Optimal Metabolic Health Through Continuous Glucose Monitoring

NCT04920058 | Completed FDA Device

• 1 other identifier: STUDY001801
• Study Type: interventional
• Target: 66
• Locations: 1 country
• Sites: 1

Brief Summary

The primary focus of this study is to evaluate the role of Continuous Glucose Monitoring (CGM) with Levels Health software as a tool to provide feedback and accountability necessary to create sustainable behavioral changes in nutrition associated with improved metabolic health and resilience against chronic and infectious diseases.

Conditions

Metabolic Syndrome

Keywords

continuous glucose monitor; CGM; Ketogenic diet; metabolic health; inflammation; low carbohydrate diet; Levels health; CGM software; Behavioral testing; PHQ9; GAD-7 assessment; SSSQ assessment; ZRT Laboratory; cytokine assay; HbA1c; hsCRP; beta-hydroxybutrate; ketone bodies

Outcome Measures

Primary Outcomes (2)

• Glucose stability from baseline to 12 weeks as measured by Continuous Glucose Monitoring (CGM)

  The intervention arm will have Continuous Glucose Monitoring (CGM) data collected over 12 weeks per protocol design. Subjects will be considered stable with no more than a 10% increase in average CGM from baseline. This outcome with be presented as mean glucose and Hba1c concentration as well as the number of subjects that improved average CGM from baseline.

  12 weeks

• Glucose stability from baseline to 12 weeks as measured by hemoglobin A1c (HbA1c)

  Both arms will have HbA1c collected over 12 weeks per protocol design. HbA1c is considered pre-diabetes when between 5.7-6.4% and abnormally high when above 6.4%. Subjects will be considered stable with no more than a 10% increase in HbA1c from baseline. This outcome with be presented as mean Hba1c concentration as well as the number of subjects that improved average HbA1c from baseline.

  12 weeks

Secondary Outcomes (16)

• Changes in depression severity from baseline to 12 weeks as measured by Patient Health Questionnaire-9 (PHQ-9) assessment

  12 weeks

• Changes in anxiety from baseline to 12 weeks as measured by GAD-7 assessment

  12 weeks

• Changes in daily stress from baseline to 12 weeks as measured by Short Stress State Questionnaire (SSSQ) assessment

  12 weeks

• Changes in circulating ghrelin from baseline to 12 weeks

  12 weeks

• Changes in circulating glucagon from baseline to 12 weeks

  12 weeks

Study Arms (2)

Wellness Program combined with Continuous Glucose Monitoring (CGM)

EXPERIMENTAL

Continuous Glucose Monitoring (CGM) sensor combined with Levels CGM software that provides real-time visualization, analysis and feedback will be added to a Wellness Program incorporating a low carbohydrate diet (\<50 g carbohydrate). Subjects in the group will be manually randomized and listed in a sealed envelope by someone who is not part of the study team

Device: Continuous Glucose Monitor; Other: <Active Comparator?>

Wellness Program

ACTIVE COMPARATOR

Wellness Program incorporating a low carbohydrate diet (\<50 g carbohydrate). Subjects in the group will be manually randomized and listed in a sealed envelope by someone who is not part of the study team

Other: <Active Comparator?>

Interventions

Continuous Glucose Monitor DEVICE

Continuous glucose monitor - a device that monitors blood glucose levels in a continuous closed-loop manner. This can also refer to the process of continuous glucose monitoring

Also known as: Continuous Glucose Monitor Software, CGM, CGM Software

Wellness Program combined with Continuous Glucose Monitoring (CGM)

<Active Comparator?> OTHER

\<describe, Comprehensive Wellness Program incorporating a low carbohydrate diet (\<50g/day) and associated education \>

Also known as: <Wellness Program?>

Wellness Program; Wellness Program combined with Continuous Glucose Monitoring (CGM)

Eligibility Criteria

• Age: 18 Years - 69 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Ages 18-69 years of age
• Desire to improve metabolic health through nutritional, fitness, cognitive, and behavioral therapies.
• Voluntarily participate in either a live or virtual 12-week, multidisciplinary wellness program created and led by Allison Hull, DO.
• Body Mass Index (BMI) \> 20 kg/m2
• Fasting Blood Glucose (FBG) of 85-125 mg/dl
• HbA1c of 5.0-6.4 %

You may not qualify if:

• Type 1 or 2 Diabetes.
• Chronic Kidney Disease
• End Stage Liver Disease
• Use of any weight loss medications currently or in the past 3 months.
• Disordered Eating - anorexia or bulimia nervosa.
• Pregnant or Breastfeeding females.

Sponsors & Collaborators

• University of South Florida: lead

Study Sites (1)

Florida Medical Clinic

Wesley Chapel, Florida, 33544, United States

Location

Related Publications (15)

• Di Flaviani A, Picconi F, Di Stefano P, Giordani I, Malandrucco I, Maggio P, Palazzo P, Sgreccia F, Peraldo C, Farina F, Frajese G, Frontoni S. Impact of glycemic and blood pressure variability on surrogate measures of cardiovascular outcomes in type 2 diabetic patients. Diabetes Care. 2011 Jul;34(7):1605-9. doi: 10.2337/dc11-0034. Epub 2011 May 24.

  PMID: 21610126 BACKGROUND

• Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, Colette C. Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA. 2006 Apr 12;295(14):1681-7. doi: 10.1001/jama.295.14.1681.

  PMID: 16609090 BACKGROUND

• Buscemi S, Re A, Batsis JA, Arnone M, Mattina A, Cerasola G, Verga S. Glycaemic variability using continuous glucose monitoring and endothelial function in the metabolic syndrome and in Type 2 diabetes. Diabet Med. 2010 Aug;27(8):872-8. doi: 10.1111/j.1464-5491.2010.03059.x.

  PMID: 20653743 BACKGROUND

• Nalysnyk L, Hernandez-Medina M, Krishnarajah G. Glycaemic variability and complications in patients with diabetes mellitus: evidence from a systematic review of the literature. Diabetes Obes Metab. 2010 Apr;12(4):288-98. doi: 10.1111/j.1463-1326.2009.01160.x.

  PMID: 20380649 BACKGROUND

• Rodriguez-Segade S, Rodriguez J, Camina F, Fernandez-Arean M, Garcia-Ciudad V, Pazos-Couselo M, Garcia-Lopez JM, Alonso-Sampedro M, Gonzalez-Quintela A, Gude F. Continuous glucose monitoring is more sensitive than HbA1c and fasting glucose in detecting dysglycaemia in a Spanish population without diabetes. Diabetes Res Clin Pract. 2018 Aug;142:100-109. doi: 10.1016/j.diabres.2018.05.026. Epub 2018 May 26.

  PMID: 29807103 BACKGROUND

• Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalova L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015 Nov 19;163(5):1079-1094. doi: 10.1016/j.cell.2015.11.001.

  PMID: 26590418 BACKGROUND

• Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, Nathan DM; Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002 Feb 7;346(6):393-403. doi: 10.1056/NEJMoa012512.

  PMID: 11832527 BACKGROUND

• Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H, Ilanne-Parikka P, Keinanen-Kiukaanniemi S, Laakso M, Louheranta A, Rastas M, Salminen V, Uusitupa M; Finnish Diabetes Prevention Study Group. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001 May 3;344(18):1343-50. doi: 10.1056/NEJM200105033441801.

  PMID: 11333990 BACKGROUND

• Li G, Zhang P, Wang J, Gregg EW, Yang W, Gong Q, Li H, Li H, Jiang Y, An Y, Shuai Y, Zhang B, Zhang J, Thompson TJ, Gerzoff RB, Roglic G, Hu Y, Bennett PH. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet. 2008 May 24;371(9626):1783-9. doi: 10.1016/S0140-6736(08)60766-7.

  PMID: 18502303 BACKGROUND

• Hall H, Perelman D, Breschi A, Limcaoco P, Kellogg R, McLaughlin T, Snyder M. Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol. 2018 Jul 24;16(7):e2005143. doi: 10.1371/journal.pbio.2005143. eCollection 2018 Jul.

  PMID: 30040822 BACKGROUND

• Brynes AE, Adamson J, Dornhorst A, Frost GS. The beneficial effect of a diet with low glycaemic index on 24 h glucose profiles in healthy young people as assessed by continuous glucose monitoring. Br J Nutr. 2005 Feb;93(2):179-82. doi: 10.1079/bjn20041318.

  PMID: 15788110 BACKGROUND

• Freckmann G, Hagenlocher S, Baumstark A, Jendrike N, Gillen RC, Rossner K, Haug C. Continuous glucose profiles in healthy subjects under everyday life conditions and after different meals. J Diabetes Sci Technol. 2007 Sep;1(5):695-703. doi: 10.1177/193229680700100513.

  PMID: 19885137 BACKGROUND

• Liao Y, Schembre S. Acceptability of Continuous Glucose Monitoring in Free-Living Healthy Individuals: Implications for the Use of Wearable Biosensors in Diet and Physical Activity Research. JMIR Mhealth Uhealth. 2018 Oct 24;6(10):e11181. doi: 10.2196/11181.

  PMID: 30355561 BACKGROUND

• Danne T, Nimri R, Battelino T, Bergenstal RM, Close KL, DeVries JH, Garg S, Heinemann L, Hirsch I, Amiel SA, Beck R, Bosi E, Buckingham B, Cobelli C, Dassau E, Doyle FJ 3rd, Heller S, Hovorka R, Jia W, Jones T, Kordonouri O, Kovatchev B, Kowalski A, Laffel L, Maahs D, Murphy HR, Norgaard K, Parkin CG, Renard E, Saboo B, Scharf M, Tamborlane WV, Weinzimer SA, Phillip M. International Consensus on Use of Continuous Glucose Monitoring. Diabetes Care. 2017 Dec;40(12):1631-1640. doi: 10.2337/dc17-1600.

  PMID: 29162583 BACKGROUND

• Hadj-Abo A, Enge S, Rose J, Kunte H, Fleischhauer M. Individual differences in impulsivity and need for cognition as potential risk or resilience factors of diabetes self-management and glycemic control. PLoS One. 2020 Jan 29;15(1):e0227995. doi: 10.1371/journal.pone.0227995. eCollection 2020.

  PMID: 31995586 BACKGROUND

MeSH Terms

Conditions

Metabolic Syndrome; Inflammation

Interventions

Health Promotion

Condition Hierarchy (Ancestors)

Insulin Resistance; Hyperinsulinism; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Health Education; Preventive Health Services; Health Services; Health Care Facilities Workforce and Services

Study Officials

• Dominic D'Agostino, PhD

  University of South Florida

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: INVESTIGATOR
• Masking Details: Participants CGM record and biological samples will be de-identified to investigator.
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: To determine if a data driven behavioral tool incorporating Levels CGM software provides superior feedback, accountability, control, and reward mechanisms necessary to create positive, sustainable dietary behavioral change.

Study Record Dates

• First Submitted: December 9, 2020
• First Posted: June 9, 2021
• Study Start: May 10, 2021
• Primary Completion: April 18, 2022
• Study Completion: April 18, 2022
• Last Updated: November 15, 2022

Record last verified: 2022-05

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)