Metabolic SyndromeCGMNCT04920058
Optimal Metabolic Health Through Continuous Glucose Monitoring
A study testing Continuous Glucose Monitor and <Active Comparator?> in people with Metabolic Syndrome, run by University of South Florida at 1 site in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 69 yearsHealthy volunteers can take part
- Conditions the study is for
- Metabolic Syndrome
- What is being tested?
- Continuous Glucose Monitor and <Active Comparator?>
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 66 participants
- Where
- 1 siteUnited States
- Study dates
- 10 May 2021to 18 Apr 2022
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 69 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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 %
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
United States1 site
Florida Medical Clinic
Wesley Chapel, Florida, 33544
About this study
Official title: Improving Cognitive-Behavioral and Cardio-Metabolic Health Through Continuous Glucose Monitoring (CGM)
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.
Read the detailed description
Achieving optimal metabolic health and glycemic control is a common goal among not only diabetics, but also for healthy individuals, athletes, elite military operators and for infectious disease prevention and resilience. No isolated biomarker is currently ubiquitously accepted as a marker of overall metabolic health and most rely on isolated snapshot (single time point) analyses and not a continuous closed-loop biomarker data assessment. Glycosylated hemoglobin (A1c) provides limited characterization of glycemic variability, which contributes to the progression of glycemic dysregulation. For example, emerging evidence links the amplitude and duration of glycemic variability as an independent risk factor linked to cardiovascular disease (CVD) (Di Flaviani 2011, Monnier 2006). Hyperglycemia-induced endothelial dysfunction and oxidative stress are greater with larger glycemic variability (Monnier 2006, Buscemi 2010). Glycemic variability is more deleterious for the cardiovascular system than sustained hyperglycemia (Nalysnyk 2010). Few technologies allow for continuous biomarker monitoring over time, and under a range of conditions like daily activities, swimming, exercise, sleep, etc. Multiple lines of evidence strongly suggest the predictive impact and value of monitoring glycemic variability on acute and chronic health of diabetes populations and non-diabetes populations (Rodriguez-Segade 2018, Zeevi 2015). Thus, there has been emerging interest in therapeutic approaches that seek to reduce glycemic variability. This potential for early detection of glycemic dysregulation is likely to be the single most beneficial effect of using CGM as an informational device, especially in the context of other biomarkers measures periodically. It is likely that people will make lifestyle modifications if they are aware of an impending health problem, detected through real time GCM-tracked glycemic variability. Lifestyle modifications are proven to be the most effective intervention for restoring normal fasting glucose levels and preventing diabetes among dysglycemic subjects, reducing the conversion to diabetes by 58% over placebo, and by 39% over metformin in one large US study (Diabetes Prevention Program Research Group 2002). Long terms follow-ups on other international studies have shown equally significant results at 4 years (Tuomilehto 2001) and 14 years (Li 2008) after the controlled lifestyle interventions ended, including reductions in diabetes incidence of 58%, and 43% respectively.
It is known that metabolic health is on a spectrum and long-term studies in diabetic populations have demonstrated that reducing glycemic variability is more important than lowering baseline hyperglycemia in terms of reducing cardiovascular complications (Hall 2018). Therefore, there exists a scientific rationale to study interventions that can optimize metabolic health in non-diabetics since the potential benefits of metabolic awareness extend beyond the diabetic population. Emerging technology that can provide tight feedback on lifestyle effects could be a valuable mechanism for non-diabetics seeking to improve education and reduce their lifetime risk of disease. Though such outcomes have not yet been demonstrated in long term studies, the existing research reveals promising results, including improved screening for metabolic risk (Rodriguez-Segade 2018), clear observability of effects of lifestyle intervention (Hall 2018, Brynes 2005, Freckmann 2007), and acceptance of a minimal-risk strategy for use as a preventative tool in a non-diabetic population (Liao 2018). The Diabetes Prevention Program Research Group called for a shift in response in order to reverse these trends, stating that: "methods of treating diabetes remain inadequate and that prevention is preferable (Diabetes Prevention Program Research Group 2002)." Though unproven as a preventative measure, monitoring of glycemic variability is - at worst - unlikely to exacerbate the problem. At best, however, if it becomes a widespread lifestyle tool, the benefits of improved individual metabolic awareness and educated action could have compounding effects at a larger societal scale.
Therefore, there exists a scientific rationale to study interventions that can optimize metabolic health with improved glycemic monitoring technologies (Danne 2017). It is becoming clear, that in addition to diabetic populations, normal, healthy populations can benefit from stable, controlled blood sugar levels, and that feedback mechanisms, including wearable technologies, can be employed. Thus, CGM could be a promising method of improving biomarkers of metabolic health for virtually anyone. In addition, optimal metabolic health is typically associated with improved behavioral health and cognitive resilience and decision making (Hadj-Abo 2020). Thus, optimizing and monitoring glycemic control may be useful for mental health and may be a valuable tool for military personnel and first responders under metabolic stress. Advances in software and hardware technologies have been developed to measure, analyze and predict glycemic variability and provides insight on how this dynamic biomarker correlates to metabolic fitness. Specifically, new advances in CGM technologies offer the potential to monitor, predict and change behavior through a closed-loop feedback system. By comparing CGM data with blood markers of metabolic health (eg.HbA1c , Insulin, etc.), and inflammation (e.g. hsCRP, cytokines) and along with assessments of emotion, cognition and behavior, a more robust interpretation and deconvolution of CGM data with experimental interventions may be possible.
- Conditions
- Metabolic Syndrome
- Being tested
- Continuous Glucose Monitor, <Active Comparator?>
- 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
- Medical subject headings
- Metabolic Syndrome, Inflammation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Continuous Glucose MonitorDevice
Also called Continuous Glucose Monitor Software, CGM, CGM Software
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
<Active Comparator?>Other
Also called <Wellness Program?>
<describe, Comprehensive Wellness Program incorporating a low carbohydrate diet (<50g/day) and associated education >
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: 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 Receives: Other: <Active Comparator?> |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 12 weeks
Secondary outcomes (16)
Changes in depression severity from baseline to 12 weeks as measured by Patient Health Questionnaire-9 (PHQ-9) assessment
Both arms will complete the PHQ-9 assessment at baseline and at the end of the 12 week study per protocol design. PHQ-9 score of depression severity ranges from 0-27 as follows: 0-4 none, 5-9 mild, 10-14 moderate, 15-19 moderately severe, 20-27 severe. Subjects will be considered stable if they remain within 2 points of their baseline range. This outcome will be presented as the mean PHQ-9 assessment score as well as the number of subjects that remained stable, increased, or decreased on the scale.
Measured: 12 weeks
Changes in anxiety from baseline to 12 weeks as measured by GAD-7 assessment
Both arms will complete the Generalised Anxiety Disorder Assessment (GAD-7) over the 12 week study per protocol design. GAD-7 total score ranges from 0 to 21. 0-4: minimal anxiety. 5-9: mild anxiety. 10-14: moderate anxiety. 15-21: severe anxiety. Subjects will be considered stable if they remain within 2 points of their baseline range. This outcome with be presented as the mean GAD-7 assessment score as well as the number of subjects that remained stable, increased, or decreased on the scale.
Measured: 12 weeks
Changes in daily stress from baseline to 12 weeks as measured by Short Stress State Questionnaire (SSSQ) assessment
Daily stress will be assessed by the SSSQ. It is a 1min questionnaire consisting of 24 simple questions regarding their stress level perception. It can be performed on an iPad. Conscious appraisals of stress, or stress states, are an important aspect of human performance. Therefore, we will use a short multidimensional self-report measure of stress state, the SSSQ (Helton, 2004) to evaluate the changes in stress level during the mission. The SSSQ measures task engagement, distress, and worry.
Measured: 12 weeks
Changes in circulating ghrelin from baseline to 12 weeks
Both arms will have blood drawn for analysis of circulating ghrelin over the 12 week study per protocol design. This outcome will be presented as the mean concentration of ghrelin (pg/mL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
Measured: 12 weeks
Changes in circulating glucagon from baseline to 12 weeks
Both arms will have blood drawn for analysis of circulating glucagon over the 12 week study per protocol design. This outcome will be presented as the mean concentration of glucagon (pg/mL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
Measured: 12 weeks
Changes in circulating leptin from baseline to 12 weeks
Both arms will have blood drawn for analysis of circulating leptin over the 12 week study per protocol design. This outcome will be presented as the mean concentration of leptin (pg/mL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
Measured: 12 weeks
Changes in circulating insulin from baseline to 12 weeks
Both arms will have blood drawn for analysis of circulating insulin over the 12 week study per protocol design. This outcome will be presented as the mean concentration of insulin (pg/mL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
Measured: 12 weeks
Changes in circulating GLP-1 from baseline to 12 weeks
Both arms will have blood drawn for analysis of circulating GLP-1 over the 12 week study per protocol design. This outcome will be presented as the mean concentration of GLP-1 (pg/mL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
Measured: 12 weeks
Changes in circulating hsCRP from baseline to 12 weeks
Both arms will have blood drawn for analysis of circulating hsCRP over the 12 week study per protocol design. This outcome will be presented as the mean concentration of hsCRP (mg/L) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
Measured: 12 weeks
Changes in circulating total cholesterol from baseline to 12 weeks
Both arms will have blood drawn for analysis of circulating total cholesterol over the 12 week study per protocol design. This outcome will be presented as the mean concentration of total cholesterol (mg/dL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
Measured: 12 weeks
Changes in circulating HDL from baseline to 12 weeks
Both arms will have blood drawn for analysis of circulating HDL over the 12 week study per protocol design. This outcome will be presented as the mean concentration of HDL (mg/dL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
Measured: 12 weeks
Changes in circulating LDL and ApoB from baseline to 12 weeks
Both arms will have blood drawn for analysis of circulating LDL and ApoB over the 12 week study per protocol design. This outcome will be presented as the mean concentration of LDL (mg/dL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
Measured: 12 weeks
Changes in circulating triglycerides from baseline to 12 weeks
Both arms will have blood drawn for analysis of circulating triglycerides over the 12 week study per protocol design. This outcome will be presented as the mean concentration of triglycerides (mg/dL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
Measured: 12 weeks
Changes in blood glucose from baseline to 12 weeks using POC finger stick glucometer.
Subjects in the treatment arm will use a point of care (POC) finger stick glucometer to test their blood glucose levels over the 12 week study per protocol design. Glucose in the range of 70-120mg/dL will be considered normal. This outcome will be presented as the mean glucose concentration, the percent of subjects that remained in the normal range, and the number of patients who remained stable, increased, or decreased from baseline over time.
Measured: 12 weeks
Changes in blood ketones (beta hydroxybutyrate) from baseline to 12 weeks using POC finger stick ketone meter.
Subjects in the treatment arm will use a POC finger stick ketone meter to test their blood ketone levels over the 12 week study per protocol design. Beta-hydroxybutyrate in the range of 0-5mM will be considered normal. This outcome will be presented as the mean beta-hydroxybutyrate concentration, the percent of subjects that remained in the normal range, and the number of patients who remained stable, increased, or decreased from baseline over time.
Measured: 12 weeks
Changes in hepatic steatosis from baseline to 12 weeks as measured by abdominal ultrasound (US).
Both arms will undergo an abdominal US pre- and post- the 12 week study for assessment of hepatic steatosis as a marker of fatty liver disease. Hepatic fat content will be estimated by assessment of radiographic findings and measurement of liver echogenicity scored by a qualified ultrasound technologist.. This outcome with be presented as none, mild, moderate, or severe for individual subjects as well as the number of subjects that remained stable, increased, or decreased in severity from pre- to post- study.
Measured: 12 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Jun 2021
- Study start10 May 2021
- Primary completion18 Apr 2022
- Study completion18 Apr 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (15)Papers the sponsor has linked to this study.
- Other trials for Metabolic SyndromeStudies of the same condition, open and under way.
- More trials run by University of South FloridaThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- 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.
- Masking
- Single blind
- Who is masked
- Investigator
- Enrolment
- 66 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of South Florida
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Dominic D'Agostino, PhD, Principal investigator, University of South Florida
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04920058
- Sponsor's protocol ID
- STUDY001801
- Acronym
- CGM
- Status last verified
- May 2022
- First submitted
- 9 Dec 2020
- Last updated
- 15 Nov 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Publications
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.
PubMed 21610126Monnier 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.
PubMed 16609090Buscemi 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.
PubMed 20653743Nalysnyk 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.
PubMed 20380649Rodriguez-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.
PubMed 29807103Zeevi 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.
PubMed 26590418Knowler 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.
PubMed 11832527Tuomilehto 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.
PubMed 11333990Li 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.
PubMed 18502303Hall 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.
PubMed 30040822Brynes 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.
PubMed 15788110Freckmann 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.
PubMed 19885137Liao 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.
PubMed 30355561Danne 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.
PubMed 29162583Hadj-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.
PubMed 31995586
Study at a glance
From the registry record, last updated by the sponsor on 15 Nov 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Improving Cognitive-Behavioral and Cardio-Metabolic Health Through Continuous Glucose Monitoring (CGM)
- ClinicalTrials.gov ID
- NCT04920058
- Acronym / sponsor ID
- CGM / STUDY001801
- Conditions
- Metabolic Syndrome
- Keywords
- continuous glucose monitor, CGM, Ketogenic diet, metabolic health, inflammation, low carbohydrate diet, Levels health, CGM software
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 66 participants (actual)
- Lead sponsor
- University of South FloridaUniversity, hospital or other
- Study start
- 10 May 2021 (actual)
- Primary completion
- 18 Apr 2022 (actual)
- Study completion
- 18 Apr 2022 (actual)
- First posted
- 9 Jun 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Achieving optimal metabolic health and glycemic control is a common goal among not only diabetics, but also for healthy individuals, athletes, elite military operators and for infectious disease prevention and resilience. No isolated biomarker is currently ubiquitously accepted as a marker of overall metabolic health and most rely on isolated snapshot (single time point) analyses and not a continuous closed-loop biomarker data assessment. Glycosylated hemoglobin (A1c) provides limited characterization of glycemic variability, which contributes to the progression of glycemic dysregulation. For example, emerging evidence links the amplitude and duration of glycemic variability as an independent risk factor linked to cardiovascular disease (CVD) (Di Flaviani 2011, Monnier 2006). Hyperglycemia-induced endothelial dysfunction and oxidative stress are greater with larger glycemic variability (Monnier 2006, Buscemi 2010). Glycemic variability is more deleterious for the cardiovascular system than sustained hyperglycemia (Nalysnyk 2010). Few technologies allow for continuous biomarker monitoring over time, and under a range of conditions like daily activities, swimming, exercise, sleep, etc. Multiple lines of evidence strongly suggest the predictive impact and value of monitoring glycemic variability on acute and chronic health of diabetes populations and non-diabetes populations (Rodriguez-Segade 2018, Zeevi 2015). Thus, there has been emerging interest in therapeutic approaches that seek to reduce glycemic variability. This potential for early detection of glycemic dysregulation is likely to be the single most beneficial effect of using CGM as an informational device, especially in the context of other biomarkers measures periodically. It is likely that people will make lifestyle modifications if they are aware of an impending health problem, detected through real time GCM-tracked glycemic variability. Lifestyle modifications are proven to be the most effective intervention for restoring normal fasting glucose levels and preventing diabetes among dysglycemic subjects, reducing the conversion to diabetes by 58% over placebo, and by 39% over metformin in one large US study (Diabetes Prevention Program Research Group 2002). Long terms follow-ups on other international studies have shown equally significant results at 4 years (Tuomilehto 2001) and 14 years (Li 2008) after the controlled lifestyle interventions ended, including reductions in diabetes incidence of 58%, and 43% respectively.
It is known that metabolic health is on a spectrum and long-term studies in diabetic populations have demonstrated that reducing glycemic variability is more important than lowering baseline hyperglycemia in terms of reducing cardiovascular complications (Hall 2018). Therefore, there exists a scientific rationale to study interventions that can optimize metabolic health in non-diabetics since the potential benefits of metabolic awareness extend beyond the diabetic population. Emerging technology that can provide tight feedback on lifestyle effects could be a valuable mechanism for non-diabetics seeking to improve education and reduce their lifetime risk of disease. Though such outcomes have not yet been demonstrated in long term studies, the existing research reveals promising results, including improved screening for metabolic risk (Rodriguez-Segade 2018), clear observability of effects of lifestyle intervention (Hall 2018, Brynes 2005, Freckmann 2007), and acceptance of a minimal-risk strategy for use as a preventative tool in a non-diabetic population (Liao 2018). The Diabetes Prevention Program Research Group called for a shift in response in order to reverse these trends, stating that: "methods of treating diabetes remain inadequate and that prevention is preferable (Diabetes Prevention Program Research Group 2002)." Though unproven as a preventative measure, monitoring of glycemic variability is - at worst - unlikely to exacerbate the problem. At best, however, if it becomes a widespread lifestyle tool, the benefits of improved individual metabolic awareness and educated action could have compounding effects at a larger societal scale.
Therefore, there exists a scientific rationale to study interventions that can optimize metabolic health with improved glycemic monitoring technologies (Danne 2017). It is becoming clear, that in addition to diabetic populations, normal, healthy populations can benefit from stable, controlled blood sugar levels, and that feedback mechanisms, including wearable technologies, can be employed. Thus, CGM could be a promising method of improving biomarkers of metabolic health for virtually anyone. In addition, optimal metabolic health is typically associated with improved behavioral health and cognitive resilience and decision making (Hadj-Abo 2020). Thus, optimizing and monitoring glycemic control may be useful for mental health and may be a valuable tool for military personnel and first responders under metabolic stress. Advances in software and hardware technologies have been developed to measure, analyze and predict glycemic variability and provides insight on how this dynamic biomarker correlates to metabolic fitness. Specifically, new advances in CGM technologies offer the potential to monitor, predict and change behavior through a closed-loop feedback system. By comparing CGM data with blood markers of metabolic health (eg.HbA1c , Insulin, etc.), and inflammation (e.g. hsCRP, cytokines) and along with assessments of emotion, cognition and behavior, a more robust interpretation and deconvolution of CGM data with experimental interventions may be possible.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (investigator)
- Number of arms
- 2
- Model description
- 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.
| Arm | Type | Receives |
|---|---|---|
| Wellness Program combined with Continuous Glucose Monitoring (CGM) | Experimental | Device: Continuous Glucose Monitor, Other: <Active Comparator?>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 |
| Wellness Program | Active comparator | Other: <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 |
Who may qualify
- Ages
- 18 to 69 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- 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
Key exclusion criteria (6 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Continuous Glucose MonitorAlso: Continuous Glucose Monitor Software, CGM, CGM SoftwareContinuous 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 | Device | Wellness Program combined with Continuous Glucose Monitoring (CGM) |
| <Active Comparator?>Also: <Wellness Program?><describe, Comprehensive Wellness Program incorporating a low carbohydrate diet (<50g/day) and associated education > | Other | Wellness Program, Wellness Program combined with Continuous Glucose Monitoring (CGM) |
Primary outcome
Glucose stability from baseline to 12 weeks as measured by Continuous Glucose Monitoring (CGM)
Time frame: 12 weeks
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.
Glucose stability from baseline to 12 weeks as measured by hemoglobin A1c (HbA1c)
Time frame: 12 weeks
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.
16 secondary outcomes
Changes in depression severity from baseline to 12 weeks as measured by Patient Health Questionnaire-9 (PHQ-9) assessment
Time frame: 12 weeks
Changes in anxiety from baseline to 12 weeks as measured by GAD-7 assessment
Time frame: 12 weeks
Changes in daily stress from baseline to 12 weeks as measured by Short Stress State Questionnaire (SSSQ) assessment
Time frame: 12 weeks
Changes in circulating ghrelin from baseline to 12 weeks
Time frame: 12 weeks
Changes in circulating glucagon from baseline to 12 weeks
Time frame: 12 weeks
Changes in circulating leptin from baseline to 12 weeks
Time frame: 12 weeks
Changes in circulating insulin from baseline to 12 weeks
Time frame: 12 weeks
Changes in circulating GLP-1 from baseline to 12 weeks
Time frame: 12 weeks
Changes in circulating hsCRP from baseline to 12 weeks
Time frame: 12 weeks
Changes in circulating total cholesterol from baseline to 12 weeks
Time frame: 12 weeks
Changes in circulating HDL from baseline to 12 weeks
Time frame: 12 weeks
Changes in circulating LDL and ApoB from baseline to 12 weeks
Time frame: 12 weeks
Changes in circulating triglycerides from baseline to 12 weeks
Time frame: 12 weeks
Changes in blood glucose from baseline to 12 weeks using POC finger stick glucometer.
Time frame: 12 weeks
Changes in blood ketones (beta hydroxybutyrate) from baseline to 12 weeks using POC finger stick ketone meter.
Time frame: 12 weeks
Changes in hepatic steatosis from baseline to 12 weeks as measured by abdominal ultrasound (US).
Time frame: 12 weeks
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Nov 2022.
- Background: PubMed 21610126Di 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.
- Background: PubMed 16609090Monnier 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.
- Background: PubMed 20653743Buscemi 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.
- Background: PubMed 20380649Nalysnyk 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.
- Background: PubMed 29807103Rodriguez-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.
- Background: PubMed 26590418Zeevi 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.
- Background: PubMed 11832527Knowler 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.
- Background: PubMed 11333990Tuomilehto 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.
- Background: PubMed 18502303Li 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.
- Background: PubMed 30040822Hall 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.
- Background: PubMed 15788110Brynes 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.
- Background: PubMed 19885137Freckmann 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Dominic D'Agostino, PhD principal investigator
University of South Florida
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Continuous Glucose Monitor and <Active Comparator?> in people with Metabolic Syndrome, run by University of South Florida at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 15 Nov 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Glucose stability from baseline to 12 weeks as measured by Continuous Glucose Monitoring (CGM) (time frame: 12 weeks). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04920058, which also lists 15 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Nov 2022.