CAP guideline update, 2019: Samples with 1% to 100% of tumor nuclei positive for ER or progesterone receptor (PgR) are interpreted as positive. If ER (not PgR), 1% to 10% of tumor cell nuclei are immunoreactive, the sample are reported as ER Low Positive. There are limited data on the overall benefit of endocrine therapies for patients with low level (1%-10%) ER expression, but they currently suggest possible benefit, so patients are considered eligible for endocrine treatment. A sample is considered negative for ER or PgR if < 1% or 0% of tumor cell nuclei are immunoreactive [ 10 ]. An Allred score between 0 and 8. This scoring system looks at what percentage of cells test positive for hormone receptors, along with how well the receptors show up after staining, called intensity: proportion of cells staining (0, no staining; 1, < 1%; 2, between 1 and 10%; 3, between 11 and 33%; 4, between 34 and 66% and 5, between 67%–100% of the cells staining). Intensity of positive tumor cells (0, none; 1, weak, 2, intermediate; and 3, strong) [ 11 ].

HER2 Test Guideline IHC Recommendations, 2018. IHC 0: as defined by no staining observed or membrane staining that is incomplete and is faint/barely perceptible and within <  = 10% of the invasive tumor cells. IHC 1 + : as defined by incomplete membrane staining that is faint/barely perceptible and within > 10% of the invasive tumor cells. IHC 2 + : The revised definition of IHC 2 + (equivocal) is weak to moderate complete membrane staining observed in > 10% of tumor cells. IHC 3 + : based on circumferential membrane staining that is complete, intense in > 10% of tumor cells. [ 12 ].

ASCO–CAP HER2 SISH Test Guideline Recommendations,2018 Twenty nuclei (each containing red (Chr17) and black (HER2) signals) should be enumerated. The final results for the HER2 status are reported based on the ratio formed by dividing the sum of HER2 signals for all 20 nuclei divided by the sum of Chromosome 17 signals for all 20 nuclei. The amplification status is defined as Amplified if the HER2/Chromosome 17 ratio > / = 2.0 and the average Her2 gene copy number is > / = 4.0. It is non-Amplified if the HER2/Chromosome 17 ratio < 2.0 with the Her2 gene copy number is < 4.0. If the HER2/Chr17 ratio is < 2 and the Her2 gene copy number is between 4.0 and 6.0, or, HER2/Chr17 ratio is > / = 2 and the Her2 gene copy number is < 4, or HER2/Chr17 ratio is < 2 and the Her2 gene copy number is > / = 6.0, an additional work should be done. [ 12 ].

Follow-up duration was calculated from the date of diagnosis to the date of the last follow-up. Patients still alive at the last follow-up censored or to the date of occurrence of any event or death.

Disease-free survival was defined as the duration (months) from the initial diagnosis of breast cancer to first any type of recurrence (invasive ipsilateral breast tumor recurrence, local invasive recurrence, regional invasive recurrence, invasive contra lateral breast cancer, distant metastasis.

Overall survival (OS) is defined as the time from diagnosis of breast cancer to death from any cause.

Data were statistically analyzed using an IBM-compatible personal computer with Statistical Package for the Social Sciences (SPSS) version 23. Quantitative data were expressed as mean, standard deviation (SD) and range (minimum–maximum). Qualitative data were expressed as Number (N) and percentage (%), while A P value of < 0.05 was statistically significant. For comparison of unmatched data, chi-square tests were used for categorical variables and t-tests or Mann–Whitney tests for continuous variables.

In this study, we analyzed 138 cases of IMPC which presented to our center in the period from 2015 to 2022.We included a total number of 500 cases of IDC as controls with a ratio of controls to cases 4:1.

Propensity score matching (PSM) is a method for filtrating experimental and control cases of similar characteristics, which are called the matching variables, from existing data to make them comparable in a retrospective analysis. PSM reduce the effect of selection bias. So, the comparison of outcomes between two groups can be fair.

The variables for propensity score matching were selected as follows: age (years), tumour size (cm), nodal status, HR status and HER2 status.

To diminish the effects of baseline differences and potential confounds in clinical characteristics and patients across histology subtypes for outcome differences (disease-free survival and overall survival), PSM method was applied with each micropapillary patient matched to one IDC patient who showed similar baseline characteristics in terms of: menopausal status, comorbidities, multiplicity, histologic grade, tumor size, stage, nodal status, ER /PR status. Differences in prognosis were assessed by Kaplan–Meier analysis.

Most of the patients were postmenopausal, the mean age of patients in IMPC group was 57.36 ± 11.321 years while the mean age of the IDC group was 56.63 ± 9.719 years ( p  = 0.45) (Table 1 ).

The most common presentation of IMPC on breast mammography was an irregular shaped mass with a non-circumscribed spiculated margin. while, the most common sonographic finding of IMPC was hypoechoic mass with irregular shapes and spiculated margins. Associated microcalcifications were found in 49 patients (35.5%) of IMPC group. Figs. ( 1 , 2 ): Radiological characteristics of IMPC.

A , B 37-years-old female patient presented with Left breast UOQ extensive fine pleomorphic and amorphous calcifications of segmental distribution, with UOQ multiple indistinct irregular masses. C ultrasound showed left breast UOQ multiple irregular hypoechoic masses with calcific echogenic foci, the largest is seen at 1 o’clock measuring 13 × 15mm. Intraductal echogenic lesions are noted

A , B , C 40-years-old female patient presented with left UOQ extensive pleomorphic microcalcifications of segmental distribution reaching the areola, with multiple well-circumscribed small obscured masses. D , E complementary Ultrasound showed left 2 o’clock multiple ill-defined and well-defined hypoechoic masses (BIRADS 5)

All patients underwent axillary sonography where 77 patients (55.8%) of the IMPC group exhibited pathological lymph nodes and 18 patients (13%) had indeterminate lymph nodes demonstrating preserved hila and associated with either a symmetrical increase of their cortical thickness reaching 3mm or with a focal increase in the cortical thickness.

Multiple lesions were detected in 30% of IMPC patients in comparison to 7% of IDC patients. Intra-ductal extension with nipple involvement was found in 44 patients (31.9%) of the IMPC group (Table 2 ).

MRI was done for 5 cases (3.6%), while CESM was performed for 18 cases (13%) of the IMPC group, the commonest presentation of IMPC in contrast study was irregular shaped enhanced mass in 21 patients and non-mass enhancement was found in 5 patients. Figs. ( 3 , 4 ).

Further imaging modalities. A , B , C 60-years-old female patient had right breast irregular hypoechoic solid mass by ultrasound (BIRADS 5). D , E CESM showed a right breast irregular heterogeneously enhancing solid mass

Role of CESM in diagnosis of IMPC patients. A , B 42-years-old patient presented with a left LIQ irregular spiculated mass with suspicious microcalcifications, other similar lesions were seen anterior and posterior at the same line. C Ultrasound showed a heterogeneously hypoechoic irregular mass with a spiculated outline with multiple similar satellite lesions were seen anterior and posterior to the main lesions

The average tumor size in the IMPC and IDC groups was 3.37 ± 2.04 cm and 2.72 ± 1.39 cm, respectively ( P  < 0.001).

The percentage of tumors larger than 5cm, was reported 9.5% in IMPC and 7.4% in IDC.

The pure form of IMPC was the most common type and found in 90 cases (65%) and 47 cases (34%) were mixed type where IDC was the commonest associated type.

There are 6 cases in the IMPC group diagnosed as invasive mucinous carcinoma on biopsy, then in the specimen was mixed invasive micropapillary, IBC-NST and invasive mucinous carcinoma.

On core biopsy, 28 cases were diagnosed as IMPC with focal IDC component, but in corresponding specimens 10 cases were only approved to be mixed invasive micropapillary and invasive duct carcinoma, while others diagnosed as pure invasive micropapillary carcinoma without IDC component.

On the other hand, 48 of our cases were diagnosed as IDC on core biopsy, but in the final specimen examination, 17 of these cases were diagnosed as pure invasive micropapillary carcinoma without invasive ductal component.

The explanation of controversy in proper histologic subtyping of carcinoma on core biopsy and the definite subtype on the corresponding specimen was that the ductal component which only represented in the biopsy is a very minor component of the tumor or the limited sampling, tissue fragmentation and architecture distortion in core biopsy may cause diagnostic pitfalls as regard precise subtyping of the tumor.

The incidence of LVI in the IMPC group was 88.3% in comparison to 47.0% in the IDC group ( p  < 0.001).

IMPC had a higher incidence of lymph node involvement than the IDC group (68.8% and 56% respectively) with N3 stage reported in 12.4% of IMPC patients.

IMPC had a higher nuclear grade than the IDC group (25.1% and 15.2% respectively).

The percentage of ER-positive patients was 97.8% in the IMPC group and 87.6% in the IDC group ( p  < 0.001), while PR-positive cases were 98.6% in the IMPC group and 88.8% in the IDC group ( p  < 0.001). HER2 status was positive in 4.3% of IMPCs and 8% of IDCs ( p  = 0.23) (Table 3 ) (Figs. 5 ,  6 ).

A case of invasive micropapillary carcinoma. A case of invasive micropapillary carcinoma, grade II. A Tissue core biopsy, × 100, B MRM specimen × 100 with Positive metastatic L. nodes 2/15, C ER is positive in > 90% of tumor cells, × 100, D PR is positive in > 90% of tumor cells, × 400, E HER2/neu is negative, × 400 and F) Ki-67 labelling index is high, × 200. This case was considered as luminal type pure invasive micropapillary carcinoma. (100 micron 20__ 50 micron 40)

A case of invasive duct carcinoma. A case of invasive duct carcinoma, grade II. A Tissue core biopsy, × 100, B MRM specimen, × 200 with negative L. nodes 0/16, C ER is positive in > 90% of tumor cells, × 200, D PR is positive in > 90% of tumor cells, × 100, E HER2/neu is negative, × 400. This case was considered as luminal type pure invasive duct carcinoma

Regarding definitive surgical management, IMPC had a lower rate of breast conserving surgery (26% vs.37.8%) compared with IDC. While, 49.3% of IMPC patients underwent modified radical mastectomy in comparison to 46% of the IDC patients. Such high incidence of mastectomy was due to the advanced stage at presentation, presence of multiple lesions and presence of intra-ductal extension with nipple involvement.

The incidence of re-surgery in the IMPC group was only in 3 cases, two of them underwent completion mastectomy after the initial conservative breast surgery and axillary clearance. While one patient underwent wider margin excision as positive margin for an invasive residual disease was found.

Two patients in the IMPC group had distant metastasis at the initial diagnosis, they had multiple metastatic lesions and received systemic treatment but one of them underwent palliative mastectomy.

Systemic chemotherapy was administered to 107 patients (77.5%) in the IMPC group and to 207 patients (41%) in the IDC group. Hormonal therapy was administered to all IMPC patients and 76% patients in the IDC group (Table 4 ).

The overall median follow-up duration was 21 months (range 6 – 88 months) with mean follow up duration = 29.8months.

Among the 138 IMPC patients, local recurrence developed in 3 cases, they developed a recurrence at 6,18 and 48 months postoperative. Distant metastasis developed in 5 patients in the form of bone, lung, hepatic and mediastinal lymph node metastasis.

The survival analysis indicated that IMPC patients had no significant difference in overall survival compared with IDC patients and no differences were noted in locoregional recurrence rate comparing IMPCs with IDCs (2.2% and 0.4% respectively). P value for local recurrence = 0.12 (yates corrected chi square).

Distant metastasis rate comparing IMPCs with IDCs was (3.7% and 5.4% respectively). P value for distant metastasis = 0.53 (Table 5 ).

Comparison of OS between IDC and micropapillary cases (Matched by propensity score matching -PSM).

Case Processing Summary

Overall Comparisons

• Test of equality of survival distributions for the different levels type

Disease free survival

• Test of equality of survival distributions for the different levels of type

IMPC is a highly invasive type of breast cancer. Hashmi A.A. et al. [ 13 ] found that the incidence of IMPC is very low accounting for 0.76–3.8% of breast carcinomas.

Shi WB et al.; [ 7 ] in a study comparing 188 IMPC cases and 1,289 invasive ductal carcinoma (IDC) cases from China showed that IMPC can occur either alone or mixed with other histological types, such as ductal carcinoma in situ, mucinous carcinoma and IDC. Furthermore, the majority of patients had mixed IMPC.

Fakhry et al. [ 14 ] reported that 64.7% of IMPC patients were pure type. In our study, we found that the pure form of IMPC was the commonest type and presented in 90 patients (65%) and 47 cases (34%) were mixed type which was similar to that reported by Nassar et al. [ 15 ], and Guo et al. [ 16 ] in their studies.

In our study, the commonest finding of IMPC on breast mammography was an irregular shaped mass with a non-circumscribed spiculated margin. While, the commonest sonographic finding of IMPC was hypoechoic mass with irregular shapes and spiculated margins.

These findings were similar to the results demonstrated by Jones et al., [ 17 ] which found that the commonest morphologic finding of IMPC was an irregular high-density lesion (50% of patients) with spiculated margin (42% of patients). However, Günhan-Bilgen et al. [ 18 ] reported that an ovoid or round lesion was found in 53.8% of patients.

Alsharif et al., [ 19 ] reported that the commonest sonographic finding of IMPC was hypoechoic masse (39/41, 95%) with irregular shape (30/41, 73.2%) and angular or spiculated margin (26/41, 63.4%).

In our study, MRI was done for 5 cases (3.6%), while CESM was performed for 18 cases (13%) of the IMPC group, the commonest presentation of IMPC in contrast study was irregular shaped enhanced lesion in 21 cases and non-mass enhancement was presented in 5 cases.

Nangogn et al. [ 20 ] and yoon et al. [ 8 ] recorded that the commonest finding of IMPCs in MRI was spiculated irregular mass with early rapid initial heterogenous enhancement, indicating that the MRI findings correlated with the invasiveness of IMPC.

Fakhry et al. [ 14 ] conducted a study on 68 cases, out of which 17 cases underwent CEM. In all of these cases, the masses showed pathological enhancement, which was either in the form of mass enhancement (12/17 patients, 70.6%) or non-mass enhancement (4/17 patients, 23.5%). The majority of the enhanced masses were irregular in shape (11/12 patients, 91.7%).

All patients underwent axillary sonography and 77 patients (55.8%) of the IMPC group exhibited pathological lymph nodes; this percentage was similar to that recorded by Nangong et al. [ 20 ] which was 54.8% and lower than that recorded by Jones et al. [ 17 ] but higher than that of Günhan et al. [ 18 ] which were 67% and 38% respectively.

Günhan et al. [ 18 ] reported microcalcification in about 66.7% of the cases. In our study, associated microcalcifications were found in 49 patients (35.5%) of the IMPC group. Yun et al. [ 21 ] and Adrada et al. [ 22 ] showed a fine pleomorphic appearance (66.7% and 68%).

Hao et al. [ 23 ] compared the rate of tumors larger than 5cm, reporting 3% in IDC and 4.3% in IMPC. In our study, the rate of tumors larger than 5cm, was reported 7.4% in the IDC patients and 9.5% in the IMPC patients.

Yu et al., et al. [ 24 ] documented in a study comparing 72 cases of IMPC and 144 cases of IDC of the breast that IMPC had a higher nuclear grade than IDC (52.8% vs. 37.5% respectively). In our study, IMPC had a higher nuclear grade than the IDC group (25.1% and 15.2% respectively).

Verras GI et al.; [ 9 ] demonstrated that IMPC was an aggressive breast cancer subtype with a great tendency to lymphovascular invasion and lymph node metastasis. In our study, the incidence of LVI in the IMPC patients was 88.3% in comparison to 47.0% in the IDC patients ( p  < 0.001). Tang et al., [ 25 ] also reported that lymphovascular involvement was more common among the IIMPC group than IDC group, with a percentage of 14.7% compared to only 0.1% in the IDC group.

Also, Shi et al. [ 7 ] reported that LVI was detected in 74.5% of cases. Furthermore, the frequency of LVI was found to be greater in IMPC cases when compared to IDC cases. Jones et al., [ 17 ] recorded angiolymphatic invasion in 69% of cases.

Hashmi et al. [ 13 ] reported in his comparative study that nodal involvement was present in 49.5% of IDC patients and N3 stage was only 15.6% in IDC patients compared to 33% in IMPC patients. In our study, the percentage of lymph node involvement of IMPC and IDC patients were 68.8% and 56% respectively with N3 stage reported in 12.4% of IMPC patients.

Guan et al. [ 26 ], Lewis et al., [ 27 ], Pettinato et al., [ 28 ] and De La Cruz et al., [ 29 ] recorded a higher percentage of lymph node metastasis in IMPC patients, reaching 90%, 92.9%,55.2% and 60.9% respectively.

The management of IMPC remains controversial, particularly among breast surgeons. Modified radical mastectomy was the preferred surgical procedure for the majority of IMPC case reports, as found in a study conducted by Yu et al., [ 24 ] where 99% of IMPC cases underwent modified radical mastectomy. Fakhry et al. [ 14 ] reported that 76.5% of the patients underwent modified radical mastectomy. In our study, 49.3% of IMPC patients received modified radical mastectomy.

IMPC patients were also prone to accept BCS rather than mastectomy in the previous series conducted by Lewis GD,et al. [ 27 ] and Vingiani, A. et al. [ 30 ]. However, the precise prognosis value of BCS for patients with IMPC remained unknowable. In our study, IMPC had a lower rate of breast conserving surgery (26% vs.37.8%) compared with IDC.

IMPC was characterized by a high incidence of ER and PR positivity. Our study recorded a high percentage of ER (97.8%) and PR (98.6%) expression. Our findings are similar to those found by Walsh et al., [ 31 ] who reported ER and PR expression of 90% and 70%, respectively. Zekioglu et al. [ 32 ] demonstrated a rate of ER and PR expression of 68% and 61%respectively.

In this study, we reported a relatively lower percentage of HER-2 positivity (4.3%). Also, Nangong et al. [ 20 ] showed HER 2 overexpression in 26.4% of cases.

However, Cui et al. [ 33 ] reported a much higher incidence of HER 2 positivity and Perron et al., [ 34 ] reported that 65% of IMPCs were HER-2 positive.

Chen, A et al. [ 35 ] reported that that the percentage of radiation therapy for IMPC patients was similar to those seen in IDC patients and demonstrates a similar benefit of radiation treatment in both groups. In our study,77.5% patients received radiotherapy in IMPC group in compared to 59.4% patients in IDC group.

Shi et al. [ 7 ] found that patients with IMPC had worse recurrence-free survival (RFS) and overall survival (OS) rates as compared to those with IDC. However, because IMPC is relatively rare, most studies had reported on small sample sizes with limited follow-ups.

Yu et al., [ 24 ] conducted a comparison between IMPC and IDC patients, and the results showed that the IMPC group had a greater tendency for LRR compared to the IDC group ( P  = 0.03), but the distant metastasis rate ( P  = 0.52) and OS rate ( P  = 0.67) of the IMPC showed no statistical differences from the IDC group.

Nevertheless, several recent studies documented that IMPC had better or similar prognosis in comparison to IDC.

Hao et al. [ 23 ] and Vingiani et al. [ 30 ] documented that there was no statistically significant difference in OS and disease-free survival between IMPC patients and IDC patients which was similar to our results. locoregional recurrence rate comparing IMPCs with IDCs was (2.2% and 0.4% respectively). P value for local recurrence = 0.12 (yates corrected chi square). Distant metastasis rate comparing IMPCs with IDCs was (3.7% and 5.4% respectively). P value for distant metastasis = 0.53.

Chen H et al. [ 36 ], compared the overall survival in patient groups with similar nodal involvement and found that IMPC group had better breast cancer–specific survival and overall survival than IDC group.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

Invasive micropapillary carcinoma

Invasive duct carcinoma

Modified radical mastectomy

Conserving breast surgery

Estrogen receptor

Progesterone receptor

Lymphovascular invasion

Contrast enhanced spectral mammography

Overall survival

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Mohamed Fathy Abdelfattah Abdelrahman Elithy

Present address: Department of Surgical Oncology, Faculty of Medicine, Al Azhar University, Cairo, Egypt

Mahmoud Hassaan

Present address: Departement of Surgical Oncology, National Cancer Institute, Cairo University, Giza, Egypt

Authors and Affiliations

Department of General Surgery, Faculty of Medicine, Ain Shams University, Cairo, Egypt

Yasmine Hany Abdel Moamen Elzohery

Department of Radiodiagnosis, NCI, Cairo University, Giza, Egypt

Amira H. Radwan & Sherihan W. Y. Gareer

Department of Pathology, National Cancer Institute, Cairo University, Giza, Egypt

Mona M. Mamdouh

Department of Epidemiology and Preventive Medicine, National Liver Institute, Menoufia, Egypt

Baheya Center for Early Detection and Treatment of Breast Cancer, Giza, Egypt

Yasmine Hany Abdel Moamen Elzohery, Amira H. Radwan, Sherihan W. Y. Gareer, Mona M. Mamdouh, Inas Moaz, Abdelrahman Mohammad Khalifa, Osama Abdel Mohen, Mohamed Fathy Abdelfattah Abdelrahman Elithy & Mahmoud Hassaan

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Mohamed fathy participated in the sequence alignment and Yasmine hany drafted the manuscript. Mahmoud Hassan participated in the design of the study. Inas Moaz and Abdelrahman Mohammad performed the statistical analysis. Amira H. Radwan and Sherihan WY Gareer conceived the study. Mona M Mamdouh and Osama abdel Mohen participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

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Elzohery, Y.H.A.M., Radwan, A.H., Gareer, S.W.Y. et al. Micropapillary breast carcinoma in comparison with invasive duct carcinoma. Does it have an aggressive clinical presentation and an unfavorable prognosis?. BMC Cancer 24 , 992 (2024). https://doi.org/10.1186/s12885-024-12673-0

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Received : 05 April 2024

Accepted : 23 July 2024

Published : 12 August 2024

DOI : https://doi.org/10.1186/s12885-024-12673-0

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