Key Dates

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Issued by

National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

National Heart, Lung, and Blood Institute (NHLBI)

National Institute on Aging (NIA)

Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

National Cancer Institute (NCI)

All applications to this funding opportunity announcement should fall within the mission of the Institutes/Centers. The following NIH Offices may co-fund applications assigned to those Institutes/Centers.

Division of Program Coordination, Planning and Strategic Initiatives, Office of Disease Prevention (ODP)

Office of Behavioral and Social Sciences Research (OBSSR)

Office of Nutrition Research (ONR)

Purpose

This Request for Information (RFI) invites input on research strategies to address obesity heterogeneity. It is intended to solicit input from a broad array of interested individuals, groups and communities including the biomedical and behavioral research community, physicians and other health care professionals, industry, and other interested parties including public health or social service officials at the federal, state, county and community levels, community organizations, those in educational settings, payers, media, advocacy groups and the general public.

Background

According to the most recent figures from the CDC, more than 40% of U.S. adults have obesity including 9% with severe obesity.  There is increasing recognition that obesity is a heterogeneous disease like other complex chronic disorders.  Obesity heterogeneity describes the phenomenon of inter-individual variability in:

[a] susceptibility to development of obesity in response to obesogenic exposures,

[b] the response to obesity interventions and/or

[c] the susceptibility to development of specific obesity-associated comorbidities.

Obesogenic mediators [and their moderators] can include genes, other biological factors including age and sex, behavioral and psychosocial factors, along with environmental and social exposures, including social influences of health.  Obesity interventions include therapies in any domain including behavioral [e.g., dietary, physical activity, or other lifestyle modifications], surgical [e.g., Roux-en-Y gastric bypass surgery, implanted devices] or pharmacological [anti-obesity medications (AOMs) such as incretin mimetics, phentermine-topiramate, bupropion-naltrexone, etc.]. Obesity-associated comorbidities include, but are not limited to type 2 diabetes, obstructive sleep apnea, metabolic-associated fatty liver disease, depression, some cancers, osteoarthritis, hypertension, chronic kidney disease, and cardiovascular diseases, which in some cases may be bi-directional.

There are numerous examples of obesity heterogeneity in animal models and humans.  For example, when inbred or outbred strains of rodents are exposed to an obesogenic environment, some animals appear obesity prone or obesity resistant.  In humans, during a longitudinal assessment of bariatric surgery, there was considerable inter-individual variability in weight change trajectories and such heterogeneity has also been observed with interventions involving diet or AOMs.  In addition, there is inter-individual variability in susceptibility to obesity-related medical conditions (co-morbidities) not yet predictable based on current assessments.

Precision medicine aims to match the right treatment to a patient’s disease subtype a priori as opposed to using a trial-and-error approach.  That approach seems especially warranted for obesity, given its heterogenous nature, the number of people affected, the costs of new and emerging AOMs or bariatric surgery procedures, and the burden of obesity-associated comorbidities.  NIH has led workshops, symposia and initiatives to address obesity heterogeneity, including the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project.  This RFI is soliciting advice regarding how best to build upon these efforts to move basic and clinical research on obesity heterogeneity forward toward clinical utility.

To address interindividual variability [responder/ non-responder phenomena] in other complex diseases, it has been instructive to take a lesson from the asthma field, noted for its early recognition of prioritizing elucidation of disease endotypes over phenotypes.  Asthma is now considered an umbrella diagnosis for several diseases with distinct mechanistic pathways [endotypes] & variable clinical presentations [phenotypes].  A phenotype covers the clinically relevant or obvious properties of the disease [for obesity that could be metabolically healthy obesity, gynoid obesity, eating in the absence of hunger, or impulsivity].  However, phenotypes do not necessarily show the direct relationship to underlying disease etiology and pathophysiology that may be a key for treatments to work; endotypes [or endophenotypes which require a genetic component] are meant to do that.  Another aspect is that several endotypes can lead to the same phenotype or umbrella diagnosis.  Classifying complex diseases using pathogenetic mechanism-based subtypes (endotypes/endophenotypes) may have other advantages in epidemiological, genetic, and treatment-related studies.

Information Requested

The NIH is interested in information to guide us in identifying promising research strategies to address obesity heterogeneity in the U.S.  Endotyping is already being applied or explored in a number of complex diseases including but not limited to asthma, atherosclerosis, autism, chronic primary vasculitis, eosinophilic esophagitis, schizophrenia, sepsis, Sjögren’s syndrome, sleep apnea, systematic lupus erythematosus, and type 1 diabetes.  Given that this approach has not been widely used in obesity, NIH staff have been reflecting on the types of research needed to develop obesity endotypes and endophenotypes, algorithms to detect them, enhance prediction of efficacious treatment for individual patients, and improve prediction of susceptibility for development of obesity-related co-morbidities.  We are therefore requesting input on:

1.  How best to study obesity heterogeneity based on scientific opportunity and potential health impact.

2.  Promising strategies to reveal distinct mechanistic pathways [endotypes] underlying obesity subtypes, and their biomarkers.

3.  How advances in data science, including machine learning and artificial intelligence, can be leveraged to accelerate the development of precision approaches to prevention and treatment of obesity and its associated co-morbidities.

4.  How to best incorporate the expectation of inter-individual variability during the planning of obesity prevention or treatment trials [e.g., statistical plans for latent class or principal component analyses to capture or refine predictors of response or discover endotypes]

5.  Strategies that incorporate community factors, cultural, social, and economic factors, or other social determinants of health into endotypes.

6. The potential role of obesity endotypes in discerning susceptibility to or resilience from obesity co-morbidities.

7. Other comments, suggestions, or considerations relevant to this RFI and obesity heterogeneity.

Note specific interest of NICHD: Examining how obesity heterogeneity varies across the lifespan, and for populations of relevance to NICHD, including infants, children, adolescents, and individuals in the transition from adolescence to adulthood; pregnant and lactating persons; individuals of reproductive age with regards to reproductive health; girls and women from pre-puberty through perimenopause with regards to research on gynecological and/or reproductive health; individuals of any age with intellectual and developmental delays and/or disabilities.

Note of specific interest to NIA: We are particularly interested in information on age-related variability in obesity outcomes and treatment response heterogeneity, the role of health disparities and biological mechanisms of obesity in aging, heterogeneity of the impact of obesity on longevity, healthy ageing, intervention refinement and optimization, along with outcome measure validation particularly in ageing. 

How to submit a response

Responses will be accepted through November 29, 2024.

Responses must be submitted to [email protected] via email, either (a) within the body of the email or (b) as an attachment (PDF or MS Word).  Please note that no forms are required, and no page limits have been instituted.  Respondent comments do not have to address all of the above items, but in that case, it would be appreciated if the specific question or question number was reiterated ahead of the response.

The input from this RFI is for planning purposes only and should not be construed as a solicitation for applications or as an obligation on the part of the Government to provide support for any ideas identified in response to it.  Please note that the United States Government will not pay for the preparation of any information submitted or for its use of that information. 

The responses to this RFI will not be directly published, and we will not provide comments on any respondent’s submission. Otherwise, the NIH will use the information submitted at its discretion.  For example, the responses may be compiled, summarized and shared internally with NIH program officials that oversee obesity research grants, members of the Obesity Research Task Force, and other NIH leadership. It may also be shared with other federal agencies coordinating with NIH on reducing obesity or through the non-government organizations that they participate in for that purpose.  NIH will honor any respondent requests to make their input anonymous by requesting so in the body of the email or later. We will in that case remove any identifying information such as names, affiliation, or email addresses before sharing those responses electronically.

We look forward to your input and hope that you will share this document with your colleagues.

Inquiries

Please direct all inquiries to:

Christopher J. Lynch, Ph.D.
Senior Advisor
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Telephone: 301-325-4232