Obesity and Overweight: Probing Causes, Consequences, and Novel Therapeutic Approaches Through the American Heart Association's Strategically Focused Research Network

Jeanne M Clark; W Timothy Garvey; Kevin D Niswender; Ann Marie Schmidt; Rexford S Ahima; Jose O Aleman; Ashley N Battarbee; Joshua Beckman; Wendy L Bennett; Nancy J Brown; Paula Chandler‐Laney; Nancy Cox; Ira J Goldberg; Kirk M Habegger; Lorie M Harper; Alyssa H Hasty; Bertha A Hidalgo; Sangwon F Kim; Julie L Locher; James M Luther; Nisa M Maruthur; Edgar R Miller; Mary Ann Sevick; Quinn Wells

doi:10.1161/JAHA.122.027693

. 2023 Feb 8;12(4):e027693. doi: 10.1161/JAHA.122.027693

Obesity and Overweight: Probing Causes, Consequences, and Novel Therapeutic Approaches Through the American Heart Association's Strategically Focused Research Network

Jeanne M Clark ^1,^2,^3,^✉, W Timothy Garvey ^4,^✉, Kevin D Niswender ^5,^6,^✉, Ann Marie Schmidt ^7,^✉, Rexford S Ahima ⁸, Jose O Aleman ⁹, Ashley N Battarbee ¹⁰, Joshua Beckman ¹¹, Wendy L Bennett ^1,^2,^3,¹², Nancy J Brown ¹³, Paula Chandler‐Laney ⁴, Nancy Cox ¹⁴, Ira J Goldberg ⁹, Kirk M Habegger ¹⁵, Lorie M Harper ^10,¹⁶, Alyssa H Hasty ^17,¹⁸, Bertha A Hidalgo ¹⁹, Sangwon F Kim ^8,²⁰, Julie L Locher ²¹, James M Luther ²², Nisa M Maruthur ^1,^2,³, Edgar R Miller ^1,^2,³, Mary Ann Sevick ^9,²³, Quinn Wells ^24,²⁵

^¹Division of General Internal Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD

^²Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD

^³Welch Center for Prevention, Epidemiology and Clinical Research, The Johns Hopkins University, Baltimore, MD

^⁴Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL

^⁵Tennessee Valley Healthcare System, Vanderbilt University Medical Center, Nashville, TN

^⁶Division of Diabetes, Department of Medicine, Endocrinology and Metabolism, Vanderbilt University Medical Center, Nashville, TN

^⁷Department of Medicine, Diabetes Research Program, Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY

^⁸Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, The Johns Hopkins University School of Medicine, Baltimore, MD

^⁹Division of Endocrinology, Department of Medicine, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY

^¹⁰Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Alabama at Birmingham, Birmingham, AL

^¹¹Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN

^¹²Department of Population, Family and Reproductive Health, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD

^¹³Yale School of Medicine, New Haven, CT

^¹⁴Vanderbilt Genetics Institute and Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

^¹⁵Division of Endocrinology, Department of Medicine, Diabetes, and Metabolism, University of Alabama at Birmingham, Birmingham, AL

^¹⁶Division of Maternal‐Fetal Medicine, Department of Women’s Health, Dell Medical School, University of Texas at Austin, Austin, TX, USA

^¹⁷Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN

^¹⁸VA Tennessee Valley Healthcare System, Nashville, TN

^¹⁹Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL

^²⁰Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD

^²¹Division of Gerontology, Department of Medicine, Geriatrics, and Palliative Care, University of Alabama at Birmingham, Birmingham, AL

^²²Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center Tennessee, Nashville, TN

^²³Department of Population Health, Center for Healthful Behavior Change, New York University Langone Health, New York, NY

^²⁴Department of Pharmacology, Vanderbilt University, Nashville, TN

^²⁵Department of Medicine, Vanderbilt University Medical Center, Nashville, TN

Correspondence to: Ann Marie Schmidt, MD, 435 East 30th Street, Science Building Room 615, New York, NY 10016. Email: annmarie.schmidt@nyumc.org , Jeanne Clark, MD, MPH, The Johns Hopkins University, 2024 E. Monument Street, Suite 2–600, Baltimore, MD 21287. Email: jmclark@jhmi.edu , W. Timothy Garvey, MD, MACE, Department of Nutrition Sciences, University of Alabama at Birmingham, 1675 University Boulevard, Birmingham, AL 35223. Email: garveyt@uab.edu , Kevin Niswender, MD, PhD, 7465 Medical Research Building IV 2213 Garland Avenue, Nashville, TN 37232. Email: kevin.niswender@vumc.org

^✉

Corresponding author.

PMCID: PMC10111504 PMID: 36752232

Abstract

ABSTRACT: As the worldwide prevalence of overweight and obesity continues to rise, so too does the urgency to fully understand mediating mechanisms, to discover new targets for safe and effective therapeutic intervention, and to identify biomarkers to track obesity and the success of weight loss interventions. In 2016, the American Heart Association sought applications for a Strategically Focused Research Network (SFRN) on Obesity. In 2017, 4 centers were named, including Johns Hopkins University School of Medicine, New York University Grossman School of Medicine, University of Alabama at Birmingham, and Vanderbilt University Medical Center. These 4 centers were convened to study mechanisms and therapeutic targets in obesity, to train a talented cadre of American Heart Association SFRN‐designated fellows, and to initiate and sustain effective and enduring collaborations within the individual centers and throughout the SFRN networks. This review summarizes the central themes, major findings, successful training of highly motivated and productive fellows, and the innovative collaborations and studies forged through this SFRN on Obesity. Leveraging expertise in in vitro and cellular model assays, animal models, and humans, the work of these 4 centers has made a significant impact in the field of obesity, opening doors to important discoveries, and the identification of a future generation of obesity‐focused investigators and next‐step clinical trials. The creation of the SFRN on Obesity for these 4 centers is but the beginning of innovative science and, importantly, the birth of new collaborations and research partnerships to propel the field forward.

Keywords: American Heart Association, obesity, overweight, Strategically Focused Research Network

Subject Categories: Metabolism

Nonstandard Abbreviations and Acronyms

AHA: American Heart Association
COMMODORE: Cardiovascular and Multiple Metabolic Disease in Obesity Resource
DIAPH1: diaphanous 1
DPP‐IV: dipeptidyl peptidase IV
GD: gestational diabetes
GLP‐1R: glucagon‐like peptide‐1 receptor
IPMK: inositol polyphosphate multikinase
PKA: protein kinase A
PRESCIANT: Preclinical Science Integration and Translation
RAGE: receptor for advanced glycation end products
SFRN: Strategically Focused Research Network
sRAGE: soluble receptor for advanced glycation end products
TRF: time‐restricted feeding
TRIM: Time‐Restricted Intake of Meals
UCP1: uncoupling protein 1

In the United States, overweight and obesity are chronic diseases that contribute to excess morbidity and mortality. Despite public health efforts, these disorders are on the rise, and their consequences are burgeoning. ¹ The Centers for Disease Control and Prevention report that during 2017 to 2018, the prevalence of obesity in the United States was 42.4%, which was increased from the prevalence of 30.5% during 1999 to 2002. ² Among those afflicted with obesity, between 1999 and 2000, and 2017 and 2018, the prevalence of severe obesity doubled, from 4.7% to 9.2%. ² Obesity imposes a significant financial burden to the health care system, with annual costs of $147 billion reported in 2008. ² Current estimates indicate that by 2030, the global prevalence of overweight and obesity may exceed 57%. ³

Although >40% of the US population suffers with obesity, deeper investigation indicates that there are disparities across racial and ethnic groups, and additional disparities arise based on other factors such as age, sex, and socioeconomic status. ⁴ Beyond the burden of obesity and overweight in adults, the increase in obesity in children and adolescents is a cause of significant concern for the health and longevity of the population. In the United States, about 1 in 5 children and adolescents has obesity. ⁵ Despite these stark facts and figures, there are multiple hurdles to tackling obesity in children, such as access to healthy diet and physical activities. ⁶

In both children and adults, major consequences of obesity include the development of serious chronic disorders that further reduce quality of life and life span, such as type 2 diabetes, dyslipidemias, hypertension, and cardio‐ and cerebrovascular disease and their consequences in ischemic injuries. ⁷ ^, ⁸ Furthermore, in 2020, the SARS‐CoV‐2 virus infected >228 million people worldwide, with >4.6 million deaths during the COVID‐19 pandemic. ⁹ Multiple studies have concluded that obesity increases the risk of severe COVID‐19. ¹⁰ At this time, the full gamut of mechanisms underlying the role of obesity in SARS‐CoV‐2 is not understood; the contribution of obesity to the dysregulated immune response and its role in cardiometabolic disease have been implicated. ¹⁰ ^, ¹¹

Public health‐based approaches to legislate antiobesity initiatives, such as mandated food product labeling (calorie and nutrition information); the banning of transfat foods in restaurants and grocery stores by the Food and Drug Administration; taxation imposed on sugar‐sweetened beverages; and school level policies aimed at optimizing nutrition, physical activity, and restricting access to fast food by school‐age children, may take time to bear their anticipated fruit. ¹² Moreover, inherent biological mechanisms, imposed in part by high‐energy diets, alterations in physical activity, and lifestyle changes, as well as other yet‐to‐be‐identified factors, play key roles in the obesity epidemic, such as through altered sleep patterns and stress; genetic factors; epigenetic factors that emerge consequent to the obesogenic environment, which may be transgenerational; and modulation of the gut microbiome, as examples. ¹³

These considerations underscore that obesity is a chronic disease that confers excess morbidity and mortality as a consequence of its complications, and the full force of our medical model should be brought to bear on its prevention and treatment. ¹⁴ It was in this context that the American Heart Association (AHA), through the creation of the Strategically Focused Research Network (SFRN) on Obesity, sought to bring together research groups to take on the problems of overweight and obesity through the use of state‐of‐the‐art technologies and research tools not in isolation, but through intra‐ and intercenter collaborations.

In the absence of interruption and reversal of overweight and obesity, because these disorders beget additional chronic cardiovascular and other diseases, their long‐term sequelae may become irreversible. The 4 centers of the SRFN on Obesity address some of these complex issues in the sphere of obesity and its consequences. The center at Johns Hopkins University School of Medicine addresses the question of time‐restricted feeding (TRF) and if redistribution of the daily timing of energy intake impacts obesity and cardiometabolic health. The center at New York University Grossman School of Medicine addresses how the interplay of inflammation and altered energy expenditure present formidable barriers to sustained weight loss. The center at the University of Alabama at Birmingham queries the mechanisms of the transgenerational impact of the mother's obesity and the effects on offspring. The center at Vanderbilt University Medical Center aims to develop precision medicine strategies to target obesity and impact cardiometabolic health.

In the sections to follow, the history of the SFRN on Obesity, the centers' goals, composition, progress, and future directions in obesity research and population‐level implementation will be detailed. Importantly, because training of the next generation of researchers studying obesity and overweight was a seminal goal of the SFRN on Obesity, this review summarizes the work and the accomplishments of the fellows directly supported by this initiative and those who joined and experienced the benefits imbued through the SFRN mechanism.

History of the SFRN

In 2011 to 2012, the AHA developed the SFRN mechanism to address key strategic issues determined by the AHA Board of Directors. Three to 6 research centers make up each network, which are supported for a period of 4 years, including costs for network oversight and administration. The first SFRN launched in 2014 and was focused on prevention. One to 2 additional networks addressing different key strategic issues were launched each year, with a total of 14 SFRNs funded as of May 2022. In addition to funding research projects across the translational spectrum, all centers included a training program to produce a cadre of new investigators to energize the field of research and to produce important new research results.

An additional key objective of this initiative was to encourage collaboration among the centers in each network as well as across SFRNs, both in training and research efforts. The AHA appointed an oversight advisory committee to each SFRN to monitor progress and encourage interactive and collaborative activities, and to develop and implement a plan for regular dialogue among the center participants.

The SFRN on Obesity was the sixth such network funded by the AHA. The call for proposals was released in June 2016, and proposals were due in November of that year. After the initial review, the most meritorious centers were invited to present to a peer review committee in February 2017, from which 4 centers were selected for funding: Johns Hopkins University School of Medicine, New York University Grossman School of Medicine, the University of Alabama at Birmingham, and Vanderbilt University Medical Center. The SFRN on Obesity officially launched in April 2017, and continued through March 2022 (including a 1‐year no‐cost extension). Figure 1 identifies the 4 funded centers and their leadership, including the center director, project and key coleaders, the center training director, and the AHA‐funded fellows.

The figure illustrates the 4 centers, themes, and projects, and center director, project leaders, training director, and the American Heart Association–named fellows. ©Copyright 2021 American Heart Association, Inc., a 501(c) (3) not‐for‐profit. Unauthorized use prohibited. 11/21DS18536. CVD indicates cardiovascular disease; DIAPH1, diaphanous 1; GLP‐1, glucagon‐like peptide‐1; NYU, New York University; RAGE, receptor for advanced glycation end products; and UAB, University of Alabama at Birmingham.

Obesity SFRNs: Goals and Results

The sections to follow summarize each center noting the central theme and key accomplishments of the basic, clinical, and population projects.

Johns Hopkins University School of Medicine Obesity Center: The Role of TRF on Obesity and Cardiometabolic Health

Center Director: Jeanne M. Clark, MD, MPH.

Central Theme

Evidence suggests that the timing of calories consumed, not solely the number of calories, can have significant effects on body weight and metabolism. Furthermore, early studies suggest that actively synchronizing food intake with the circadian rhythm may enhance weight loss and improve cardiometabolic parameters, including concentrations of glucose and lipids. Therefore, this center sought to test the overarching hypothesis that the mistiming of caloric intake relative to normal circadian rhythm contributes to obesity and adverse cardiometabolic outcomes, including impaired glucose homeostasis, dyslipidemia, and hypertension. Note that a full list of the articles supported through this center is included within the reference section. ¹⁵ ^, ¹⁶ ^, ¹⁷ ^, ¹⁸ ^, ¹⁹ ^, ²⁰ ^, ²¹ ^, ²² ^, ²³ ^, ²⁴ ^, ²⁵ Figure 2 summarizes the center's key accomplishments.

Basic Project

Metabolic and Cardiovascular Effects of Time‐Restricted Feeding in Obese Mice.

Led by Dr Rexford Ahima, Dr Sangwon Kim and Fellow, Dr Ikrak Jung.

Objectives

The objectives of the basic project were to examine in vivo actions of IPMK (inositol polyphosphate multikinase) during TRF in mice and to determine the molecular mechanisms mediating cellular actions of IPMK in glucose and lipid metabolism.

Major Findings

The project used primary hepatocytes and a genetically modified mouse model in which IPMK was specifically deleted in the liver. Overall expression of hepatic IPMK was found to be higher in the TRF group than in ad libitum‐fed wild‐type mice. A genetic deletion of hepatic IPMK suppresses insulin signaling, disrupts glucose homeostasis, and enhances lipid accumulation in the liver. The data suggest that IPMK plays a crucial role in hepatic metabolism and may contribute to the beneficial effects of TRF.

Clinical Project

Effect of Time‐Restricted, Isocaloric Feeding on Weight: A Randomized Feeding Study in Adults With Obesity.

Led by Dr Nisa Maruthur, and Fellow, Dr Ruth‐Alma Turkson‐Ocran.

Objectives

While maintaining isocaloric status at the individual level, the objective of the project was to determine the effect of TRF (80% of calories before 1 pm) versus a usual feeding pattern (≥50% calories after 5 pm) over 12 weeks on weight change, changes in glucose homeostasis, and additional outcomes, including blood pressure, appetite, and insulin signaling molecule expression.

Major Findings

Of 41 randomized participants (TRF, n=21; usual feeding pattern, n=20), all completed the study and provided data for the primary outcome (weight). Adherence to the timing of feeding windows and the consumption of only study food was extremely high. Changes in weight, glucose homeostasis (fasting glucose, homeostatic model assessment for insulin resistance, area under the curve for glucose, and fasting insulin), blood pressure, and lipids did not differ across the intervention arm at 12 weeks.

Population Project

The Impact of Timing of Eating on Weight: A Multisite Cohort Study Using the Metabolic Compass Mobile Application.

Led by Dr Wendy Bennett and Fellow, Dr Attia Goheer.

Objectives

The objectives of the population project were to optimize the functionality, usability, and behavioral aspects of the Daily24 mobile application, and to conduct a population‐based cohort study of 1000 patients from 3 health systems, part of the National Patient‐Centered Clinical Research Network, to assess adherence with use of the Daily24 app and the association between windows of eating and weight.

Major Findings

With end‐user input, the investigators designed the Daily24 mobile application, which was shown to be usable for data collection of timing of eating and sleeping in an observational cohort study. They recruited patients aged ≥18 years with a weight and height recorded within the past 2 years from 3 health systems that were part of the National Patient‐Centered Clinical Research Network Clinical Data Research Network. Of the 70 661 patients who sent electronic research invitations over 6 months (email or patient portal), 1021 (1.4%) completed electronic consent and baseline online surveys, and 4 withdrew, leaving a total of 1017 participants. The majority were women (88.3%), White (77.5%), and college graduates (73.6%), and the mean age was 51.1 years. Of the 1017 enrolled participants, 54% (n=547) downloaded and used the Daily24 app for 1 or more days. Younger age, White race, higher education levels and higher income, having no children aged <18 years, and having used 1 to 5 health apps previously were significantly associated with being a Daily24 app user (versus nonuser) in adjusted models. Among the 547 Daily24 app users, the mean interval from first to last meal was 11.5 (2.3) hours and was not associated with weight change. The number of meals per day was positively associated with weight change (0.28 kg per meal per year [95% CI, 0.02–0.53]). These findings do not support the use of time‐restricted eating as a strategy for long‐term weight loss in a general medical population.

New York University Grossman School of Medicine: Braking Inflammation in Obesity and Metabolic Dysfunction: Translational and Therapeutic Opportunities

Center Director: Dr Ann Marie Schmidt.

Central Theme

The central theme of the SFRN on Obesity at New York University evolved from the observations that despite the severity of the problem of obesity and its cardiometabolic consequences, it is established that even with effective treatments, principally bariatric surgery and, more recently, the advent of successful pharmacological approaches, natural brakes, such as those that block effective energy expenditure through metabolic adaptation mechanisms after weight loss, may dramatically impede the ability to lose and sustain substantive weight loss. The center discovered that amplified RAGE (receptor for advanced glycation end products) and DIAPH1 (diaphanous1) activity, both in murine models and in human subjects, hinders medically or surgically induced optimal weight loss through the convergence of 2 key metabolic processes, that is, by suppression of energy expenditure and propagation of a proinflammatory state in adipose tissue, liver, and other metabolic organs. Through 3 interconnected and highly collaborative projects from mouse models to human subjects, this center tested the premise that these RAGE‐dependent processes converge via perturbation of endogenous adipocyte and macrophage/immune cell functions to block weight loss, and that interruption of RAGE signaling would release the innate brake on energy expenditure and on suppression of anti‐inflammatory mediators, thereby accelerating weight loss and return of metabolic homeostasis. Note that a full list of all of the articles supported through this center is included within the reference section. ²⁶ ^, ²⁷ ^, ²⁸ ^, ²⁹ ^, ³⁰ ^, ³¹ ^, ³² ^, ³³ ^, ³⁴ ^, ³⁵ ^, ³⁶ ^, ³⁷ ^, ³⁸ Figure 3 summarizes the center's key accomplishments.

Basic Project

RAGE/DIAPH1: Inflammation and Suppression of Energy Expenditure in Obesity: Mechanisms and Therapeutic Opportunities.

Led by Dr Ann Marie Schmidt and Fellow, Dr Henry Ruiz.

Objectives

The objective of the basic project was to test the premise that the RAGE/DIAPH1 axis suppresses energy expenditure and modulates expression of pro‐ versus anti‐inflammatory mediators, processes linked to insulin resistance, and metabolic dysfunction in obesity.

Major Findings

The work of the project revealed that global‐ or adipocyte‐specific deletion of Ager, the gene encoding RAGE, in mice fed a high‐fat diet resulted in reduction in obesity, fat mass, and insulin and glucose intolerance, together with higher energy expenditure. The studies revealed that RAGE‐dependent mechanisms were accounted for by suppression of PKA (protein kinase A)‐mediated phosphorylation of its key targets implicated in lipid metabolism, hormone‐sensitive lipase, and p38 mitogen‐activated protein kinase, upon β‐adrenergic receptor stimulation. Through RAGE‐dependent dampening of lipolysis, the reduced expression and activity of UCP1 (uncoupling protein 1) and thermogenic programs resulted. ²⁶

Studies were also performed in the subcutaneous adipose tissue versus omental adipose tissue of humans with obesity versus morbid obesity. It was found that in subcutaneous adipose tissue, but not omental adipose tissue, the expression pattern of genes in the advanced glycation end products/RAGE/DIAPH1 axis is significantly and positively correlated with that of inflammatory and adipogenic markers. Furthermore, in subcutaneous adipose tissue, but not omental adipose tissue, AGER expression positively and significantly correlated with homeostatic model assessment for insulin resistance, a measure of insulin resistance. ³²

Collectively, these findings suggested seminal roles for the RAGE axis in obesity and insulin resistance. Studies are underway to complete experiments illustrating the benefits of RAGE/DIAPH1 pharmacological antagonism using a newly discovered chemical probe that blocks that binding of the RAGE cytoplasmic domain with DIAPH1, known as RAGE229 ³⁹ in acceleration of weight loss and improvement in metabolic health in murine models.

Clinical Project

Obesity, Inflammation, and Cardiovascular Risk.

Led by Drs Ira Goldberg and Jose Alemán and Fellow, Dr Dimitris Nasias.

Objectives

The objectives of the clinical project were to study white blood cells, fat tissue, circulating lipids and the concentrations of soluble RAGE (sRAGE) in humans to determine the relationship between weight loss and improvements in glycemia and inflammation.

Major Findings

Through multiple state‐of‐the‐art approaches, including single cell transcriptomic analyses, metabolomics studies, and adipose tissue fat biopsies, the project demonstrated that sleeve gastrectomy and dietary weight loss markedly alter the phenotype of white blood cells and circulating metabolites. The nature of the changes in white blood cells is directly impacted by the presence of diabetes. Specifically, the reduction in inflammatory cells is primarily observed in the metabolically healthy group. Experiments are underway to examine adipogenic and inflammatory end points in the adipose tissues collected during the study and to interrogate potential associations with the advanced glycation end products/RAGE and other key pathways in these findings.

In this context, plasma metabolomics analyses indicate a reduction in circulating concentrations of ceramides in all subjects undergoing sleeve gastrectomy, which was more pronounced in patients without diabetes. The research team is comparing the observed metabolomic signatures with the analyses from other recently completed bariatric surgery cohorts analyzed by the New York University center, ³⁶ as well as in TRF interventions, which are being performed in collaboration with the Johns Hopkins University center.

Population Project

Alternative Technology‐Supported Behavioral Weight Loss Intervention Programs.

Led by Dr Mary Ann Sevick and Fellow, Dr Collin Popp.

Objectives

Mechanistic evidence links postprandial glycemic response to weight gain and indicates that the postprandial glycemic response is highly individual and dependent, at least in part, on the composition and function of the intestinal microbiota. In collaboration with and prompted by Segal's report supporting personalized dietary approaches to weight loss, ⁴⁰ the population project proposed to compare a standardized to a personalized counseling approach for the study of weight loss, weight maintenance, body composition, and metabolic adaptation.

Major Findings

A 2‐phase, randomized clinical trial was conducted in patients with prediabetes and obesity recruited from community‐based settings. Phase 1 included 6 months of active intervention, and Phase 2 consisted of 6 months of maintenance and observation. Participants were randomized with equal allocation to 2 groups: (1) a standardized behavioral weight loss intervention with a 1‐size‐fits‐all regimen that included counseling about restriction of calories and calories from fat, and physical activity, delivered using mHealth technology (termed mHealth), or (2) all of the elements of mHealth, plus personalized dietary recommendations to minimize glycemic response to meals (termed personalized‐mHealth). The primary aim was to compare weight losses in personalized versus standardized arms, with the hypothesis that percent weight loss_Personalized would be greater than percent weight loss_Standardized at 6 months, which would be sustained at 12 months. The secondary aims of the study were to describe between‐group differences in body composition and metabolic adaptation at 6 and 12 months, and weight regain between 6 and 12 months.

Between January 2018 and May 2021, the study enrolled 204 participants. Of the 105 participants allocated to the personalized group, 87 (83%) completed 3‐month and 84 (80%) completed 6‐month assessments. Of the 99 participants allocated to the standardized group, 70 (71%) completed 3‐month and 71 (72%) completed 6‐month assessments. Because of the COVID‐19 pandemic, recruitment was temporarily suspended, truncating 12‐month follow‐up to a subset of participants (n=156), including 53 (68%) personalized and 47 (60%) standardized participants. Weight change at the 6‐month time point did not significantly differ between the groups. Furthermore, no between‐group differences were observed in body composition and adaptive thermogenesis. Of note, the change in resting energy expenditure from 0 to 6 months was found to be significantly greater in the standardized group. ³⁷

University of Alabama at Birmingham Center: University of Alabama at Birmingham Center Strategically Focused Obesity Center: Intergenerational Transmission of Obesity

Center Director: Dr W. Timothy Garvey.

Central Theme

Offspring exposed to maternal overnutrition, undernutrition, and dysglycemia in utero have long‐term risk for obesity and cardiometabolic disease including type 2 diabetes and hypertension. ⁴¹ ^, ⁴² ^, ⁴³ ^, ⁴⁴ ^, ⁴⁵ ^, ⁴⁶ ^, ⁴⁷ In utero programming of the developing fetus has been implicated in this phenomenon, but the mechanisms are not clearly elucidated. The overarching goal of the University of Alabama at Birmingham center was to investigate the pathophysiological and epigenetic mechanisms by which an in utero environment involving nutritional stress, obesity, and gestational diabetes (GD) in mothers during gestation contributes to risk for obesity and cardiometabolic disease in offspring. Note that a full list of all of the articles supported through this center is included within the reference section. ⁴⁸ ^, ⁴⁹ ^, ⁵⁰ ^, ⁵¹ ^, ⁵² ^, ⁵³ ^, ⁵⁴ Figure 4 summarizes the center's key accomplishments.

GDM indicates gestational diabetes mellitus.

Basic Project

Intergenerational Transmission of Obesity: Rodent Models of In Utero Stress.

Led by Drs W. Timothy Garvey and Kirk Habegger, and Fellow, Dr Rogerio Sertie.

Objective

Rodent models assessed whether alteration of the in utero environment caused by malnutrition (an isocaloric protein‐restricted diet) or overnutrition (high‐fat Western diet) produced obesity and cardiometabolic disease in offspring. The offspring from the different treatment groups were then split between low‐ and high‐fat diets after weaning to interrogate if changes in body composition, energy balance, and glucose homeostasis regulation could be attributed to in utero programming. A particular focus was whether abnormalities in the satiety hormone‐hypothalamic axis induced by in utero nutritional stress were responsible for alterations in food intake, body weight, and cardiometabolic disease traits in offspring.

Major Findings

Gestational exposure to an isocaloric protein‐restricted diet produced low‐birth‐weight offspring with greater percent lean mass and less caloric intake despite comparable leptin concentrations as controls. ⁴⁸ Conversely, offspring of dams fed a high‐fat diet during gestation displayed increased sensitivity to diet‐induced obesity and greater energy intake despite higher leptin concentrations and insulin resistance. ⁴⁸ These results suggest that the intrauterine environment modified the set point around which leptin regulates body weight, contributing to obesity.

DNA methylation in the hypothalami of offspring was assessed at the whole genome level using Medip‐Seq, and whole organ transcriptomic analysis was conducted using RNA sequencing. The combination of these studies allowed an examination as to whether differentially methylated CpGs would be associated with differential expression of the corresponding mRNA at that gene locus. Early results point to effects of the in utero environment on the Stat3 and Socs genes involved in inflammation and leptin signaling, ⁵⁵ genes involved in energy metabolism, and genes identified in the population project, which have been previously associated with waist‐to‐hip ratio in humans (eg, RRAS2). The observation that the in utero environment is associated with differential methylation at the STAT3 and RRAS2 loci implicates genes involved in leptin signaling and fat distribution in the intergenerational transmission of obesity and cardiometabolic disease.

Clinical Project

Intergenerational Transmission of Obesity: Antenatal Mother and Beyond Birth Offspring.

Led by Drs Lorie Harper and Ashley Battarbee.

Objective

This project enrolled women at or after 36 weeks of pregnancy, stratified into the groups defined by maternal weight status at the first prenatal care visit (normal weight, obese), and obese with GD to test the hypothesis that alterations in the in utero environment would produce differences in the cardiometabolic phenotypes of neonates at birth and 3 months of age. Furthermore, it was hypothesized that the same epigenetic modifications observed in older children and associated with cardiometabolic disease traits would also be present at the time of birth.

Major Findings

Enrollment was completed in September 2021, and the 3‐month postpartum assessment was completed in December 2021. Analyses are underway to test the hypothesis that intrauterine exposure to maternal obesity, with and without GD, impacts the infant metabolic phenotype at 3 months with increased fat accretion, and that these results are mediated by epigenetic changes identified at birth. Furthermore, whether the epigenetic modifications are similar to those in the older children and have persisted to ages 4 to 10 years was to be examined. The investigators aim to identify modifiable risk factors for persistent metabolic derangements at 3 months of life to prevent lifelong obesity in infants with in utero exposure to maternal insulin resistance and obesity.

Population Project

Intergenerational Transmission of Obesity in a Cohort of Mother–Child Dyads.

Led by Drs Paula Chandler‐Laney, Bertha Hidalgo, and Fellow, Dr Samantha Martin.

Objective

This project enrolled mother–child dyads to compare obesity and cardiometabolic phenotypes and epigenetic signatures of mothers and 4‐ to 10‐year‐old children following a pregnancy complicated by maternal obesity with GD, maternal obesity without GD, and compared with dyads with a healthy pregnancy in which women had a body mass index (BMI) in the normal weight range at entry to prenatal care.

Major Findings

Women with a history of obesity in pregnancy, with and without GD, had a poorer cardiometabolic phenotype as compared with women with normal weight during the index pregnancy. ⁴⁹ However, the women with obesity were different depending on whether GD was also present. In women with obesity but no GD, poor cardiometabolic health was associated with current adiposity. However, for women with obesity and GD, poor cardiometabolic health was independent of current adiposity. Children's cardiometabolic traits were modestly correlated with those of their mothers, but at this young age, only adiposity significantly differed across groups, with higher numbers in children exposed to maternal obesity, with and without GD. ⁴⁹ The lack of group differences on other cardiometabolic biomarkers and traits in children suggests that the perturbations produced by in utero exposure to maternal obesity or GD, as reported in prior studies, ⁴³ ^, ⁵⁶ may become more pronounced once children reach puberty or adolescence. Epigenome‐wide association studies in mothers and children demonstrated that several biologically putative genes were associated with cardiometabolic disease phenotypes including STAT3, SOCS, and AKT, all involved in leptin and insulin signaling, genes regulating inflammatory responses, and genes involved in cellular vesicular trafficking, including intracellular translocation of SLC2A4, the gene encoding GLUT4 (glucose transporter type 4).

Vanderbilt University Medical Center: Toward Obesity Precision Medicine: Promise of the Glucagon‐Like Peptide 1 Receptor

Center Director: Dr Kevin Niswender.

Objective

The foundational context of the Vanderbilt University center is the observation that drugs targeting GLP‐1R (glucagon‐like peptide‐1 receptor) are cardioprotective and that genetic variants in the coding sequence of its gene are also protective. The overarching goal was to develop an integrated platform for understanding the genetic architecture, molecular pharmacology, pathophysiology, and therapeutic potential of the GLP‐1R axis in obesity and cardiometabolic health. Specifically, the center sought to understand the mechanisms by which cardioprotection arises from GLP‐1R activation. The platform enabled the investigators to ask direct questions about the role of the GLP‐1R and genetic variants in obesity and cardiovascular disease. The center sought to identify critical new and important genes, pathways, and mechanisms necessary to fully understand obesity and optimize therapeutic approaches. Note that a full list of all of the articles supported through this center is included within the reference section. ⁵⁷ ^, ⁵⁸ ^, ⁵⁹ ^, ⁶⁰ ^, ⁶¹ ^, ⁶² Figure 5 summarizes the center's key accomplishments.

GLP‐1 indicates glucagon‐like peptide‐1.

Basic Project

GLP‐1R and Cardiometabolic Health.

Led by Dr Kevin Niswender.

Objective

The goal of the basic project was to understand the molecular pharmacology and in vivo physiology of GLP‐1R variants that are known to impact cardiometabolic outcomes in humans. The investigators sought to determine if signaling bias occurs with variant receptors, and whether similar patterns of bias could be induced in the reference GLP‐1R by small molecule positive allosteric modulators. Furthermore, the investigators sought to determine the impact on cardiometabolic outcomes in mice carrying these variant receptors.

Major Findings

GLP‐1R variants alter coupling to intracellular signaling pathways, inducing specific patterns of signaling bias. In one case, this bias results in substantial augmentation of coupling to potent antioxidant and anti‐inflammatory response pathways. In vivo, GLP‐1R variants have an important impact on both glucose and energy homeostasis, although these studies are ongoing.

Clinical Project

Vascular Effects of GLP‐1R Activation.

Led by Drs James M. Luther and Nancy J. Brown.

Objectives

The goal of the Clinical project was to determine whether GLP‐1R activation exerts weight loss–independent effects through improvement in vascular measures, metabolic parameters, and inflammation.

Participants with obesity and prediabetes were enrolled and randomized to the GLP‐1R analogue, liraglutide; the DPP‐IV (dipeptidyl peptidase IV) inhibitor sitagliptin, or dietary weight loss for 14 weeks, with matching placebo injection and pills for drug (NCT0310930). These treatments were chosen to dissect the specific contribution of GLP‐1R activation, endogenous GLP‐1, and weight loss on beneficial cardiovascular effects. Treatment with sitagliptin inhibits degradation of endogenous GLP‐1 and additional circulating vasoactive peptides, but does not induce weight loss or reduce cardiovascular risk. Treatment with the GLP‐1R analogue liraglutide is proven to induce weight loss and reduce cardiovascular risk. Dietary weight loss controls for the proven beneficial effects of weight loss in the absence of direct GLP‐1R activation. The effects of treatment on glucose metabolism, endothelial function, fibrinolytic function, and circulating inflammatory markers were assessed at baseline and then after 2 and 14 weeks of the randomized intervention. The investigators additionally administered the selective GLP‐1R antagonist exendin (9–39) to determine which effects are GLP‐1R dependent.

Major Findings

Liraglutide and diet intervention promoted weight loss and improved fasting glucose, insulin resistance, and fibrinolytic function, as measured by plasminogen activator inhibitor 1. Treatments did not alter endothelial vasodilatory function, as measured by flow‐mediated dilation in this cohort, who had relatively normal baseline endothelial function. The longer‐term cardiovascular benefits of liraglutide were thus not well captured by changes in flow‐mediated dilation. ⁵⁷

Population Project

Genome and Phenome Studies on Obesity and Cardiovascular Disease to Dissect GLP‐1R Mechanism.

Led by Drs Nancy Cox and Quinn S. Wells, with Fellows, Dr Megan Shuey‐Henthorn and Rebecca Levinson.

Objectives

There is significant interindividual variation among individuals with obesity in the amount of excess weight, presence of metabolic abnormalities, and the occurrence of cardiovascular disease. These observations suggest that there are subgroups within obesity with distinct disease mechanisms and overall risk. This project sought to test the hypothesis that obesity is composed of multiple subtypes that differ in regard to cause, natural history, and risk for complications. The project leveraged large‐scale public genomic data, as well as a large database of electronic health records linked with dense, genome‐wide genotyping to discover obesity subtypes, identify obesity‐related genes and genetic modifiers, define the relative contributions of risk mediators to obesity subtypes, and discover novel subtype‐specific risk factors.

Major Findings

A major goal of the project was to develop an enduring data set consisting of well‐curated electronic health record data including key cardiometabolic parameters and associated genetic data. The large task was accomplished with COMMODORE (Cardiovascular and Multiple Metabolic Disease in Obesity Resource) for the study of obesity and cardiometabolic disease. COMMODORE includes demographic data, BMI, laboratory values, and cardiovascular diseases, from Vanderbilt University Medical Center's deidentified electronic health record on nearly 850 000 individuals, which will be a potent resource for cardiometabolic research moving forward. Because alternative measures of body composition such as waist‐to‐hip ratio are not routinely collected in the clinic, the project tested the hypothesis that better tools than BMI could address cardiometabolic risk. Using COMMODORE, the investigators determined that modeling height and weight as independent, interacting variables results in less bias and improved predictive accuracy for all tested traits relative to using BMI. ⁵⁸ Also, using COMMODORE, the investigators have determined the effect of change in weight relative to accumulation of the components of metabolic syndrome on 10‐year coronary artery disease risk.

Center Collaborations

Among the goals of the SFRN on Obesity was the stimulation of collaborations, as follows: intra‐obesity center collaborations, inter‐obesity center collaborations, and cross‐SFRN collaborations. Although the shutdowns and research disruptions caused by COVID‐19 limited to some degree the envisioned collaborations, in the sections to follow, we detail examples of the collaborations that would not have been possible without the creation of the SFRNs. As results are finalized and articles are accepted and published, there is great potential for new collaborations to result from the SFRN on Obesity centers.

Johns Hopkins University

Johns Hopkins University Obesity Basic and Clinical Projects

The objective of the collaboration was to understand the potential effect of TRF in humans on IPMK expression. IPMK expression from baseline and postintervention specimens was evaluated for 25 TRIM (Time‐Restricted Intake of Meals) participants. Interpretation of these results is ongoing.

Johns Hopkins University Obesity and New York University Obesity Centers

Using the metabolomics infrastructure and expertise at New York University, the investigators analyzed plasma samples from a subset of the TRIM participants (clinical project). Preliminary analyses suggest a difference in metabolites from glucose and lipid pathways, and the investigators are performing analysis of the full TRIM sample. Notably, there have been no publications to date on the impact of TRF on metabolomics end points.

Johns Hopkins University Obesity and University of Alabama at Birmingham Hypertension Centers

Johns Hopkins University has enlisted Dr Byron Jaeger from the University of Alabama at Birmingham Hypertension Center to consult on analyses of 24‐hour ambulatory blood pressure in the TRIM study (clinical project). The article from this collaboration is being prepared for submission.

Johns Hopkins University Obesity and Johns Hopkins University GoRedforWomen Centers

In the second year of the SFRN on Obesity, additional funding was obtained from the AHA to support a collaborative project between the obesity center and the GoRedforWomen Center at Johns Hopkins University. The investigators designed an exploratory study using the Daily24 mobile application to collect information about timing of eating and sleeping, as well as a survey of 24‐hour caloric intake, in a subset of Johns Hopkins University participants in the GoRedforWomen cohort of women diagnosed with obesity (BMI >30 kg/m²) and heart failure with preserved ejection fraction. For the analysis, they designed a case–control study, selecting a comparison group of people without heart failure from the larger Daily24 cohort. The investigators are finalizing the analysis for publication.

New York University Grossman School of Medicine

Obesity Basic and Population Projects

Drs Schmidt (basic) and Sevick and Popp (population) collaborated to measure concentrations of plasma sRAGE and endogenous secretory RAGE and their potential associations with weight loss, body composition, and energy expenditure in participants undergoing weight loss at baseline and at 3 months, as described above in the population project. Although the baseline concentrations of sRAGE isoforms did not predict the changes in fat mass or fat‐free mass, all baseline sRAGE isoform concentrations were positively associated with the change in resting energy expenditure at 3 months. Further analyses demonstrated that the association between sRAGE isoforms and energy expenditure was independent of glycosylated hemoglobin, thereby suggesting that the relationship observed on sRAGE isoforms and energy expenditure was not dependent on glycemia. ³⁰

In addition, the collaboration between the clinical project and the Johns Hopkins University Obesity SFRN clinical project was discussed above.

University of Alabama at Birmingham Center

Obesity Clinical and Population Projects

Dr Chandler‐Laney (population) is collaborating with Dr Tita (consultant for the clinical project) and colleagues in the University of Alabama at Birmingham Divisions of Maternal Fetal Medicine and of Preventive Medicine to conduct research investigating cardiometabolic health of women and children. In one study, growth, adiposity and neurodevelopmental outcomes are being evaluated in 5‐ to 10‐year‐old children born to women who participated in a clinical trial evaluating whether adjunctive azithromycin prophylaxis reduced risk of maternal infection following cesarean delivery (R01HD097207; Principal Investigator: Subramaniam). In another project, Drs Chandler‐Laney and Lewis (population) and Dr Tita are coinvestigators of an intervention to leverage home visitor programs to improve cardiovascular health of women and their children (UG3HL162973; Principal Investigator: Dutton). Dr Hidalgo (population) is principal investigator of a project involving collaboration with Drs Tita and Subramaniam, investigating epigenetic biomarkers of preeclampsia risk among mothers with chronic hypertension.

Obesity Basic and Population Projects

Drs Habegger (basic) and Hidalgo (population) are collaborating on a project involving methylome‐ and transcriptome‐wide profiling to hypothalamic and germline tissue to address whether metabolic perturbations in the parent generation are associated with DNA methylation and gene expression in the hypothalamus of adult offspring, and whether differences in offspring DNA methylation are caused by epigenetic inheritance or to in utero exposure to suboptimal conditions. Analyses are ongoing.

Vanderbilt University Medical Center

Vanderbilt Obesity and Vanderbilt Vascular SFRNs

Dr Megan Shuey (Vanderbilt Obesity SFRN, population project) collaborated with Dr Joshua Beckman of the Vanderbilt Vascular SFRN. This collaboration resulted in the development of the PRESCIANT (Preclinical Science Integration and Translation) platform, which leverages preclinical investigations to identify high‐confidence pathways, networks, and regulators of human disease. ⁵⁹ A second intercenter collaboration capitalized on both the obesity and vascular SFRNs at Vanderbilt. Led by Dr Katherine Cahill, the team studied the expression of GLP‐1R on platelets in both mice and humans to begin to understand the impact of GLP‐1R on platelet function in terms of aggregation and inflammation. ⁶⁰

Training Mission of the SFRN on Obesity

A central charge to the 4 SFRN centers on obesity was the development of robust training programs. Specifically, each center named an experienced training director to serve in this role. Although the COVID‐19 pandemic affected some of the recruitment goals for the training component of the SFRN, each center successfully recruited formally named AHA fellows (Table 1). Their projects and research productivity are presented in the Table1. In addition to published articles, the AHA fellows participated in and presented their research work at multiple national and international conferences, particularly at the annual sessions of the AHA in November of each year. In addition to the formally named AHA fellows for each SFRN, numerous additional trainees participated in the center mechanism.

Table 1.

American Heart Association Strategically Focused Research Network on Obesity Fellows

John Hopkins University (Training Director, Edgar R. Miller III, MD, PhD)	Fellows' publications
Attia Goheer, PhD 2017–2019 Population project, What Influences the “When” of Eating and Sleeping	16, 17, 18, 22, 23
Ikrak Jung, PhD 2018–2020 Basic project, Regulation of Hepatic Insulin Signaling and Glucose Production by IPMK	²⁰
Ruth Alma Turkson‐Ocran, PhD 2019–2021 Clinical project, The Effect of Time‐Restricted Feeding on Ambulatory Blood Pressure	²⁴
University of Alabama at Birmingham (Training Director, Julie Locher, PhD)
Samantha Martin, PhD 2018–2020 Population project, Intergenerational Transmission of Obesity in a Cohort of Mother–Child Dyads	49, 50, 53, 54
Rogerio Sertie, PhD 2018–2021 Basic project, The Intergenerational Transmission of Obesity in Rat Models of In Utero Stress	48, 51
New York University Grossman School of Medicine (Director, Ira J. Goldberg, MD)
Collin Popp, PhD, MS, RD 2017–2019 Population project: Effect of a Personalized Diet to Reduce the Postprandial Glycemic Response to Meals Versus a Low‐Fat Diet on Weight Loss in Adults With Prediabetes and Obesity: A Randomized Clinical Trial	27, 28, 29, 30, 31, 37, 38
Henry H. Ruiz, PhD 2018–2020 Basic project: The RAGE/DIAPH1 Signaling Pathway and Implications for Obesity and Impaired Weight Loss	26, 32, 33, 34, 35
Dimitris Nasias, MSc, PhD 2019–2021 Clinical/translational project: Single Cell RNA Sequencing of Obese Subjects Following Sleeve Gastrectomy Show Reduced Expression of Inflammatory Markers and Increased Viral Response of White Blood Cell	Pending
Vanderbilt University (Training Directors, Alyssa H. Hasty, PhD and Joshua A. Beckman, MD, MSc)
Rebecca Levinson, PhD 2017–2019 Population project: COMMODORE (Vanderbilt Cardiovascular and Metabolic Disease in Obesity Resource) for Obesity Precision Medicine	58, 62
Megan Shuey‐Henthorn, PhD, MSc 2019–2021 Population project: GLP‐1R Signaling Bias and Cardioprotection; Unbiased Machine Learning Approaches to Develop Obesity Subtypes	58, 59, 61, 62

Open in a new tab

COMMODORE indicates Cardiovascular and Multiple Metabolic Disease in Obesity Resource; DIAPH1, diaphanous 1; GLP‐1R, glucagon‐like peptide‐1 receptor; IPMK, inositol polyphosphate multikinase; and RAGE, receptor for advanced glycation end products.

At Johns Hopkins University, 2 additional T32‐funded fellows were recruited to work on the clinical project, TRIM. Dr Scott Pilla was a fellow in the Division of General Internal medicine funded by a T32 grant from the National Heart, Lung, and Blood Institute. Dr Pilla became an integral part of the clinical project team from 2017 to 2019 including his transition to faculty. He was involved in finalizing the study design and feeding intervention as well as study recruitment. He was recruited to the Johns Hopkins University faculty in 2018 as an Assistant Professor of Medicine and successfully competed for a KL2 award and then a K23 award from the National Institute of Diabetes and Digestive and Kidney Diseases. He is coauthor of several articles that are in development. Dr Daisy Duan was a fellow in the Division of Endocrinology, Diabetes, and Metabolism who also worked with the clinical project team from 2019 to 2022. She was funded by 2 T32 grants, 1 from the National Institute of Diabetes and Digestive and Kidney Diseases and 1 from the National Heart, Lung, and Blood Institute. Dr Duan led efforts to examine the hormonal effects of TRF, including collecting samples to measure cortisol levels. Dr Duan joined the faculty at Johns Hopkins University in July 2022 as an Assistant Professor of Medicine and has a pending K23 award from the National Institute of Diabetes and Digestive and Kidney Diseases.

At the New York University Grossman School of Medicine Center, 6 additional fellows collaborated with the named AHA fellows. Drs Lakshmi Arivazhagan and Robin Wilson (basic project) contributed to the studies in the basic science project; both fellows reported their findings at annual sessions of the AHA. In the clinical project, for inpatients undergoing bariatric surgery‐induced weight loss, Drs Brenda Dorcely and Joanne Bruno are studying the effects of metabolic state on changes in white blood cell transcriptome and on the plasma metabolomics profile, respectively. In the population project, led by Drs Collin Popp and a junior faculty member in population health, Dr Lu Hu, fellows Drs Anna Y. Kharmats and Lauren Berube studied the specific effects of personalized versus standardized weight loss treatment arms on glycemic variability in the study participants.

At the University of Alabama at Birmingham center, 4 doctoral students and several undergraduate students participated in the basic and population projects. Two doctoral students, Drs Jessica Bahorski and Makenzie Callahan, have since graduated. Dr Bahorski is Assistant Professor at Florida State University, and Dr Callahan is continuing her education. The other 2 doctoral students, Bethany Moore and Alysha Everett, remain enrolled in the doctoral program. Among the undergraduate students, Cody Anger has since matriculated into medical school at University of Alabama at Birmingham and Natalie Presedo has matriculated into New York University Grossman School of Medicine.

At the Vanderbilt University center, 3 additional fellows were active in the center's research projects. Dr Megan Vogel worked on GLP‐1R signaling bias in the basic project and is now senior manager at Sarepta Therapeutics. Dr Mona Mashayekhi studied immunologic effects of GLP‐1R activation in obesity in the clinical project and is now an instructor of medicine. Dr Monica Bhanot studied macrophage iron handling in human adipose tissue in the clinical project and is now on the faculty at Erie County Medical Center in Buffalo, New York. Additionally, 4 Vanderbilt University undergraduate students participated in the basic project for several years; 2 of the students are matriculating in medical school and 2 of the students are matriculating in graduate school.

Collectively, despite limitations to recruitment and onboarding caused by the COVID‐19 pandemic, the named AHA fellows, as well as other members of the trainee community at the 4 centers, benefitted from the breadth of research, training, and career development opportunities, research meetings, seminars, and symposia.

Summary

In the aggregate, the work of the SFRN on Obesity highlights the complex pathophysiology of obesity as a disease and its impact on health over the life cycle. As illustrated in Figure 6, the 4 centers probed these mechanisms and consequences through fetal development into adulthood, both in animal models and in human participants.

Collaborative networks addressed the problems of overweight and obesity. The 4 centers of the American Heart Association Strategically Focused Research Network on Obesity on Obesity probed the mechanisms and consequences of overweight and obesity through an integrated approach during fetal development into adulthood, both in animal models and in human participants. Biological and social determinants of health were considered through an integrated multiorgan/multisystem approach. Strategically Focused Research Network on Obesity ©Copyright 2022 American Heart Association, Inc., a 501(c) (3) not‐for‐profit. Unauthorized use prohibited (4/22DS18987). NYU indicates New York University; and UAB, University of Alabama at Birmingham.

The efforts of these 4 centers focused on key pathophysiological processes contributing to the disease, including vascular dysfunction, insulin resistance, inflammation, thermogenesis, and dysregulated caloric intake, in addition to abnormalities in the mass, distribution and function of adipose tissue. These pathophysiological processes were studied at the level of key target organs and tissues participating in this multisystem disease, including blood vessels, skeletal muscle, liver, bone marrow and circulating peripheral blood immune cells, adipose tissue, and satiety hormone interaction with the hypothalamic feeding centers. A comprehensive translational approach probing cell‐intrinsic and cell–cell communication mechanisms in basic science projects as well as human physiology and public health in clinical and epidemiological studies was used. Ultimately, the complexity of obesity involves interactions among large sets of susceptibility genes, epigenetic modifications, behaviors, lifestyle factors and social determinants of health. These pathophysiological processes are operative in utero, affect the risk of obesity and cardiometabolic disease in children and adults, and can become intergenerational, carrying over into the next generation.

The data suggest that treatment and prevention must be comprehensive and operative over the life cycle. Primordial prevention involves the entire population and is needed because we live in an obesogenic environment. Some individuals will be at increased risk of obesity because of the inheritance of susceptibility genes and interactions involving lifestyle preferences and environmental determinants. These individuals require primary prevention that targets these specific risk factors to prevent the emergence of disease. Many individuals do ultimately develop obesity under the influence of these risk factors. In the absence of complications, clinicians are in a secondary treatment mode, and the goal is to prevent the emergence of complications and further weight gain. Important among these complications is the exacerbation of cardiometabolic disease leading to the end‐stage manifestations of type 2 diabetes, cardiovascular disease, and nonalcoholic fatty liver disease. Once these complications occur, tertiary treatment is required to prevent further disease deterioration and to achieve weight loss, and to identify other therapies sufficient to ameliorate the complications.

Although these centers emphasized mechanistic studies, the data also point to important aspects of prevention and treatment. Understanding whether there is a role for time‐restricted eating and how to incorporate this and other lifestyle modifications into lasting interventions at a population level remains to be seen. The importance of metabolic health in the mother during pregnancy points to the importance of a healthy body before conception to avoid adverse effects on the cardiometabolic health in the offspring extending into adulthood. However, lifestyle changes may not permanently alter the pathophysiological underpinnings of obesity, for example, inflammation, insulin resistance, and abnormal regulation of caloric intake by satiety factors. For this reason, medications that pharmacologically reverse these abnormalities may be needed. Importantly, there is a great need for further research. Because of the complexity and multisystem involvement in obesity and metabolic disease, we are far from being able to identify a rational and broad‐based strategy for disease prevention and management that could involve personalized care.

Through this SFRN on Obesity, innovative and serendipitous collaborations and partnerships were stimulated, and essential bridges linking basic, clinical, and population level research were born and are being cemented. Through the highly successful training program built from this SFRN, both through the fellows directly funded by the SFRN on Obesity and the many other fellows and trainees who were attracted to the programs, the future for obesity research and solutions is highly promising. This team of SFRN researchers looks forward to seeing the many next steps and achievements as they unfold.

Sources of Funding

This research was funded by the American Heart Association SFRNs as follows: 17SFRN33560006 to Johns Hopkins University School of Medicine; 17SFRN33490004 to New York University Grossman School of Medicine; 17SFRN33570038 to the University of Alabama at Birmingham; and 17SFRN33520017, 17SFRN33560015, 17SFRN33520059, and 17SFRN33590035 to the Vanderbilt University Medical Center.

Disclosures

Dr Brown is a member of the Scientific Advisory Board of Alnylam Pharmaceuticals and has equity in Johnson and Johnson. Dr Garvey has served as a volunteer consultant on advisory committees for Jazz Pharmaceuticals, Boehringer Ingelheim, Eli Lilly, Novo Nordisk, and Pfizer; in each case, he received no financial compensation. He has also been a paid consultant for Fractyl Health and Alnylam Pharmaceuticals. He has served as site principal investigator for clinical trials, sponsored by his university and funded by Eli Lilly, Novo Nordisk, Epitomee, and Pfizer. Dr Schmidt holds patents and patent applications through New York University Grossman School of Medicine that have been submitted/published that are related to the work of the New York University SFRN. The remaining authors have no disclosures to report.

Acknowledgments

The authors gratefully acknowledge the support of H. Caine, Research Operations Manager, Office of Science Operations, of the AHA. She has managed and supported this SFRN on Obesity since 2017. We are indebted to her for all of her support and expert management of the center grants and for the preparation of this article. The authors gratefully acknowledge the support of R. Elliott, AHA, in the preparation of the figure illustrations provided in this article. The authors are grateful to the oversight advisory committee for all of their support, insights, and critical review of the centers and their progress, including Dr Volgman, chair of the oversight advisory committee and Drs Ader, Hardin, Kumar, Mahle (oversight advisory committee vice chair), Powell‐Wiley, Pratt, Sun, Tian, Tristani‐Firouzi, and J.L. Young II. The authors gratefully acknowledge the expert assistance of L. Woods of the Diabetes Research Program, Department of Medicine, New York University Grossman School of Medicine, for her expert assistance in the preparation of this article. Drs Clark, Luther, Garvey, Niswender, and Schmidt wrote and edited the article. All other authors edited and agreed to the final version of the article.

For Sources of Funding and Disclosures, see page 16.

Contributor Information

Jeanne M. Clark, Email: jmclark@jhmi.edu.

W. Timothy Garvey, Email: garveyt@uab.edu.

Kevin D. Niswender, Email: kevin.niswender@vumc.org.

Ann Marie Schmidt, Email: annmarie.schmidt@nyumc.org, Email: jmclark@jhmi.edu.

References

1. National Institute of Diabetes and Digestive and Kidney Diseases . Overweight & obesity statistics. Available at: https://www.niddk.nih.gov/health‐information/health‐statistics/overweight‐obesity. Accessed January 6, 2023.
2. Centers for Disease Control and Prevention . Adult obesity facts. Available at: https://www.cdc.gov/obesity/data/adult.html. Accessed January 6, 2023.
3. Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes (Lond). 2008;32:1431–1437. doi: 10.1038/ijo.2008.102 [DOI] [PubMed] [Google Scholar]
4. May AL, Freedman D, Sherry B, Blanck HM. Obesity–United States, 1999‐2010. MMWR Suppl. 2013;62:120–128. [PubMed] [Google Scholar]
5. Centers for Disease Control and Prevention . Childhood overweight & obesity. Available at: https://www.cdc.gov/obesity/childhood/index.html. Accessed January 6, 2023.
6. Thomas‐Eapen N. Childhood obesity. Prim Care. 2021;48:505–515. doi: 10.1016/j.pop.2021.04.002 [DOI] [PubMed] [Google Scholar]
7. National Heart Lung and Blood Institute. Managing overweight and obesity in adults. Systematic evidence review from the Obesity Expert Panel, 2013 . Available at: https://www.nhlbi.nih.gov/sites/default/files/media/docs/obesity‐evidence‐review.pdf. Accessed January 6, 2023.
8. National Heart Lung and Blood Institute . Clinical guidelines on the identification, evaluation and treatment of overweight and obesity in adults. The evidence report. NO. 98–4083. Available at: https://www.nhlbi.nih.gov/files/docs/guidelines/ob_gdlns.pdf. Accessed January 6, 2023.
9. Johns Hopkins University of Medicine . Coronavirus resource center. Available at: https://coronavirus.jhu.edu/. Accessed January 6, 2023.
10. Dalamaga M, Christodoulatos GS, Karampela I, Vallianou N, Apovian CM. Understanding the co‐epidemic of obesity and COVID‐19: current evidence, comparison with previous epidemics, mechanisms, and preventive and therapeutic perspectives. Curr Obes Rep. 2021;10:214–243. doi: 10.1007/s13679-021-00436-y [DOI] [PMC free article] [PubMed] [Google Scholar]
11. Howell CR, Zhang L, Yi N, Mehta T, Cherrington AL, Garvey WT. Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID‐19 outcomes. Obesity (Silver Spring). 2022;30:1483–1494. doi: 10.1002/oby.23440 [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Tiwari A, Balasundaram P. Public health considerations regarding obesity. Treasure Island, Florida: StatPearls Publishing; 2021. [Updated September 3, 2022]. Available at: https://www.ncbi.nlm.nih.gov/books/NBK572122/. Accessed January 6, 2023. [PubMed] [Google Scholar]
13. Grannell A, Fallon F, Al‐Najim W, le Roux C. Obesity and responsibility: is it time to rethink agency? Obes Rev. 2021;22:e13270. doi: 10.1111/obr.13270 [DOI] [PubMed] [Google Scholar]
14. Garvey WT. Is obesity or adiposity‐based chronic disease curable: the set point theory, the environment, and second‐generation medications. Endocr Pract. 2022;28:214–222. doi: 10.1016/j.eprac.2021.11.082 [DOI] [PubMed] [Google Scholar]
15. Tu‐Sekine B, Padhi A, Jin S, Kalyan S, Singh K, Apperson M, Kapania R, Hur SC, Nain A, Kim SF. Inositol polyphosphate multikinase is a metformin target that regulates cell migration. FASEB J. 2019;33:14137–14146. doi: 10.1096/fj.201900717RR [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Goheer A, Holzhauer K, Martinez J, Woolf T, Coughlin JW, Martin L, Zhao D, Lehmann H, Clark JM, Bennett WL. What influences the "when" of eating and sleeping? A qualitative interview study. Appetite. 2021;156:104980. doi: 10.1016/j.appet.2020.104980 [DOI] [PubMed] [Google Scholar]
17. Woolf TB, Goheer A, Holzhauer K, Martinez J, Coughlin JW, Martin L, Zhao D, Song S, Ahmad Y, Sokolinskyi K, et al. Development of a mobile app for ecological momentary assessment of circadian data: design considerations and usability testing. JMIR Form Res. 2021;5:e26297. doi: 10.2196/26297 [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Lent MR, Atwood M, Bennett WL, Woolf TB, Martin L, Zhao D, Goheer AA, Song S, McTigue KM, Lehmann HP, et al. Night eating, weight, and health behaviors in adults participating in the Daily24 study. Eat Behav. 2022;45:101605. doi: 10.1016/j.eatbeh.2022.101605 [DOI] [PubMed] [Google Scholar]
19. Chen T, Chen Y, Gao J, Gao P, Moon JH, Ren J, Zhu R, Song S, Clark JM, Bennett W, et al. Machine learning to summarize and provide context for sleep and eating schedules. bioRxiv. 2021. 2020.2012.2031.424983. [Google Scholar]
20. Jung I‐R, Anokye‐Danso F, Jin S, Ahima RS, Kim SF. IPMK modulates insulin‐mediated suppression of hepatic glucose production. Journal of Cellular Physiology. 2022;237:3421–3432. doi: 10.1002/jcp.30827 [DOI] [PubMed] [Google Scholar]
21. Wu B, White K, Maw MT, Charleston J, Zhao D, Guallar E, Appel LJ, Clark JM, Maruthur NM, Pilla SJ. Adherence to diet and meal timing in a randomized controlled feeding study of time‐restricted feeding. Nutrients. 2022;14:2283. doi: 10.3390/nu14112283 [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Coughlin JW, Martin LM, Zhao D, Goheer A, Woolf TB, Holzhauer K, Lehmann HP, Lent MR, McTigue KM, Clark JM, et al. Electronic health record‐based recruitment and retention and mobile health app usage: multisite cohort study. J Med Internet Res. 2022;24:e34191. doi: 10.2196/34191 [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Zhao D, Guallar E, Woolf TB, Martin L, Lehmann H, Coughlin J, Holzhauer K, Goheer AA, McTigue KM, Lent MR, et al. Association of eating and sleeping intervals with weight change over time: the Daily24 cohort. J Am Heart Assoc. 2023:e026484. doi: 10.1161/JAHA.122.026484 [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Turkson‐Ocran RA, Foti K, Hines AL, Kamin Mukaz D, Kim H, Martin S, Minhas A, Norby FL, Ogungbe O, Razavi AC, et al. American Heart Association EPI|lifestyle scientific sessions: 2021 meeting highlights. J Am Heart Assoc. 2022;11:e024765. doi: 10.1161/JAHA.121.024765 [DOI] [PMC free article] [PubMed] [Google Scholar]
25. Tu‐Sekine B, Kim SF. The inositol phosphate system‐a coordinator of metabolic adaptability. Int J Mol Sci. 2022;23:6747. doi: 10.3390/ijms23126747 [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Hurtado Del Pozo C, Ruiz HH, Arivazhagan L, Aranda JF, Shim C, Daya P, Derk J, MacLean M, He M, Frye L, et al. A receptor of the immunoglobulin superfamily regulates adaptive thermogenesis. Cell Rep. 2019;28:773–791.e777. doi: 10.1016/j.celrep.2019.06.061 [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Popp CJ, Butler M, Curran M, Illiano P, Sevick MA, St‐Jules DE. Evaluating steady‐state resting energy expenditure using indirect calorimetry in adults with overweight and obesity. Clin Nutr. 2020;39:2220–2226. doi: 10.1016/j.clnu.2019.10.002 [DOI] [PubMed] [Google Scholar]
28. Popp CJ, Curran M, Wang C, Prasad M, Fine K, Gee A, Nair N, Perdomo K, Chen S, Hu L, et al. Temporal eating patterns and eating windows among adults with overweight or obesity. Nutrients. 2021;13:4485. doi: 10.3390/nu13124485 [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Popp CJ, St‐Jules DE, Hu L, Ganguzza L, Illiano P, Curran M, Li H, Schoenthaler A, Bergman M, Schmidt AM, et al. The rationale and design of the personal diet study, a randomized clinical trial evaluating a personalized approach to weight loss in individuals with pre‐diabetes and early‐stage type 2 diabetes. Contemp Clin Trials. 2019;79:80–88. doi: 10.1016/j.cct.2019.03.001 [DOI] [PubMed] [Google Scholar]
30. Popp CJ, Zhou B, Manigrasso MB, Li H, Curran M, Hu L, St‐Jules DE, Alemán JO, Vanegas SM, Jay M, et al. Soluble receptor for advanced glycation end products (sRAGE) isoforms predict changes in resting energy expenditure in adults with obesity during weight loss. Curr Dev Nutr. 2022;6:nzac046. doi: 10.1093/cdn/nzac046 [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Hu L, Illiano P, Pompeii ML, Popp CJ, Kharmats AY, Curran M, Perdomo K, Chen S, Bergman M, Segal E, et al. Challenges of conducting a remote behavioral weight loss study: lessons learned and a practical guide. Contemp Clin Trials. 2021;108:106522. doi: 10.1016/j.cct.2021.106522 [DOI] [PMC free article] [PubMed] [Google Scholar]
32. Ruiz HH, Nguyen A, Wang C, He L, Li H, Hallowell P, McNamara C, Schmidt AM. AGE/RAGE/DIAPH1 axis is associated with immunometabolic markers and risk of insulin resistance in subcutaneous but not omental adipose tissue in human obesity. Int J Obes (Lond). 2021;45:2083–2094. doi: 10.1038/s41366-021-00878-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Ruiz HH, Ramasamy R, Schmidt AM. Advanced glycation end products: building on the concept of the "common soil" in metabolic disease. Endocrinology. 2020;161:bqz006. doi: 10.1210/endocr/bqz006 [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Arivazhagan L, Ruiz HH, Wilson RA, Manigrasso MB, Gugger PF, Fisher EA, Moore KJ, Ramasamy R, Schmidt AM. An eclectic cast of cellular actors orchestrates innate immune responses in the mechanisms driving obesity and metabolic perturbation. Circ Res. 2020;126:1565–1589. doi: 10.1161/CIRCRESAHA.120.315900 [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Senatus L, MacLean M, Arivazhagan L, Egaña‐Gorroño L, López‐Díez R, Manigrasso MB, Ruiz HH, Vasquez C, Wilson R, Shekhtman A, et al. Inflammation meets metabolism: roles for the receptor for advanced glycation end products axis in cardiovascular disease. Immunometabolism. 2021;3:e210024. doi: 10.20900/immunometab20210024 [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Bruno J, Verano M, Vanegas SM, Weinshel E, Fielding CR, Lofton H, Fielding G, Schwack B, Chua DL, Wang C, et al. Body weight and prandial variation of plasma metabolites in subjects undergoing gastric band‐induced weight loss. Obesity Medicine. 2022;33:100434. doi: 10.1016/j.obmed.2022.100434 [DOI] [PMC free article] [PubMed] [Google Scholar]
37. Popp CJ, Hu L, Kharmats AY, Curran M, Berube L, Wang C, Pompeii ML, Illiano P, St‐Jules DE, Mottern M, et al. Advice of a personalized diet to reduce postprandial glycemic response vs a low‐fat diet on weight loss in adults with abnormal glucose metabolism and obesity. JAMA Network Open. 2022;5:e2233760. doi: 10.1001/jamanetworkopen.2022.33760 [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Popp CJ, Hu L, Kharmats AY, Curran M, Berube L, Wang C, Pompeii ML, Illiano P, St‐Jules DE, Mottern M, et al. Effect of a personalized diet to reduce postprandial glycemic response vs a low‐fat diet on weight loss in adults with abnormal glucose metabolism and obesity: a randomized clinical trial. JAMA Netw Open. 2022;5:e2233760. doi: 10.1001/jamanetworkopen.2022.33760 [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Manigrasso MB, Rabbani P, Egaña‐Gorroño L, Quadri N, Frye L, Zhou B, Reverdatto S, Ramirez LS, Dansereau S, Pan J, et al. Small‐molecule antagonism of the interaction of the RAGE cytoplasmic domain with DIAPH1 reduces diabetic complications in mice. Sci Transl Med. 2021;13:eabf7084. doi: 10.1126/scitranslmed.abf7084 [DOI] [PMC free article] [PubMed] [Google Scholar]
40. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben‐Yacov O, Lador D, Avnit‐Sagi T, Lotan‐Pompan M, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163:1079–1094. doi: 10.1016/j.cell.2015.11.001 [DOI] [PubMed] [Google Scholar]
41. Gaillard R, Steegers EA, Duijts L, Felix JF, Hofman A, Franco OH, Jaddoe VW. Childhood cardiometabolic outcomes of maternal obesity during pregnancy: the Generation R Study. Hypertension. 2014;63:683–691. doi: 10.1161/HYPERTENSIONAHA.113.02671 [DOI] [PubMed] [Google Scholar]
42. Chandler‐Laney P, Bush N, Granger W, Rouse D, Mancuso M, Gower B. Overweight status and intrauterine exposure to gestational diabetes are associated with children's metabolic health. Pediatr Obes. 2012;7:44–52. doi: 10.1111/j.2047-6310.2011.00009.x [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Lowe WL Jr, Lowe LP, Kuang A, Catalano PM, Nodzenski M, Talbot O, Tam WH, Sacks DA, McCance D, Linder B, et al. Maternal glucose levels during pregnancy and childhood adiposity in the hyperglycemia and adverse pregnancy outcome follow‐up study. Diabetologia. 2019;62:598–610. doi: 10.1007/s00125-018-4809-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Scholtens DM, Kuang A, Lowe LP, Hamilton J, Lawrence JM, Lebenthal Y, Brickman WJ, Clayton P, Ma RC, McCance D, et al. Hyperglycemia and Adverse Pregnancy Outcome Follow‐Up Study (HAPO FUS): maternal glycemia and childhood glucose metabolism. Diabetes Care. 2019;42:381–392. doi: 10.2337/dc18-2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Wright CS, Rifas‐Shiman SL, Rich‐Edwards JW, Taveras EM, Gillman MW, Oken E. Intrauterine exposure to gestational diabetes, child adiposity, and blood pressure. Am J Hypertens. 2009;22:215–220. doi: 10.1038/ajh.2008.326 [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Egeland GM, Meltzer SJ. Following in mother's footsteps? Mother‐daughter risks for insulin resistance and cardiovascular disease 15 years after gestational diabetes. Diabet Med. 2010;27:257–265. doi: 10.1111/j.1464-5491.2010.02944.x [DOI] [PubMed] [Google Scholar]
47. Catalano PM, Ehrenberg HM. The short‐ and long‐term implications of maternal obesity on the mother and her offspring. BJOG. 2006;113:1126–1133. doi: 10.1111/j.1471-0528.2006.00989.x [DOI] [PubMed] [Google Scholar]
48. Sertie R, Kang M, Antipenko JP, Liu X, Maianu L, Habegger K, Garvey WT. In utero nutritional stress as a cause of obesity: altered relationship between body fat, leptin levels and caloric intake in offspring into adulthood. Life Sci. 2020;254:117764. doi: 10.1016/j.lfs.2020.117764 [DOI] [PMC free article] [PubMed] [Google Scholar]
49. Martin SL, Zhang L, Callahan ML, Bahorski J, Lewis CE, Hidalgo BA, Durant N, Harper LM, Battarbee AN, Habegger K, et al. Mother‐child cardiometabolic health 4–10 years after pregnancy complicated by obesity with and without gestational diabetes. Obes Sci Pract. 2022;8:1–14. doi: 10.1002/osp4.599 [DOI] [PMC free article] [PubMed] [Google Scholar]
50. Martin SL, Callahan ML, Chandler‐Laney P. Association of gestational diabetes with child feeding practices: a secondary analysis using data from mother‐child dyads in the deep south. Appetite. 2020;151:104618. doi: 10.1016/j.appet.2020.104618 [DOI] [PMC free article] [PubMed] [Google Scholar]
51. De Luca M, Vecchie D, Athmanathan B, Gopalkrishna S, Valcin JA, Swain TM, Sertie R, Wekesa K, Rowe GC, Bailey SM, et al. Genetic deletion of Syndecan‐4 alters body composition, metabolic phenotypes, and the function of metabolic tissues in female mice fed a high‐fat diet. Nutrients. 2019;11:2810. doi: 10.3390/nu11112810 [DOI] [PMC free article] [PubMed] [Google Scholar]
52. Wilkinson L, Yi N, Mehta T, Judd S, Garvey WT. Development and validation of a model for predicting incident type 2 diabetes using quantitative clinical data and a Bayesian logistic model: a nationwide cohort and modeling study. PLoS Med. 2020;17:e1003232. doi: 10.1371/journal.pmed.1003232 [DOI] [PMC free article] [PubMed] [Google Scholar]
53. Martin SL, Phillips SR, Garr Barry V, Cedillo YE, Bahorski J, Callahan M, Garvey WT, Chandler‐Laney P. Household disorder and blood pressure in mother‐child dyads: a brief report [published online September 15, 2022]. J Fam Psychol. doi: 10.1037/fam0001032 [DOI] [PMC free article] [PubMed] [Google Scholar]
54. Moore BA, Callahan MA, Martin SL, Everett A, Garvey WT, Chandler‐Laney P. Associations among physical activity, adiposity, and insulin resistance in children exposed in utero to maternal obesity with and without gestational diabetes [published online December 21, 2022]. Pediatr Exerc Sci. doi: 10.1123/pes.2021-0222 [DOI] [PMC free article] [PubMed] [Google Scholar]
55. Wunderlich CM, Hövelmeyer N, Wunderlich FT. Mechanisms of chronic JAK‐STAT3‐SOCS3 signaling in obesity. JAKSTAT. 2013;2:e23878. doi: 10.4161/jkst.23878 [DOI] [PMC free article] [PubMed] [Google Scholar]
56. Lowe WL Jr, Scholtens DM, Kuang A, Linder B, Lawrence JM, Lebenthal Y, McCance D, Hamilton J, Nodzenski M, Talbot O, et al. Hyperglycemia and Adverse Pregnancy Outcome Follow‐Up Study (HAPO FUS): maternal gestational diabetes mellitus and childhood glucose metabolism. Diabetes Care. 2019;42:372–380. doi: 10.2337/dc18-1646 [DOI] [PMC free article] [PubMed] [Google Scholar]
57. Mashayekhi M, Beckman JA, Nian H, Garner EM, Mayfield D, Devin JK, Koethe JR, Brown JD, Cahill KN, Yu C, et al. Comparative effects of weight loss and incretin‐based therapies on endothelial vasodilatory endothelial function, fibrinolysis and inflammation in individuals with obesity and prediabetes: a randomized controlled trial. Diabetes Obes Metab. 2023;25:570–580. [DOI] [PMC free article] [PubMed] [Google Scholar]
58. Shuey MM, Huang S, Levinson RT, Farber‐Eger E, Cahill KN, Beckman JA, Koethe JR, Silver HJ, Niswender KD, Cox NJ, et al. Exploration of an alternative to body mass index to characterize the relationship between height and weight for prediction of metabolic phenotypes and cardiovascular outcomes. Obes Sci & Pract. 2022;8:124–130. doi: 10.1002/osp4.543 [DOI] [PMC free article] [PubMed] [Google Scholar]
59. Shuey MM, Xiang RR, Moss ME, Carvajal BV, Wang Y, Camarda N, Fabbri D, Rahman P, Ramsey J, Stepanian A, et al. Systems approach to integrating preclinical apolipoprotein E‐knockout investigations reveals novel etiologic pathways and master atherosclerosis network in humans. Arterioscler Thromb Vasc Biol. 2022;42:35–48. doi: 10.1161/ATVBAHA.121.317071 [DOI] [PMC free article] [PubMed] [Google Scholar]
60. Cahill KN, Amin T, Boutaud O, Printz R, Newcomb DC, Foer D, Hodson DJ, Broichhagen J, Beckman JA, Yu C, et al. Glucagon‐like peptide‐1 receptor regulates thromboxane induced human platelet activation. JACC Card Basic Transl Sci. 2022;7:713–715. doi: 10.1016/j.jacbts.2022.04.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
61. Mashayekhi M, Wilson JR, Jafarian‐Kerman S, Nian H, Yu C, Shuey MM, Luther JM, Brown NJ. Association of a glucagon‐like peptide‐1 receptor gene variant with glucose response to a mixed meal. Diabetes Obes Metab. 2021;23:281–286. doi: 10.1111/dom.14216 [DOI] [PMC free article] [PubMed] [Google Scholar]
62. Mosley JD, Levinson RT, Farber‐Eger E, Edwards TL, Hellwege JN, Hung AM, Giri A, Shuey MM, Shaffer CM, Shi M, et al. The polygenic architecture of left ventricular mass mirrors the clinical epidemiology. Sci Rep. 2020;10:7561. doi: 10.1038/s41598-020-64525-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0001] 1. National Institute of Diabetes and Digestive and Kidney Diseases . Overweight & obesity statistics. Available at: https://www.niddk.nih.gov/health‐information/health‐statistics/overweight‐obesity. Accessed January 6, 2023.

[jah38192-bib-0002] 2. Centers for Disease Control and Prevention . Adult obesity facts. Available at: https://www.cdc.gov/obesity/data/adult.html. Accessed January 6, 2023.

[jah38192-bib-0003] 3. Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes (Lond). 2008;32:1431–1437. doi: 10.1038/ijo.2008.102 [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0004] 4. May AL, Freedman D, Sherry B, Blanck HM. Obesity–United States, 1999‐2010. MMWR Suppl. 2013;62:120–128. [PubMed] [Google Scholar]

[jah38192-bib-0005] 5. Centers for Disease Control and Prevention . Childhood overweight & obesity. Available at: https://www.cdc.gov/obesity/childhood/index.html. Accessed January 6, 2023.

[jah38192-bib-0006] 6. Thomas‐Eapen N. Childhood obesity. Prim Care. 2021;48:505–515. doi: 10.1016/j.pop.2021.04.002 [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0007] 7. National Heart Lung and Blood Institute. Managing overweight and obesity in adults. Systematic evidence review from the Obesity Expert Panel, 2013 . Available at: https://www.nhlbi.nih.gov/sites/default/files/media/docs/obesity‐evidence‐review.pdf. Accessed January 6, 2023.

[jah38192-bib-0008] 8. National Heart Lung and Blood Institute . Clinical guidelines on the identification, evaluation and treatment of overweight and obesity in adults. The evidence report. NO. 98–4083. Available at: https://www.nhlbi.nih.gov/files/docs/guidelines/ob_gdlns.pdf. Accessed January 6, 2023.

[jah38192-bib-0009] 9. Johns Hopkins University of Medicine . Coronavirus resource center. Available at: https://coronavirus.jhu.edu/. Accessed January 6, 2023.

[jah38192-bib-0010] 10. Dalamaga M, Christodoulatos GS, Karampela I, Vallianou N, Apovian CM. Understanding the co‐epidemic of obesity and COVID‐19: current evidence, comparison with previous epidemics, mechanisms, and preventive and therapeutic perspectives. Curr Obes Rep. 2021;10:214–243. doi: 10.1007/s13679-021-00436-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0011] 11. Howell CR, Zhang L, Yi N, Mehta T, Cherrington AL, Garvey WT. Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID‐19 outcomes. Obesity (Silver Spring). 2022;30:1483–1494. doi: 10.1002/oby.23440 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0012] 12. Tiwari A, Balasundaram P. Public health considerations regarding obesity. Treasure Island, Florida: StatPearls Publishing; 2021. [Updated September 3, 2022]. Available at: https://www.ncbi.nlm.nih.gov/books/NBK572122/. Accessed January 6, 2023. [PubMed] [Google Scholar]

[jah38192-bib-0013] 13. Grannell A, Fallon F, Al‐Najim W, le Roux C. Obesity and responsibility: is it time to rethink agency? Obes Rev. 2021;22:e13270. doi: 10.1111/obr.13270 [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0014] 14. Garvey WT. Is obesity or adiposity‐based chronic disease curable: the set point theory, the environment, and second‐generation medications. Endocr Pract. 2022;28:214–222. doi: 10.1016/j.eprac.2021.11.082 [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0015] 15. Tu‐Sekine B, Padhi A, Jin S, Kalyan S, Singh K, Apperson M, Kapania R, Hur SC, Nain A, Kim SF. Inositol polyphosphate multikinase is a metformin target that regulates cell migration. FASEB J. 2019;33:14137–14146. doi: 10.1096/fj.201900717RR [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0016] 16. Goheer A, Holzhauer K, Martinez J, Woolf T, Coughlin JW, Martin L, Zhao D, Lehmann H, Clark JM, Bennett WL. What influences the "when" of eating and sleeping? A qualitative interview study. Appetite. 2021;156:104980. doi: 10.1016/j.appet.2020.104980 [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0017] 17. Woolf TB, Goheer A, Holzhauer K, Martinez J, Coughlin JW, Martin L, Zhao D, Song S, Ahmad Y, Sokolinskyi K, et al. Development of a mobile app for ecological momentary assessment of circadian data: design considerations and usability testing. JMIR Form Res. 2021;5:e26297. doi: 10.2196/26297 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0018] 18. Lent MR, Atwood M, Bennett WL, Woolf TB, Martin L, Zhao D, Goheer AA, Song S, McTigue KM, Lehmann HP, et al. Night eating, weight, and health behaviors in adults participating in the Daily24 study. Eat Behav. 2022;45:101605. doi: 10.1016/j.eatbeh.2022.101605 [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0019] 19. Chen T, Chen Y, Gao J, Gao P, Moon JH, Ren J, Zhu R, Song S, Clark JM, Bennett W, et al. Machine learning to summarize and provide context for sleep and eating schedules. bioRxiv. 2021. 2020.2012.2031.424983. [Google Scholar]

[jah38192-bib-0020] 20. Jung I‐R, Anokye‐Danso F, Jin S, Ahima RS, Kim SF. IPMK modulates insulin‐mediated suppression of hepatic glucose production. Journal of Cellular Physiology. 2022;237:3421–3432. doi: 10.1002/jcp.30827 [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0021] 21. Wu B, White K, Maw MT, Charleston J, Zhao D, Guallar E, Appel LJ, Clark JM, Maruthur NM, Pilla SJ. Adherence to diet and meal timing in a randomized controlled feeding study of time‐restricted feeding. Nutrients. 2022;14:2283. doi: 10.3390/nu14112283 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0022] 22. Coughlin JW, Martin LM, Zhao D, Goheer A, Woolf TB, Holzhauer K, Lehmann HP, Lent MR, McTigue KM, Clark JM, et al. Electronic health record‐based recruitment and retention and mobile health app usage: multisite cohort study. J Med Internet Res. 2022;24:e34191. doi: 10.2196/34191 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0023] 23. Zhao D, Guallar E, Woolf TB, Martin L, Lehmann H, Coughlin J, Holzhauer K, Goheer AA, McTigue KM, Lent MR, et al. Association of eating and sleeping intervals with weight change over time: the Daily24 cohort. J Am Heart Assoc. 2023:e026484. doi: 10.1161/JAHA.122.026484 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0024] 24. Turkson‐Ocran RA, Foti K, Hines AL, Kamin Mukaz D, Kim H, Martin S, Minhas A, Norby FL, Ogungbe O, Razavi AC, et al. American Heart Association EPI|lifestyle scientific sessions: 2021 meeting highlights. J Am Heart Assoc. 2022;11:e024765. doi: 10.1161/JAHA.121.024765 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0025] 25. Tu‐Sekine B, Kim SF. The inositol phosphate system‐a coordinator of metabolic adaptability. Int J Mol Sci. 2022;23:6747. doi: 10.3390/ijms23126747 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0026] 26. Hurtado Del Pozo C, Ruiz HH, Arivazhagan L, Aranda JF, Shim C, Daya P, Derk J, MacLean M, He M, Frye L, et al. A receptor of the immunoglobulin superfamily regulates adaptive thermogenesis. Cell Rep. 2019;28:773–791.e777. doi: 10.1016/j.celrep.2019.06.061 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0027] 27. Popp CJ, Butler M, Curran M, Illiano P, Sevick MA, St‐Jules DE. Evaluating steady‐state resting energy expenditure using indirect calorimetry in adults with overweight and obesity. Clin Nutr. 2020;39:2220–2226. doi: 10.1016/j.clnu.2019.10.002 [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0028] 28. Popp CJ, Curran M, Wang C, Prasad M, Fine K, Gee A, Nair N, Perdomo K, Chen S, Hu L, et al. Temporal eating patterns and eating windows among adults with overweight or obesity. Nutrients. 2021;13:4485. doi: 10.3390/nu13124485 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0029] 29. Popp CJ, St‐Jules DE, Hu L, Ganguzza L, Illiano P, Curran M, Li H, Schoenthaler A, Bergman M, Schmidt AM, et al. The rationale and design of the personal diet study, a randomized clinical trial evaluating a personalized approach to weight loss in individuals with pre‐diabetes and early‐stage type 2 diabetes. Contemp Clin Trials. 2019;79:80–88. doi: 10.1016/j.cct.2019.03.001 [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0030] 30. Popp CJ, Zhou B, Manigrasso MB, Li H, Curran M, Hu L, St‐Jules DE, Alemán JO, Vanegas SM, Jay M, et al. Soluble receptor for advanced glycation end products (sRAGE) isoforms predict changes in resting energy expenditure in adults with obesity during weight loss. Curr Dev Nutr. 2022;6:nzac046. doi: 10.1093/cdn/nzac046 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0031] 31. Hu L, Illiano P, Pompeii ML, Popp CJ, Kharmats AY, Curran M, Perdomo K, Chen S, Bergman M, Segal E, et al. Challenges of conducting a remote behavioral weight loss study: lessons learned and a practical guide. Contemp Clin Trials. 2021;108:106522. doi: 10.1016/j.cct.2021.106522 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0032] 32. Ruiz HH, Nguyen A, Wang C, He L, Li H, Hallowell P, McNamara C, Schmidt AM. AGE/RAGE/DIAPH1 axis is associated with immunometabolic markers and risk of insulin resistance in subcutaneous but not omental adipose tissue in human obesity. Int J Obes (Lond). 2021;45:2083–2094. doi: 10.1038/s41366-021-00878-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0033] 33. Ruiz HH, Ramasamy R, Schmidt AM. Advanced glycation end products: building on the concept of the "common soil" in metabolic disease. Endocrinology. 2020;161:bqz006. doi: 10.1210/endocr/bqz006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0034] 34. Arivazhagan L, Ruiz HH, Wilson RA, Manigrasso MB, Gugger PF, Fisher EA, Moore KJ, Ramasamy R, Schmidt AM. An eclectic cast of cellular actors orchestrates innate immune responses in the mechanisms driving obesity and metabolic perturbation. Circ Res. 2020;126:1565–1589. doi: 10.1161/CIRCRESAHA.120.315900 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0035] 35. Senatus L, MacLean M, Arivazhagan L, Egaña‐Gorroño L, López‐Díez R, Manigrasso MB, Ruiz HH, Vasquez C, Wilson R, Shekhtman A, et al. Inflammation meets metabolism: roles for the receptor for advanced glycation end products axis in cardiovascular disease. Immunometabolism. 2021;3:e210024. doi: 10.20900/immunometab20210024 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0036] 36. Bruno J, Verano M, Vanegas SM, Weinshel E, Fielding CR, Lofton H, Fielding G, Schwack B, Chua DL, Wang C, et al. Body weight and prandial variation of plasma metabolites in subjects undergoing gastric band‐induced weight loss. Obesity Medicine. 2022;33:100434. doi: 10.1016/j.obmed.2022.100434 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0037] 37. Popp CJ, Hu L, Kharmats AY, Curran M, Berube L, Wang C, Pompeii ML, Illiano P, St‐Jules DE, Mottern M, et al. Advice of a personalized diet to reduce postprandial glycemic response vs a low‐fat diet on weight loss in adults with abnormal glucose metabolism and obesity. JAMA Network Open. 2022;5:e2233760. doi: 10.1001/jamanetworkopen.2022.33760 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0038] 38. Popp CJ, Hu L, Kharmats AY, Curran M, Berube L, Wang C, Pompeii ML, Illiano P, St‐Jules DE, Mottern M, et al. Effect of a personalized diet to reduce postprandial glycemic response vs a low‐fat diet on weight loss in adults with abnormal glucose metabolism and obesity: a randomized clinical trial. JAMA Netw Open. 2022;5:e2233760. doi: 10.1001/jamanetworkopen.2022.33760 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0039] 39. Manigrasso MB, Rabbani P, Egaña‐Gorroño L, Quadri N, Frye L, Zhou B, Reverdatto S, Ramirez LS, Dansereau S, Pan J, et al. Small‐molecule antagonism of the interaction of the RAGE cytoplasmic domain with DIAPH1 reduces diabetic complications in mice. Sci Transl Med. 2021;13:eabf7084. doi: 10.1126/scitranslmed.abf7084 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0040] 40. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben‐Yacov O, Lador D, Avnit‐Sagi T, Lotan‐Pompan M, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163:1079–1094. doi: 10.1016/j.cell.2015.11.001 [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0041] 41. Gaillard R, Steegers EA, Duijts L, Felix JF, Hofman A, Franco OH, Jaddoe VW. Childhood cardiometabolic outcomes of maternal obesity during pregnancy: the Generation R Study. Hypertension. 2014;63:683–691. doi: 10.1161/HYPERTENSIONAHA.113.02671 [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0042] 42. Chandler‐Laney P, Bush N, Granger W, Rouse D, Mancuso M, Gower B. Overweight status and intrauterine exposure to gestational diabetes are associated with children's metabolic health. Pediatr Obes. 2012;7:44–52. doi: 10.1111/j.2047-6310.2011.00009.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0043] 43. Lowe WL Jr, Lowe LP, Kuang A, Catalano PM, Nodzenski M, Talbot O, Tam WH, Sacks DA, McCance D, Linder B, et al. Maternal glucose levels during pregnancy and childhood adiposity in the hyperglycemia and adverse pregnancy outcome follow‐up study. Diabetologia. 2019;62:598–610. doi: 10.1007/s00125-018-4809-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0044] 44. Scholtens DM, Kuang A, Lowe LP, Hamilton J, Lawrence JM, Lebenthal Y, Brickman WJ, Clayton P, Ma RC, McCance D, et al. Hyperglycemia and Adverse Pregnancy Outcome Follow‐Up Study (HAPO FUS): maternal glycemia and childhood glucose metabolism. Diabetes Care. 2019;42:381–392. doi: 10.2337/dc18-2021 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0045] 45. Wright CS, Rifas‐Shiman SL, Rich‐Edwards JW, Taveras EM, Gillman MW, Oken E. Intrauterine exposure to gestational diabetes, child adiposity, and blood pressure. Am J Hypertens. 2009;22:215–220. doi: 10.1038/ajh.2008.326 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0046] 46. Egeland GM, Meltzer SJ. Following in mother's footsteps? Mother‐daughter risks for insulin resistance and cardiovascular disease 15 years after gestational diabetes. Diabet Med. 2010;27:257–265. doi: 10.1111/j.1464-5491.2010.02944.x [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0047] 47. Catalano PM, Ehrenberg HM. The short‐ and long‐term implications of maternal obesity on the mother and her offspring. BJOG. 2006;113:1126–1133. doi: 10.1111/j.1471-0528.2006.00989.x [DOI] [PubMed] [Google Scholar]

[jah38192-bib-0048] 48. Sertie R, Kang M, Antipenko JP, Liu X, Maianu L, Habegger K, Garvey WT. In utero nutritional stress as a cause of obesity: altered relationship between body fat, leptin levels and caloric intake in offspring into adulthood. Life Sci. 2020;254:117764. doi: 10.1016/j.lfs.2020.117764 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0049] 49. Martin SL, Zhang L, Callahan ML, Bahorski J, Lewis CE, Hidalgo BA, Durant N, Harper LM, Battarbee AN, Habegger K, et al. Mother‐child cardiometabolic health 4–10 years after pregnancy complicated by obesity with and without gestational diabetes. Obes Sci Pract. 2022;8:1–14. doi: 10.1002/osp4.599 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0050] 50. Martin SL, Callahan ML, Chandler‐Laney P. Association of gestational diabetes with child feeding practices: a secondary analysis using data from mother‐child dyads in the deep south. Appetite. 2020;151:104618. doi: 10.1016/j.appet.2020.104618 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0051] 51. De Luca M, Vecchie D, Athmanathan B, Gopalkrishna S, Valcin JA, Swain TM, Sertie R, Wekesa K, Rowe GC, Bailey SM, et al. Genetic deletion of Syndecan‐4 alters body composition, metabolic phenotypes, and the function of metabolic tissues in female mice fed a high‐fat diet. Nutrients. 2019;11:2810. doi: 10.3390/nu11112810 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0052] 52. Wilkinson L, Yi N, Mehta T, Judd S, Garvey WT. Development and validation of a model for predicting incident type 2 diabetes using quantitative clinical data and a Bayesian logistic model: a nationwide cohort and modeling study. PLoS Med. 2020;17:e1003232. doi: 10.1371/journal.pmed.1003232 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0053] 53. Martin SL, Phillips SR, Garr Barry V, Cedillo YE, Bahorski J, Callahan M, Garvey WT, Chandler‐Laney P. Household disorder and blood pressure in mother‐child dyads: a brief report [published online September 15, 2022]. J Fam Psychol. doi: 10.1037/fam0001032 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0054] 54. Moore BA, Callahan MA, Martin SL, Everett A, Garvey WT, Chandler‐Laney P. Associations among physical activity, adiposity, and insulin resistance in children exposed in utero to maternal obesity with and without gestational diabetes [published online December 21, 2022]. Pediatr Exerc Sci. doi: 10.1123/pes.2021-0222 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0055] 55. Wunderlich CM, Hövelmeyer N, Wunderlich FT. Mechanisms of chronic JAK‐STAT3‐SOCS3 signaling in obesity. JAKSTAT. 2013;2:e23878. doi: 10.4161/jkst.23878 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0056] 56. Lowe WL Jr, Scholtens DM, Kuang A, Linder B, Lawrence JM, Lebenthal Y, McCance D, Hamilton J, Nodzenski M, Talbot O, et al. Hyperglycemia and Adverse Pregnancy Outcome Follow‐Up Study (HAPO FUS): maternal gestational diabetes mellitus and childhood glucose metabolism. Diabetes Care. 2019;42:372–380. doi: 10.2337/dc18-1646 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0057] 57. Mashayekhi M, Beckman JA, Nian H, Garner EM, Mayfield D, Devin JK, Koethe JR, Brown JD, Cahill KN, Yu C, et al. Comparative effects of weight loss and incretin‐based therapies on endothelial vasodilatory endothelial function, fibrinolysis and inflammation in individuals with obesity and prediabetes: a randomized controlled trial. Diabetes Obes Metab. 2023;25:570–580. [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0058] 58. Shuey MM, Huang S, Levinson RT, Farber‐Eger E, Cahill KN, Beckman JA, Koethe JR, Silver HJ, Niswender KD, Cox NJ, et al. Exploration of an alternative to body mass index to characterize the relationship between height and weight for prediction of metabolic phenotypes and cardiovascular outcomes. Obes Sci & Pract. 2022;8:124–130. doi: 10.1002/osp4.543 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0059] 59. Shuey MM, Xiang RR, Moss ME, Carvajal BV, Wang Y, Camarda N, Fabbri D, Rahman P, Ramsey J, Stepanian A, et al. Systems approach to integrating preclinical apolipoprotein E‐knockout investigations reveals novel etiologic pathways and master atherosclerosis network in humans. Arterioscler Thromb Vasc Biol. 2022;42:35–48. doi: 10.1161/ATVBAHA.121.317071 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0060] 60. Cahill KN, Amin T, Boutaud O, Printz R, Newcomb DC, Foer D, Hodson DJ, Broichhagen J, Beckman JA, Yu C, et al. Glucagon‐like peptide‐1 receptor regulates thromboxane induced human platelet activation. JACC Card Basic Transl Sci. 2022;7:713–715. doi: 10.1016/j.jacbts.2022.04.004 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0061] 61. Mashayekhi M, Wilson JR, Jafarian‐Kerman S, Nian H, Yu C, Shuey MM, Luther JM, Brown NJ. Association of a glucagon‐like peptide‐1 receptor gene variant with glucose response to a mixed meal. Diabetes Obes Metab. 2021;23:281–286. doi: 10.1111/dom.14216 [DOI] [PMC free article] [PubMed] [Google Scholar]

[jah38192-bib-0062] 62. Mosley JD, Levinson RT, Farber‐Eger E, Edwards TL, Hellwege JN, Hung AM, Giri A, Shuey MM, Shaffer CM, Shi M, et al. The polygenic architecture of left ventricular mass mirrors the clinical epidemiology. Sci Rep. 2020;10:7561. doi: 10.1038/s41598-020-64525-z [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Obesity and Overweight: Probing Causes, Consequences, and Novel Therapeutic Approaches Through the American Heart Association's Strategically Focused Research Network

Jeanne M Clark, MD, MPH

W Timothy Garvey, MD

Kevin D Niswender, MD, PhD

Ann Marie Schmidt, MD

Rexford S Ahima, MD, PhD

Jose O Aleman, MD, PhD

Ashley N Battarbee, MD

Joshua Beckman, MD, MSc

Wendy L Bennett, MD, MPH

Nancy J Brown, MD

Paula Chandler‐Laney, PhD

Nancy Cox, PhD

Ira J Goldberg, MD

Kirk M Habegger, PhD

Lorie M Harper, MD

Alyssa H Hasty, PhD

Bertha A Hidalgo, PhD

Sangwon F Kim, PhD

Julie L Locher, PhD

James M Luther, MD, MSCI

Nisa M Maruthur, MD, MHS

Edgar R Miller, MD, PhD

Mary Ann Sevick, ScD

Quinn Wells, MD, PharmD, MSCI

Abstract

Nonstandard Abbreviations and Acronyms

History of the SFRN

Figure 1. The Strategically Focused Research Network on Obesity.

Obesity SFRNs: Goals and Results

Johns Hopkins University School of Medicine Obesity Center: The Role of TRF on Obesity and Cardiometabolic Health

Central Theme

Figure 2. Key accomplishments of the Johns Hopkins University center.

Basic Project

Objectives

Major Findings

Clinical Project

Objectives

Major Findings

Population Project

Objectives

Major Findings

New York University Grossman School of Medicine: Braking Inflammation in Obesity and Metabolic Dysfunction: Translational and Therapeutic Opportunities

Central Theme

Figure 3. Key accomplishments of the New York University center.

Basic Project

Objectives

Major Findings

Clinical Project

Objectives

Major Findings

Population Project

Objectives

Major Findings

University of Alabama at Birmingham Center: University of Alabama at Birmingham Center Strategically Focused Obesity Center: Intergenerational Transmission of Obesity

Central Theme

Figure 4. Key accomplishments of the University of Alabama at Birmingham center.

Basic Project

Objective

Major Findings

Clinical Project

Objective

Major Findings

Population Project

Objective

Major Findings

Vanderbilt University Medical Center: Toward Obesity Precision Medicine: Promise of the Glucagon‐Like Peptide 1 Receptor

Objective

Figure 5. Key accomplishments of the Vanderbilt University center.

Basic Project

Objective

Major Findings

Clinical Project

Objectives

Major Findings

Population Project

Objectives

Major Findings

Center Collaborations