Examination of metabolic characteristics in the predisposition for weight gain

Active Project Grant Awarded Last action

Award ID
F32DK145220
Recipient
Awarding agency
Obligated
$79,552

Award facts

Completion date 10% complete
Place of performance
Baton Rouge, LA 70808, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
Obesity, often characterized by weight gain from over-nutrition, is one of the greatest risk factors of preventable morbidity and mortality worldwide. Obesity is evident in >40% of US adults, with prevalence projected to reach 50% by 2030. Driven by a multitude of both modifiable and non-modifiable factors, there is observed variation in the magnitude of weight gained in both controlled and free-living settings. This observation was repeated in the largest controlled overfeeding trial to date, eat (NCT01672632; n=35), and in the 8-year weight maintenance data from the look ahead trial (NCT00017953; n=4,127). Individual factors including genetics, insulin sensitivity, and substrate utilization have been shown to play a significant role in the predisposition for weight gain. However, the combined roles of factors driving variability in weight gain remain poorly understood. The overarching goal of this innovative F32 fellowship is to identify metabolic factors predictive of weight gain ahead). In aim 1, we will leverage state-of-the-art metabolic phenotyping methodology to identify metabolic characteristics that explain variation in two differing weight gain paradigms: controlled overfeeding (eat) and prospective weight regain (look ahead-ancillary study n=59). In sub-aim 1, we will use untargeted metabolomics to explain additional variability in weight gain in response to controlled overfeeding . Aim 2 will then utilize machine learning approaches to confirm predictive factors of weight regain utilizing routine clinical measures (look ahead). Addressing these aims will provide new information regarding the "ground truth" of metabolic predisposition towards weight gain and generate evidence for characterizing a phenotype of likely weight gain that will have future implications in guiding personalized approaches to healthy weight management. This secondary analysis will be supported by mentored and hands-on training in 1) metabolic phenotyping methods, 2) advanced biostatistics including machine learning, and 3) professional and career development. The applicant will work closely with her primary sponsor, DR. Ursula white (an expert in metabolic phenotyping), and her CO-sponsors DR. Leanne redman (expert in energy metabolism and precision nutrition), DR. Shengping yang (expert in biostatistics and machine learning), and DR. Christopher newgard (expert in metabolomics analyses) and consult DR. Diana thomas (expert in machine learning and predictive modeling). This comprehensive training plan, in combination with the resources available through pennington biomedical research center, provides ideal conditions to foster the applicant's future independent research career. This is an exceptional mentoring and training opportunity to build new expertise in metabolic phenotyping, adding to the applicant's existing expertise as a registered dietician and nutritional scientists leading to the applicant's ability to contribute to high impact cross-disciplinary approaches to address questions regarding tailoring dietary approaches for healthy weight management and improved cardiometabolic health.

Transactions

Action Obligated Date
Amendment -$79,552
Amendment $79,552
Original award $79,552

Quick answers

Who received award F32DK145220?

F32DK145220 was awarded to Pennington Biomedical Research Center by National Institute of Diabetes and Digestive and Kidney Diseases (National Institutes of Health) on September 18, 2026.

How much is award F32DK145220 worth?

$79,552 has been obligated on F32DK145220.

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