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Wei SJ, Schell JR, Chocron ES, Varmazyad M, Xu G, Chen WH, Martinez GM, Dong FF, Sreenivas P, Trevino R, Jiang H, Du Y, Saliba A, Qian W, Lorenzana B, Nazarullah A, Chang J, Sharma K, Munkácsy E, Horikoshi N, Gius D. Ketogenic diet induces p53-dependent cellular senescence in multiple organs. SCIENCE ADVANCES 2024; 10:eado1463. [PMID: 38758782 PMCID: PMC11100565 DOI: 10.1126/sciadv.ado1463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/12/2024] [Indexed: 05/19/2024]
Abstract
A ketogenic diet (KD) is a high-fat, low-carbohydrate diet that leads to the generation of ketones. While KDs improve certain health conditions and are popular for weight loss, detrimental effects have also been reported. Here, we show mice on two different KDs and, at different ages, induce cellular senescence in multiple organs, including the heart and kidney. This effect is mediated through adenosine monophosphate-activated protein kinase (AMPK) and inactivation of mouse double minute 2 (MDM2) by caspase-2, leading to p53 accumulation and p21 induction. This was established using p53 and caspase-2 knockout mice and inhibitors to AMPK, p21, and caspase-2. In addition, senescence-associated secretory phenotype biomarkers were elevated in serum from mice on a KD and in plasma samples from patients on a KD clinical trial. Cellular senescence was eliminated by a senolytic and prevented by an intermittent KD. These results have important clinical implications, suggesting that the effects of a KD are contextual and likely require individual optimization.
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Affiliation(s)
- Sung-Jen Wei
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Joseph R. Schell
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - E. Sandra Chocron
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Mahboubeh Varmazyad
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Guogang Xu
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Wan Hsi Chen
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Gloria M. Martinez
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Felix F. Dong
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Prethish Sreenivas
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Rolando Trevino
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Haiyan Jiang
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Yan Du
- Center for Precision Medicine, UT Health San Antonio, San Antonio, TX, USA
- School of Nursing, UT Health San Antonio, San Antonio, TX, USA
| | - Afaf Saliba
- Center for Precision Medicine, UT Health San Antonio, San Antonio, TX, USA
| | - Wei Qian
- Houston Methodist Cancer Center, Houston, TX, USA
- Houston Methodist Research Institute, Houston, TX, USA
| | - Brandon Lorenzana
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Alia Nazarullah
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Jenny Chang
- Houston Methodist Cancer Center, Houston, TX, USA
- Houston Methodist Research Institute, Houston, TX, USA
| | - Kumar Sharma
- Center for Precision Medicine, UT Health San Antonio, San Antonio, TX, USA
- Division of Nephrology, Department of Medicine, UT Health San Antonio, San Antonio, TX, USA
| | - Erin Munkácsy
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - Nobuo Horikoshi
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
| | - David Gius
- Department of Radiation Oncology, Mays Cancer Center at UT Health San Antonio MD Anderson, Joe R. and Teresa Lozano Long School of Medicine, San Antonio, TX, USA
- Barshop Institute for Longevity and Aging Studies at UT Health San Antonio, San Antonio, TX, USA
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Li S, Du Y, Miao H, Sharma K, Li C, Yin Z, Brimhall B, Wang J. Understanding Heterogeneity in Individual Responses to Digital Lifestyle Intervention Through Self-Monitoring Adherence Trajectories in Adults With Overweight or Obesity: Secondary Analysis of a 6-Month Randomized Controlled Trial. J Med Internet Res 2024; 26:e53294. [PMID: 38506903 PMCID: PMC10993111 DOI: 10.2196/53294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/01/2024] [Accepted: 01/31/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Achieving clinically significant weight loss through lifestyle interventions for obesity management is challenging for most individuals. Improving intervention effectiveness involves early identification of intervention nonresponders and providing them with timely, tailored interventions. Early and frequent self-monitoring (SM) adherence predicts later weight loss success, making it a potential indicator for identifying nonresponders in the initial phase. OBJECTIVE This study aims to identify clinically meaningful participant subgroups based on longitudinal adherence to SM of diet, activity, and weight over 6 months as well as psychological predictors of participant subgroups from a self-determination theory (SDT) perspective. METHODS This was a secondary data analysis of a 6-month digital lifestyle intervention for adults with overweight or obesity. The participants were instructed to perform daily SM on 3 targets: diet, activity, and weight. Data from 50 participants (mean age: 53.0, SD 12.6 y) were analyzed. Group-based multitrajectory modeling was performed to identify subgroups with distinct trajectories of SM adherence across the 3 SM targets. Differences between subgroups were examined for changes in clinical outcomes (ie, body weight, hemoglobin A1c) and SDT constructs (ie, eating-related autonomous motivation and perceived competence for diet) over 6 months using linear mixed models. RESULTS Two distinct SM trajectory subgroups emerged: the Lower SM group (21/50, 42%), characterized by all-around low and rapidly declining SM, and the Higher SM group (29/50, 58%), characterized by moderate and declining diet and weight SM with high activity SM. Since week 2, participants in the Lower SM group exhibited significantly lower levels of diet (P=.003), activity (P=.002), and weight SM (P=.02) compared with the Higher SM group. In terms of clinical outcomes, the Higher SM group achieved a significant reduction in body weight (estimate: -6.06, SD 0.87 kg; P<.001) and hemoglobin A1c (estimate: -0.38, SD 0.11%; P=.02), whereas the Lower SM group exhibited no improvements. For SDT constructs, both groups maintained high levels of autonomous motivation for over 6 months. However, the Lower SM group experienced a significant decline in perceived competence (P=.005) compared with the Higher SM group, which maintained a high level of perceived competence throughout the intervention (P=.09). CONCLUSIONS The presence of the Lower SM group highlights the value of using longitudinal SM adherence trajectories as an intervention response indicator. Future adaptive trials should identify nonresponders within the initial 2 weeks based on their SM adherence and integrate intervention strategies to enhance perceived competence in diet to benefit nonresponders. TRIAL REGISTRATION ClinicalTrials.gov NCT05071287; https://clinicaltrials.gov/study/NCT05071287. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1016/j.cct.2022.106845.
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Affiliation(s)
- Shiyu Li
- Department of Kinesiology, The Pennsylvania State University, University Park, PA, United States
| | - Yan Du
- School of Nursing, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Hongyu Miao
- College of Nursing, Florida State University, Tallahassee, FL, United States
| | - Kumar Sharma
- Center for Precision Medicine, Long School of Medicine, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Chengdong Li
- College of Nursing, Florida State University, Tallahassee, FL, United States
| | - Zenong Yin
- Department of Public Health, The University of Texas at San Antonio, San Antonio, TX, United States
| | - Bradley Brimhall
- Department of Pathology and Laboratory Medicine, Long School of Medicine, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Jing Wang
- College of Nursing, Florida State University, Tallahassee, FL, United States
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Li S, Du Y, Meireles C, Song D, Sharma K, Yin Z, Brimhall B, Wang J. Decoding Heterogeneity in Data-Driven Self-Monitoring Adherence Trajectories in Digital Lifestyle Interventions for Weight Loss: A Qualitative Study. RESEARCH SQUARE 2024:rs.3.rs-3854650. [PMID: 38313251 PMCID: PMC10836100 DOI: 10.21203/rs.3.rs-3854650/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Background Data-driven trajectory modeling is a promising approach for identifying meaningful participant subgroups with various self-monitoring (SM) responses in digital lifestyle interventions. However, there is limited research investigating factors that underlie different subgroups. This qualitative study aimed to investigate factors contributing to participant subgroups with distinct SM trajectory in a digital lifestyle intervention over 6 months. Methods Data were collected from a subset of participants (n = 20) in a 6-month digital lifestyle intervention. Participants were classified into Lower SM Group (n = 10) or a Higher SM (n = 10) subgroup based on their SM adherence trajectories over 6 months. Qualitative data were obtained from semi-structured interviews conducted at 3 months. Data were thematically analyzed using a constant comparative approach. Results Participants were middle-aged (52.9 ± 10.2 years), mostly female (65%), and of Hispanic ethnicity (55%). Four major themes with emerged from the thematic analysis: Acceptance towards SM Technologies, Perceived SM Benefits, Perceived SM Barriers, and Responses When Facing SM Barriers. Participants across both subgroups perceived SM as positive feedback, aiding in diet and physical activity behavior changes. Both groups cited individual and technical barriers to SM, including forgetfulness, the burdensome SM process, and inaccuracy. The Higher SM Group displayed positive problem-solving skills that helped them overcome the SM barriers. In contrast, some in the Lower SM Group felt discouraged from SM. Both subgroups found diet SM particularly challenging, especially due to technical issues such as the inaccurate food database, the time-consuming food entry process in the Fitbit app. Conclusions This study complements findings from our previous quantitative research, which used data-drive trajectory modeling approach to identify distinct participant subgroups in a digital lifestyle based on individuals' 6-month SM adherence trajectories. Our results highlight the potential of enhancing action planning problem solving skills to improve SM adherence in the Lower SM Group. Our findings also emphasize the necessity of addressing the technical issues associated with current diet SM approaches. Overall, findings from our study may inform the development of practical SM improvement strategies in future digital lifestyle interventions. Trial registration The study was pre-registered at ClinicalTrials.gov (NCT05071287) on April 30, 2022.
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Affiliation(s)
- Shiyu Li
- Department of Kinesiology, Pennsylvania State University
| | - Yan Du
- School of Nursing, UT Health San Antonio
| | | | - Dan Song
- College of Nursing, Florida State University
| | | | - Zenong Yin
- Department of Public Health, The University of Texas at San Antonio
| | | | - Jing Wang
- College of Nursing, Florida State University
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