The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index; 13 of the 17 sources its claims cite were checked for support.
Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study
Open accessAbstract
Context: Use of continuous glucose monitoring (CGM) is increasing for insulin-requiring patients with diabetes. Although data on glycemic profiles of healthy, nondiabetic individuals exist for older sensors, assessment of glycemic metrics with new-generation CGM devices is lacking. Objective: To establish reference sensor glucose ranges in healthy, nondiabetic individuals across different age groups using a current generation CGM sensor. Design: Multicenter, prospective study. Setting: Twelve centers within the T1D Exchange Clinic Network. Patients or Participants: Nonpregnant, healthy, nondiabetic children and adults (age ≥6 years) with nonobese body mass index. Intervention: Each participant wore a blinded Dexcom G6 CGM, with once-daily calibration, for up to 10 days. Main Outcome Measures: CGM metrics of mean glucose, hyperglycemia, hypoglycemia, and glycemic variability. Results: A total of 153 participants (age 7 to 80 years) were included in the analyses. Mean average glucose was 98 to 99 mg/dL (5.4 to 5.5 mmol/L) for all age groups except those over 60 years, in whom mean average glucose was 104 mg/dL (5.8 mmol/L). The median time between 70 to 140 mg/dL (3.9 to 7.8 mmol/L) was 96% (interquartile range, 93 to 98). Mean within-individual coefficient of variation was 17 ± 3%. Median time spent with glucose levels >140 mg/dL was 2.1% (30 min/d), and median time spent with glucose levels <70 mg/dL (3.9 mmol/L) was 1.1% (15 min/d). Conclusion: By assessing across age groups in a healthy, nondiabetic population, normative sensor glucose data have been derived and will be useful as a benchmark for future research studies.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
In 153 healthy nondiabetic children and adults aged 7 to 80 years, blinded Dexcom G6 CGM showed a mean 24-hour glucose of 99 ± 7 mg/dL and median 96% time in 70 to 140 mg/dL. Adults ≥60 years had higher mean glucose and less time in range than younger participants.
Key findings
- Overall mean 24-hour CGM glucose was 99 ± 7 mg/dL, mean within-individual coefficient of variation was 17 ± 3%, and median time between 70 and 140 mg/dL was 96% (IQR 93–98).
- Mean average glucose was 98–99 mg/dL for all age groups except those ≥60 years, in whom it was 104 mg/dL (P < 0.001); median time in 70–140 mg/dL was lowest at 93% in adults ≥60 years (P < 0.001).
- Median time >140 mg/dL was 2.1% (30 min/d) and <70 mg/dL was 1.1% (15 min/d); values >180 mg/dL and <54 mg/dL were uncommon, but 28% of participants had at least one hypoglycemic event overall and 14% had an overnight event.
- The correlation between mean glucose and HbA1c was weak (r = 0.27; 95% CI, 0.12–0.41).
Methods
- Multicenter, prospective study at 12 T1D Exchange Clinic Network centers; participants were healthy, nondiabetic, nonpregnant children and adults age ≥6 years with nonobese BMI, HbA1c <5.7%, and no islet autoantibodies. Of 201 screened, 153 were analyzed (age 7–80 years; mean 31 years).
- Participants wore a blinded Dexcom G6 CGM for up to 10 days with once-daily calibration; ≥72 total hours of CGM data with ≥24 overnight hours were required. Mean CGM use was 192 hours (8 days), and MARD versus blood glucose meter was 6.0% (n = 1555 paired measurements).
- Glucose outcomes were summarized overall and by age group and by daytime/nighttime; analyses included t tests for ≥60 vs <60 years, Pearson correlation for mean glucose and HbA1c, and sensitivity analyses removing implausible values.
Limitations
- The paper states that 93% of participants were white, so it could not evaluate whether results were consistent across races; possible sensor pressure during sleep may have caused inaccurate CGM readings, and the study could not determine how many nocturnal hypoglycemic events were due to pressure on the sensor.
- The paper states that after removing implausible data, mean glucose, time in 70–140 mg/dL, and CV were unchanged, but the percentage of participants with a hypoglycemic event was much lower; results may not be generalizable to capillary or laboratory-measured glucose profiles.
- The paper notes the weak HbA1c–glucose correlation may relate to HbA1c being obtained before CGM data collection and to having at most 10 days of CGM data. Evident from the text: participants were primarily recruited from family, friends, and neighbors of patients seen in diabetes clinics, which may limit generalizability to the broader nondiabetic population.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Verification
Claims the paper makes
13 of 17 cited sources checked · 7 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The overall results of our study are in agreement with previous studies (3)(4)(5)(6)(7)17) despite the different generations of CGM systems used in most of the studies.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Partly supportedcites [3] Borg, 2010, Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study, Diabetologia sourceExperimental — accuracy not yet measured
The Discussion says previous smaller CGM studies found similar glucose patterns, supporting the claim of overall agreement. The cited paper does not compare generations of CGM systems, so it does not support that qualification. (LLM, conf=0.83)
- Supportedcites [4] Freckmann, 2007, Continuous glucose profiles in healthy subjects under everyday life conditions and after different meals, J Diabetes Sci Technol source
The Discussion (page unavailable) reports glucose profiles in healthy subjects and says earlier studies found similar fasting and postprandial glucose levels. It also compares its continuous-monitoring results with results obtained using other sensor types. The agreement with the citing authors’ own study is their conclusion, not a finding attributed to Freckmann et al. (LLM, conf=0.86)
- Supportedcites [5] Fox, 2010, Variation of interstitial glucose measurements assessed by continuous glucose monitors in healthy, nondiabetic individuals, Diabetes Care source
The Methods identify this as a study of CGM measurements in healthy, nondiabetic people, so it is a previous study of the kind cited in the sentence. Its Conclusions also report that its mean sensor data were similar to those of an earlier CGM study. Whether the citing authors’ own results agree across device generations is their comparison, not a finding attributed to this source. (LLM, conf=0.86)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, the correlation between HbA1c and glucose is lower than what others have found (15).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Supportedcites [15] Nathan, 2008, Translating the A1C assay into estimated average glucose values, Diabetes Care source
The citation supplies the comparison study, not the citing authors’ own correlation. The source’s Results report a strong relationship between A1C and average glucose (R²=0.84), and its Conclusions describe that relationship as strong. Those passages support its use as a comparator for the citing authors’ lower correlation. (LLM, conf=0.87)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, median time spent with glucose levels .140mg/dL was 2.1% (30 min/d)”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and ,70 mg/dL was 1.1% (15 min/d).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and 99% (IQR, 95 to 100), respectively”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the mean of the within-individual coefficient of variation (CV), a metric of glucose variability, was 17 6 3% (Table 2).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but only 1% of participants spent $2% of time ,54 mg/dL.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Yet, 14% of participants had a hypoglycemic event overnight (Table 3).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The percentage of time between 70 to 120 mg/dL was quite different for daytime vs nighttime in the 601 age group (78% vs 92%, respectively).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the correlation was weak (r 5 0.27; 95% confidence interval, 0.12 to 0.41) between mean glucose concentration and HbA1c levels”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
- Probably not a substantial claim. 21
- It may state two separate claims as one. 16
- The quote only partly states this claim. 3
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Our findings support the recent consensus conference recommendation (12, 13) that sensor glucose values ,54 mg/dL represent a more meaningful cut-off than 70 mg/dL to define clinically important hypoglycemia because CGM glucose levels below 54 mg/dL were uncommon, whereas most participants had some glucose levels of 55 to 69 mg/dL.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Supportedcites [12] Danne, 2017, International consensus on use of continuous glucose monitoring, Diabetes Care source
The hypoglycemia recommendations classify sensor glucose below 54 mg/dL as clinically significant and 54–70 mg/dL as an alert range. They also recommend giving values below 54 mg/dL more weight in CGM outcome reports. The citing paper’s observation about how often participants entered each range is its own finding, not part of the recommendation attributed to this source. (LLM, conf=0.94)
- Partly supportedcites [13] Maahs, 2016, Outcome measures for artificial pancreas clinical trials: a consensus report, Diabetes Care sourceExperimental — accuracy not yet measured
In Additional Recommendations and Limitations (page unavailable), the consensus report suggests reporting CGM excursions below either 70 or 55 mg/dL as possible measures of hypoglycemia. It does not recommend a cutoff below 54 mg/dL or say that cutoff is more clinically meaningful than 70 mg/dL. (LLM, conf=0.85)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Most studies of CGM profiles in healthy, nondiabetic individuals have had small sample sizes or used early-generation CGM systems, which were less accurate than current devices (3)(4)(5)(6)(7).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
What the sources say
- Partly supportedcites [3] Borg, 2010, Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study, Diabetologia sourceExperimental — accuracy not yet measured
The Discussion describes earlier CGM studies of 32 and 15 people, supporting the small-sample part of the claim; Methods reports that this study used CGM in 80 nondiabetic participants. Neither section says the systems were early-generation or compares their accuracy with current devices. (LLM, conf=0.84)
- Partly supportedcites [4] Freckmann, 2007, Continuous glucose profiles in healthy subjects under everyday life conditions and after different meals, J Diabetes Sci Technol sourceExperimental — accuracy not yet measured
The Methods report CGM profiles from 24 healthy volunteers using SCGM1 devices, supporting this paper as an example of a small study using an older system. The paper reports the SCGM1’s measurement error but does not compare its accuracy with current devices or establish that most such studies share these limitations. (LLM, conf=0.84)
- Partly supportedcites [5] Fox, 2010, Variation of interstitial glucose measurements assessed by continuous glucose monitors in healthy, nondiabetic individuals, Diabetes Care sourceExperimental — accuracy not yet measured
The Abstract’s Research Design and Methods describes a CGM study of 74 healthy participants. Research Design and Methods identifies the devices used and reports missing or implausible readings from one sensor, noting that a commercially available version had newer software. The paper does not establish that most such studies had small samples or that earlier devices were less accurate than current devices. (LLM, conf=0.83)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“As the assessment of outcomes of treatment of T1D and type 2 diabetes in clinical practice and research trials moves beyond the narrow focus of HbA1c levels to include the results of sensor glucose profiles (14), there is an increased need for a repository of”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Supportedcites [14] Beck, 2017, The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading, Diabetes Care source
The citation is attached to the clause about assessing diabetes outcomes beyond HbA1c using sensor glucose profiles, not to the subsequent need for a normative-data repository. The Introduction says CGM profiles provide information missed by HbA1c, and the Conclusions say a patient’s CGM glucose profile has considerable value for diabetes management. (LLM, conf=0.88)
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This is similar to what has been observed in the T1D population and highlights the limitation of HbA1c to reflect true glycemic status for some individuals (14).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Supportedcites [14] Beck, 2017, The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading, Diabetes Care source
The Introduction states that a wide range of mean glucose concentrations can accompany a given HbA1c and that this has been demonstrated in people with type 1 diabetes. It also explains that HbA1c alone may therefore misrepresent an individual’s glycemic control. Page numbers were not supplied for these passages. (LLM, conf=0.91)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The current consensus conferences have somewhat arbitrarily set time in ranges for CGM sensor glucose levels for patients with T1D, with time in target identified as being between 70 to 180 mg/dL (12,13).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Supportedcites [12] Danne, 2017, International consensus on use of continuous glucose monitoring, Diabetes Care source
The ATTD consensus table (p. 1636) lists 70–180 mg/dL as the default CGM time-in-target range, supporting the attributed range for patients with type 1 diabetes. The paper does not describe that cutoff as “somewhat arbitrary”; that appears to be the citing authors’ characterization. (LLM, conf=0.90)
- Partly supportedcites [13] Maahs, 2016, Outcome measures for artificial pancreas clinical trials: a consensus report, Diabetes Care sourceExperimental — accuracy not yet measured
The Objective and Rationale section advocates standardized CGM glucose metrics for artificial-pancreas studies and says they are broadly applicable to type 1 diabetes studies. Additional Recommendations and Limitations mentions arbitrary glycemic ranges, but the retrieved passages do not identify 70–180 mg/dL as the target range or say that consensus conferences set that range somewhat arbitrarily. Page numbers were not available in our copy. (LLM, conf=0.80)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A total of 153 participants (age 7 to 80 years) were included in the analyses.”
- Also stated in: § Results · plain
“Therefore, 153 participants were included in the analyses, ranging from 7 to 80 years (mean, 31 years).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Also stated in: § Results · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Median sensor glucose between 70 and 140 mg/dL during the daytime and nighttime was 96% (IQR, 92 to 97%)”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Sensor glucose values .180mg/dL (10.0 mmol/L) and ,54 mg/dL (3.0 mmol/L) were uncommon.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In a sensitivity analysis after removing participants with first-degree family members with T1D (n 5 101), mean CGM glucose, time spent in the 70 to 140 mg/dL range, and glycemic variability as measured by CV were not different from the main analysis (data not shown).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedn = 101Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Mean average glucose was 98 to 99 mg/dL (5.4 to 5.5 mmol/L) for all age groups except those over 60 years, in whom mean average glucose was 104 mg/dL (5.8 mmol/L).”
- Also stated in: § Results · plain
“The mean average glucose level was 98 to 99 mg/dL (5.4 to 5.5 mmol/L) for all age groups except those $60 years old in whom mean average glucose a The underweight, normal weight, and overweight BMI categories for participants aged $18 years are ,18.5, 18.5 to ,24.9, and $24.9, respectively.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § Results · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly, in a sensitivity analysis after removing implausible data, mean glucose, time spent in the 70 to 140 mg/dL range, and CV were not different from the main analysis”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“mean glycemic variability measured by CV during the daytime and nighttime was 17% 6 3%”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Mean CGM use by the participants was 192 hours (8 days)”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedn = 153Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“with participants 6 to 11 years old having a median of 90% of their readings in this range, as compared with only 81% in the cohort over age 60 years.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“There are two possible factors that may have contributed to this weaker-than-expected correlation: (1) HbA1c was obtained prior to collection of CGM glucose data and ( 2) there are at most 10 days of CGM data (16).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Not checked — no full text of the sourcecites [16] Riddlesworth, 2018, Optimal sampling duration for continuous glucose monitoring to determine long-term glycemic control, Diabetes Technol Ther source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Understanding CGM metrics in healthy participants without diabetes is needed to serve as a benchmark for research studies to define impaired glycemic status and for future technologies aiming to help individuals achieve near-normal glycemic profiles.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Strengths of our study include the large sample size crossing the lifespan, multiple centers conducting the study, use of the latest generation technology, and up to 10 days of CGM wear time.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although our study fills the current gap in the data”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The reported mean absolute relative difference (MARD) for the Dexcom G6 in accuracy studies comparing sensor glucose values with reference blood glucose values is 9.0% (9).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Participants
What the sources say
- Not checked — no full text of the sourcecites [9] Shah, 2018, Performance of a factory-calibrated real-time continuous glucose monitoring system utilizing an automated sensor applicator, Diabetes Technol Ther source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Although pressure on the sensor during sleep may have resulted in inaccuracy of some CGM readings”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CGM profiles also provide the best window available to date to evaluate the frequency of biochemical hypoglycemia in clinical trials of new drugs and devices for the treatment of diabetes.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, HbA1c does not capture clinically relevant hyperglycemic and hypoglycemic patterns, which limits its utility in personalizing insulin-dosing decisions.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although data on glycemic profiles of healthy, nondiabetic individuals exist for older sensors, assessment of glycemic metrics with new-generation CGM devices is lacking.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“This is due to the fact that HbA1c is a reflection of overall glycemic control from the prior 60-90 days, as well as clear evidence from the DCCT establishing it as a surrogate marker for diabetes microvascular complications risk (1).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
What the sources say
- Not checked — no full text of the sourcecites [1] Nathan, 1993, The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus, N Engl J Med source
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Use of continuous glucose monitoring (CGM) is increasing for insulin-requiring patients with diabetes.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Participants were healthy, nondiabetic children and adults, primarily recruited from family, friends, and neighbors of patients seen in the diabetes clinics.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Participants
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Moreover, CGM metrics are being used to examine and compare the relative effectiveness and safety of new therapeutic agents and modern technologies in clinical diabetes research.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Continuous glucose monitoring (CGM) provides information on trends for hypoglycemia, hyperglycemia, and glycemic variability that helps patients and providers optimize glycemic control without increasing the risk for hypoglycemia.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Study participants provided written informed consent prior to study participation.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Participants
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Only participants with $72 total hours of CGM data (with at least 24 hours overnight) were included in the analysis.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Participants
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Only participants with no islet autoantibodies and a central laboratory HbA1c level ,5.7% were included in the analysis.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Participants
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“The study was conducted at 12 diabetes centers within the T1D Exchange Clinic Network after approval by institutional review boards (8).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Participants
What the sources say
- Not checked — no full text of the sourcecites [8] Beck, 2012, The T1D Exchange clinic registry, J Clin Endocrinol Metab source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper relies on this without support.
The 153 participants are representative of healthy nondiabetic individuals across age groups.
“Participants were healthy, nondiabetic children and adults, primarily recruited from family, friends, and neighbors of patients seen in the diabetes clinics.”
“Because the majority of our participants were white (93%), we were unable to evaluate whether results were consistent across races.”
The paper relies on this without support.
CGM profiles over up to 10 days reflect usual glucose profiles in healthy nondiabetic individuals.
“An early-generation Dexcom G6 CGM (Dexcom, Inc., San Diego, CA) in blinded mode (i.e., the participant was unable to see the glucose values) was worn for up to 10 days with one daily calibration using a study-provided blood glucose meter (Contour Next EZ; Ascensia Diabetes Care, Parsippany, NJ).”
“there are at most 10 days of CGM data (16).”
The authors state this as an assumption.
Low CGM readings <50 mg/dL during sleep are likely due to the participant lying on the CGM sensor.
“any low CGM readings ,50 mg/dL (2.8 mmol/L) during sleep time as defined by logs (in which case the participant was likely to be lying on the CGM sensor)”
The paper relies on this without support.
Participants' daily logs accurately recorded sleep and wake times.
“Participants completed a daily log indicating times and intensity of any exercise, start times of meals and snacks, and sleep and wake times.”
Citations
Cites
(1993) · New England Journal of Medicine · doi:10.1056/nejm199309303291401 · Cited by 24,667
International consensus on use of continuous glucose monitoring
Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., Garg, S., Heinemann, L., Hirsch, I., Amiel, S. A., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1600 · Cited by 2,069
Self-citationTranslating the A1C assay into estimated average glucose values
Nathan, D. M., Kuenen, J., Borg, R., Zheng, H., Schoenfeld, D., Heine, R. J., & for the A1c-Derived Average Glucose (ADAG) Study Group (2008) · Diabetes Care · doi:10.2337/dc08-0545 · Cited by 1,812
The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading
Beck, R. W., Connor, C. G., Mullen, D. M., Wesley, D. M., & Bergenstal, R. M. (2017) · Diabetes Care · doi:10.2337/dc17-0636 · Cited by 477
Self-citationThe T1D exchange clinic registry
Beck, R. W., Tamborlane, W. V., Bergenstal, R. M., Miller, K. M., DuBose, S. N., Hall, C. A., & for the T1D Exchange Clinic Network (2012) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2012-1561 · Cited by 456
Self-citationBergenstal, R. M., Ahmann, A. J., Bailey, T., Beck, R. W., Bissen, J., Buckingham, B., Deeb, L., Dolin, R. H., Garg, S. K., Goland, R., et al. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0051 · Cited by 307
Optimal Sampling Duration for Continuous Glucose Monitoring to Determine Long-Term Glycemic Control
Riddlesworth, T. D., Beck, R. W., Gal, R. L., Connor, C. G., Bergenstal, R. M., Lee, S., & Willi, S. M. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0455 · Cited by 266
Self-citationDeSalvo, D. J., Miller, K. M., Hermann, J. M., Maahs, D. M., Hofer, S. E., Clements, M. A., Lilienthal, E., Sherr, J. L., Tauschmann, M., Holl, R. W., et al. (2018) · Pediatric Diabetes · doi:10.1111/pedi.12711 · Cited by 233
Self-citationOutcome measures for artificial pancreas clinical trials: a consensus report
Maahs, D. M., Buckingham, B. A., Castle, J. R., Cinar, A., Damiano, E. R., Dassau, E., DeVries, J. H., Doyle, F. J., Griffen, S. C., Haidar, A., et al. (2016) · Diabetes Care · doi:10.2337/dc15-2716 · Cited by 211
Shah, V. N., Laffel, L. M., Wadwa, R. P., & Garg, S. K. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0143 · Cited by 197
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Correlation Between Blood Glucose Fluctuations and Osteoporosis in Type 2 Diabetes Mellitus
Chen, F., Wang, P., Dai, F., Zhang, Q., Ying, R., Ai, L.-Y., & Chen, Y.-Q. (2025) · International Journal of Endocrinology · doi:10.1155/ije/8889420 · Cited by 13
Glycemic fluctuations caused by COVID-19: Results from continuous glucose monitoring
Shen, Y., Zhang, L., Fan, X., et al. (2021) · Obesity Medicine · doi:10.1016/j.obmed.2021.100328 · Cited by 12
Zhang, X., Sun, F., Wongpipit, W., Huang, Y., & Wong, S. H. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.732751 · Cited by 13
Ramos-Leví, A., Rubio-Herrera, M., Matía-Martín, P., Pérez-Ferre, N., Marcuello, C., Sánchez-Pernaute, A., García, A. T., & Calle-Pascual, A. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12134295 · Cited by 11
“, performed in healthy non-diabetic individuals wearing CGM for 10 days [29].”
BackgroundChobot, A. P., Piona, C. A., Bombaci, B., Kamińska-Jackowiak, O., Mancioppi, V., & Passanisi, S. (2024) · Children · doi:10.3390/children11080907 · Cited by 11
Bruno, J., Walker, J. M., Nasserifar, S., Upadhyay, D., Ronning, A., Vanegas, S. M., Popp, C. J., Barua, S., & Alemán, J. O. (2024) · iScience · doi:10.1016/j.isci.2024.111501 · Cited by 16
Aging of glucose profiles in an adult population without diabetes
Pazos-Couselo, M., Portos-Regueiro, C., González‐Rodríguez, M., López, J. M. G., Alonso‐Sampedro, M., Rodríguez‐González, R., Fernández‐Merino, C., & Gudé, F. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.109929 · Cited by 12
Glycaemic Effects of a 156-km Ultra-trail Race in Athletes: An Observational Field Study
Parent, C., Mauvieux, B., Lespagnol, É., Hingrand, C., Vauthier, J., Noirez, P., Hurdiel, R., Martinet, Q., Delaunay, P., Besnard, S., et al. (2024) · Sports Medicine · doi:10.1007/s40279-024-02013-4 · Cited by 12
“These thresholds were chosen in accordance with the typical CGM resting values in healthy non-diabetic populations of between 70 and 140 mg/dL during approximately 96% of time, with values > 180 mg/dL being uncommon [28].”
Burckhardt, M., Auzanneau, M., Rosenbauer, J., Binder, E. B., Weiskorn, J., Hess, M., Klinkert, C., Mirza, J., Zehnder, L.-S., Wenzel, S., et al. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241288870 · Cited by 10
“13 Children and adolescents without diabetes spend 96% of the time in the glucose range between 70 and 140 mg/dL. 14 Therefore, another CGM-derived marker, Time in Tight Range (TTR, percentage of glucose values spent between 70 and 140 mg/dL) has been proposed as a new marker to further assess and…”
Liang, Z. (2022) · Frontiers in Medical Technology · doi:10.3389/fmedt.2022.1026830 · Cited by 10
Marks, B. E., Waldman, G., Reardon, K., Terrio, S., Kumar, A., Stafford, D. E. J., Garvey, K. C., & Wolfsdorf, J. I. (2020) · Pediatric Diabetes · doi:10.1111/pedi.13010 · Cited by 10
“We hypothesize that there was less educational value in wearing the CGM because those without diabetes would be expected to have minimal glycemic variability, with a mean glucose of 99 mg/dL and to spend 97% of the time with sensor glucose values between 70 and 140 mg/dL.(41) After completion of their respective curri…”
BackgroundToward an Optimal Definition of Hypoglycemia with Continuous Glucose Monitoring
MAHMOUDI, Z., Favero, S. D., Jacob, P., & Choudhary, P. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106303 · Cited by 10
Remission in Ketosis-Prone Diabetes
Kikani, N. J., & Balasubramanyam, A. (2022) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2022.06.005 · Cited by 11
Overduin, R. J., Venema, A., Lubout, C. M. A., et al. (2024) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2024.108573 · Cited by 9
Daya, N. R., Fang, M., Wang, D., Valint, A., Windham, B. G., Coresh, J., Echouffo-Tcheugui, J. B., & Selvin, E. (2024) · Diabetes Care · doi:10.2337/dc24-1990 · Cited by 10
Berry, S. A., Liarakos, A. L., Koutroukas, V., Choudhary, P., Wilmot, E. G., & Iqbal, A. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16284 · Cited by 11
Continuous Glucose Monitoring for Prediabetes: Roles, Evidence, and Gaps
Zahalka, S. J., Aktürk, H. K., Galindo, R. J., Shah, V. N., & Wang, C. C. L. (2025) · Endocrine Practice · doi:10.1016/j.eprac.2025.05.742 · Cited by 17
Self-citationChien, A., Shin, J., Liu, M., Arrieta, A., Cordero, T. L., Swensen, A. R., & Vigersky, R. A. (2025) · Diabetes Technology & Therapeutics · doi:10.1177/15209156251376654 · Cited by 11
D’Souza, N. C., Kesibi, D., Yeung, C., Shakeri, D., D’Souza, A. I., Macpherson, A., & Riddell, M. C. (2023) · Sensors · doi:10.3390/s23229059 · Cited by 9
Intra‐pancreatic fat is associated with continuous glucose monitoring metrics
Liu, Y., Kimita, W., Shamaitijiang, X., Skudder‐Hill, L., Sequeira, I. R., & Petrov, M. S. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15550 · Cited by 9
“15 18 Two related CGM metrics, time below range (TBR) and time above range (TAR), have been shown to provide valuable insights into glycaemic control and are therefore widely used in both clinical and research settings.”
BackgroundHamilton, R., McCarthy, O. M., Bain, S. C., & Bracken, R. M. (2024) · European Journal of Sport Science · doi:10.1002/ejsc.12201 · Cited by 9
Glycemic Variability: The Danger of a Physiologically Stable Metric
Battelino, T., & Dovč, K. (2020) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaa486 · Cited by 9
CGM for insulinoma screening: a prospective and observational case-control study
Ma, J., Huang, X., Zhao, J., Lu, J., Lu, W., Bao, Y., Zhou, J., & Han, J. (2021) · Endocrine Related Cancer · doi:10.1530/erc-20-0447 · Cited by 9
Dynamic Methods for Childhood Hypoglycemia Phenotyping: A Narrative Review
Rossi, A., Rutten, M. G. S., van Dijk, T. H., Bakker, B. M., Reijngoud, D., Oosterveer, M. H., & Derks, T. G. J. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.858832 · Cited by 10
Zignoli, A., Skroče, K., Lipman, D. J., & Zisser, H. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105809 · Cited by 9
Köhlmoos, A., & Dittmar, M. (2025) · Journal of the Endocrine Society · doi:10.1210/jendso/bvaf081 · Cited by 8
Postpartum Continuous Glucose Monitoring to Detect Dysglycemia After Gestational Diabetes
Cabrera, C., OGYAADU, S. J., Levister, C. M., Kaplan, L. R., Ipek, A., Ferrara, L. A., Levy, C. J., & O’Malley, G. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251351819 · Cited by 8
“30 Even in the participants with normal OGTT, we found much higher rates of time in hyperglycemia.”
Kaduk, K., Kaeber, M., Kühnel, A., Torrado, M. B., Grahlow, M., Derntl, B., & Kroemer, N. B. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.106035 · Cited by 11
“To 65–67 This was done using a sliding window of 1 h, advancing in steps of 5 min.”
Dashti, H. S., Leong, A., Mogensen, K. M., Annambhotla, M., Li, P., Deng, H., Carey, A. N., Burns, D. L., Winkler, M. F., Compher, C. W., et al. (2023) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2023.11.016 · Cited by 9
Shah, V. N., Aktürk, H. K., Vigers, T., Pyle, L. L., Oliver, N. S., & Klonoff, D. C. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0365 · Cited by 8
Self-citationMonnier, L., Colette, C., & Owens, D. R. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0006 · Cited by 8
Watso, J. C., Robinson, A. T., Singar, S., Cuba, J. N., & Koutnik, A. P. (2024) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00694.2023 · Cited by 8
García, I. A., García‐Moreno, R. M., Plaza, B. L., Alcalá, B. B., Pérez, P. V., Bonis, A. C. B., de Lucas, E. Z., Milla, S. P., Sánchez, M. C. P., & de Villar, N. G. P. (2024) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2024.111839 · Cited by 7
The Relationship Between Glycated Albumin and Time in Tight Range in Type 2 Diabetes
Ni, J., Han, W., Wang, Y., Yu, J., Lu, W. D., Wang, Y., Ma, X., Lu, J., & Zhou, J. (2025) · Journal of Diabetes · doi:10.1111/1753-0407.70073 · Cited by 8
Ulene, S. R., Wang, S., Cook, J. R., McAuley, F. M., Wooster, M. E., Faheem, K. F., Varoli, A., McGuinness, J. E., Vasan, N., Trivedi, M. S., et al. (2025) · Breast Cancer Research and Treatment · doi:10.1007/s10549-025-07745-z · Cited by 7
“The value of 140 mg/dL is considered clinically significant and has been used as a definition for hyperglycemia among patients without diabetes in previous studies [19–21].”
Bakhshi, B., Sultana, N., Lin, H., Fei, D., Vallejo, V., Gatanti, A., Steenkamp, D., Murabito, J. M., McKeown, N. M., Walker, M. E., et al. (2025) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2025.07.026 · Cited by 10
“Earlier investigations of younger, healthier adults (using Dexcom G6 pro, but with daily manual calibration) had suggested that individuals without diabetes spend the majority of time within a tight range of 70 – 140 mg/dL and therefore may exhibit limited variation in CGM metrics [47].”
Zaman, A., Grau, L., Jeffers, R., Steinke, S. C., Catenacci, V. A., Cornier, M., Rynders, C. A., & Thomas, E. A. (2023) · Obesity Science & Practice · doi:10.1002/osp4.702 · Cited by 9
Song, J., McNeany, J., Wang, Y., Daley, T. C., Stecenko, A. A., & Kamaleswaran, R. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109255 · Cited by 8
Richardson, R. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241298248 · Cited by 7
“Two open-source CGM datasets were retrospectively analyzed in this study: a nondiabetic dataset 25 and a T1D dataset, T1DiabetesGranada.”
What is a normal glucose value?
Shah, V. N., & Kerr, D. E. (2025) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(25)00023-3 · Cited by 7
Self-citationSpartano, N. L., Prescott, B. R., Walker, M. E., Shi, E., Venkatesan, G., Fei, D., Lin, H., Murabito, J. M., Ahn, D. T., Battelino, T., et al. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251315171 · Cited by 8
Self-citationBismuth, É., Joubert, M., Renard, É., Tubiana‐Rufi, N., Chaillous, L., Bonnemaison, É., Hanaire, H. P., Coutant, R., Schaepelynck, P., Beltrand, J., et al. (2025) · Diabetes & Metabolism · doi:10.1016/j.diabet.2025.101637 · Cited by 9
Bone Structure and Turnover in Postmenopausal Women With Long‐Standing Type 1 Diabetes
Shah, V. N., Qui, S., Stoneback, J. W., Qamar, L., Ferguson, V. L., Kohrt, W. M., Snell‐Bergeon, J. K., & Rao, S. D. (2023) · JBMR Plus · doi:10.1002/jbm4.10831 · Cited by 6
Self-citationOptimizing the design of time-restricted eating human trials
Varady, K. A., & Chow, L. S. (2024) · Nature Metabolism · doi:10.1038/s42255-024-01073-0 · Cited by 7
Kofod, D. H., Diederichsen, S. Z., Bomholt, T., Andersen, M. Ø., Andersen, A., Mannheimer, E. E., Rix, M., Liem, Y. S., Lindhard, K., Hansen, H. P., et al. (2025) · Diabetologia · doi:10.1007/s00125-025-06388-5 · Cited by 6
Fernández, C. G., Mitsigiorgi, R., Fochini, M., Leung, A., Pérez, C. F., & Nicolaides, K. H. (2025) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2025.09.031 · Cited by 6
Continuous glucose monitoring as a tool in early-stage type 1 diabetes
Carr, A. L. J., Sutton, H., Agesen, R. M., Bonifacio, E., Bosi, E., Gillard, P., Swaby, R., Vercauteren, J., & Besser, R. E. J. (2026) · Diabetologia · doi:10.1007/s00125-026-06707-4 · Cited by 8
Short term e-bicycle riding results in favorable cardiometabolic shifts in moderately active adults
Alessio, H. M., Ballard, K. D., Reidy, P. T., Hayward, K. M., Bagg, A. M., Cooley, R. A., O’Connell, M. J., Montoye, A. H. K., & Timmerman, K. L. (2024) · European Journal of Applied Physiology · doi:10.1007/s00421-024-05418-1 · Cited by 9
“We used a standard deviation of 17 mg/dL (Shah et al. 2019).”
MethodOhno, T., Tsujino, D., & Nishimura, R. (2024) · Expert Review of Endocrinology & Metabolism · doi:10.1080/17446651.2024.2400487 · Cited by 6
“In 2023, ATTD introduced a new blood glucose management indicator called Time in Tight Range (TITR), which is defined as the percentage of time within the range of 70 to 140 mg/dL per day [6].”
Castañeda, J., de Galan, B. E., van Kuijk, S. M. J., Arrieta, A., van den Heuvel, T. R. A., & Cohen, O. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15955 · Cited by 8
Nigi, L., Batzibal, M. S., Cataldo, D., & Dotta, F. (2024) · Diabetes Therapy · doi:10.1007/s13300-024-01656-w · Cited by 6
Monnier, L., Colette, C., Renard, É., Benhamou, P., Aouinti, S., Molinari, N., & Owens, D. (2024) · Diabetes & Metabolism · doi:10.1016/j.diabet.2024.101589 · Cited by 8
Continuous glucose monitoring in para cyclists: An observational study
Weijer, V., van der Werf, R., van der Haijden, M., et al. (2024) · European Journal of Sport Science · doi:10.1002/ejsc.12220 · Cited by 6
Karnebeek, K., Rijks, J. H. J., Dorenbos, E., Gerver, W. M., Plat, J., & Vreugdenhil, A. C. E. (2020) · Nutrients · doi:10.3390/nu12051228 · Cited by 6
Declercq, D., Helleputte, S., Marchand, S., et al. (2022) · Journal of Cystic Fibrosis · doi:10.1016/j.jcf.2022.08.016 · Cited by 6
Masutomi, H., Mineshita, Y., Ishihara, K., Hirao, K., Shibata, S., & Furutani, A. (2023) · European Journal of Nutrition · doi:10.1007/s00394-023-03138-4 · Cited by 6
“When the blood glucose level falls below 70 mg/dL, hypoglycemic symptoms may appear [52].”
BackgroundSawada, S., Kodama, S., Tsuchiya, S., Kurosawa, S., Endo, A., Sugawara, H., Hosaka, S., Kawana, Y., Asai, Y., Yamamoto, J., et al. (2020) · Clinical Obesity · doi:10.1111/cob.12409 · Cited by 6
Prendin, F., Streicher, O., Cappon, G., Rolfes, E., Herzig, D., Bally, L., & Facchinetti, A. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02856-5 · Cited by 5
Kim, J. Y., Kim, S., Park, S. H., Lee, J. A., Cho, S. H., Oh, R., Jang, M., Lee, Y., Kim, G., Hur, K. Y., et al. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112325 · Cited by 7
Díaz-Soto, G., Fernández-Velasco, P., & Bahillo-Curieses, P. (2025) · Endocrines · doi:10.3390/endocrines6030034 · Cited by 5
“Unlike the target range established by consensus for TIR, which is primarily based on its relationship with the goal of 7% HbA1c, TiTR partially derives its control targets from those observed in healthy populations [8].”
Safety and Glycemic Outcomes of the MiniMed 780G System with a Disposable All-in-One Sensor
Nally, L. M., Sherr, J. L., Garg, S. K., Marks, B. E., Laffel, L. M., Pihoker, C., Accacha, S. D., Thrasher, J. R., Abuzzahab, M. J., Reed, J. H. C., et al. (2025) · Diabetes Technology & Therapeutics · doi:10.1177/15209156251368928 · Cited by 8
“Thus, 107 pediatric participants completed the study.”
Self-citationRákóczi, G., Nagy, J., Jozaee, S., Jozaee, S., Szentes, B. L., Lee, J., Rancz, A., Hegyi, P. J., Agócs, G., & Sipter, E. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261426384 · Cited by 5
“37 In a cohort of 8315 individuals without diabetes, fasting glucose fluctuated daily by 7.5 mg/dL, and 40% were reclassified when multiple sequential CGM readings were analyzed, 38 underscoring the limits of single fasting measurements.”
InfluentialWen, X., Zeng, N., Zhang, N., Ou, T., Li, X., Li, X., Li, W., Xu, K., & Du, T. (2022) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s381565 · Cited by 5
van Albada, M. E., Shah, P., Derks, T. G. J., Fuchs, S. A., Jans, J. J. M., McLin, V. A., & van der Doef, H. P. J. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1190473 · Cited by 5
Elbarbary, N. S., & Ismail, E. A. R. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112042 · Cited by 8
Frank, S., Hames, K. C., Jbaily, A., Park, J. H., Stroyeck, C., & Price, D. A. (2022) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2022.0189 · Cited by 5
Mutlu, G. Y., Eviz, E., Can, E., Gökçe, T., Muradoğlu, S., & Hatun, Ş. (2023) · Clinical Diabetology · doi:10.5603/dk.a2022.0063 · Cited by 5
McShane, M. J., Zavareh, A. T., & Jeevarathinam, A. S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-822548-6.00133-3 · Cited by 6
2. Glycaemic control assessment and targets in type 2 diabetes
Colagiuri, S., & Ceriello, A. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112146 · Cited by 7
Sebastián‐Valles, F., García-Artacho, E., Santiago-Redondo, A., Castañar, S. G., Tapia-Sanchiz, M. S., Amar, S., Navas-Moreno, V., Palacios, E. P. L., Martín, J. A. A., & Marazuela, M. (2025) · Diabetes Technology & Therapeutics · doi:10.1177/15209156251403567 · Cited by 5
Godneva, A., Phillip, M., Segal, E., & Shilo, S. (2026) · Diabetes Care · doi:10.2337/dc25-2154 · Cited by 6
Bergenstal, R., Xu, Y., Dunn, T. C., Choudhary, P., & Ajjan, R. (2026) · Diabetologia · doi:10.1007/s00125-026-06739-w · Cited by 8
“…fails to take into account non-linear biological factors, which may explain the lack of alignment at lower (<49mmol/mol or 6.0% in National Glycohemoglobin Standardization Program [NGSP]) and higher (>58 mmol/mol or 7.5% NGSP) HbA 1c levels, which has been shown in multiple datasets [3–5].”
InfluentialSelf-citationCaretto, A., Terlizzi, G. D., Pedone, E., Pennella, R., COBELLI, F. D., Tresoldi, M., Scavini, M., Bosi, E., & Laurenzi, A. (2024) · Acta Diabetologica · doi:10.1007/s00592-024-02409-8 · Cited by 5
Igbe, T., & Kovatchev, B. P. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251323913 · Cited by 4
Agmon, O., Sigawi, T., & Ilan, Y. (2025) · Endocrine and Metabolic Science · doi:10.1016/j.endmts.2025.100262 · Cited by 4
Chen, N.-C., Shah, V. N., & McQueen, R. B. (2025) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaf544 · Cited by 4
Self-citationCross-Sectional and Individual Relationships between Physical Activity and Glycemic Variability
Sparks, J. R., Sarzynski, M. A., Davis, J. M., Grandjean, P. W., & Wang, X. (2022) · Translational Journal of the American College of Sports Medicine · doi:10.1249/tjx.0000000000000207 · Cited by 4
Górska, M., Kudzin, J., Borkowska, A., Szlagatys‐Sidorkiewicz, A., Szadkowska, A., Myśliwiec, M., & Toporowska‐Kowalska, E. (2023) · Nutrients · doi:10.3390/nu15030513 · Cited by 4
Jiang, W., Li, L., Wang, W., Liang, Y., Bai, X., Xu, Y., Guo, Q., Ge, L., Liang, J., Lü, B., et al. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1573963 · Cited by 5
Mercuri, V., D’Amico, T., Costa, D., Vito, C. D., D’Angelo, L., Schiaffini, R., & Gargiulo, P. (2020) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820949882 · Cited by 4
Diabetes Technology Meeting 2022
Huang, J. K., Yeung, A. M., DuBord, A. Y., Wolpert, H. A., Jacobs, P. G., Lee, W.-A., Drincic, A., Spanakis, E. K., Sherr, J. L., Prahalad, P., et al. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221148743 · Cited by 4
Self-citationHelleputte, S., Podlogar, T., & Gonzalez, J. T. (2025) · Performance Nutrition · doi:10.1186/s44410-025-00013-7 · Cited by 3
Continuous Glucose Monitoring Profiles in Elite-Level Professional European Football Players
Skroče, K., Zignoli, A., Mihic, N., Lipman, D. J., Turner, L. V., Riddell, M. C., & Zisser, H. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251388668 · Cited by 3
“28 This difference might be caused by the addition of 3 to 4 hours of regular daily exercise, higher energy expenditure, and assumingly a higher daily carbohydrate intake.”
Belfort-DeAguiar, R., Yeckel, C., Elshafie, A., Comarella, A. P., & Hwang, J. J. (2025) · Diabetes Care · doi:10.2337/dc25-1395 · Cited by 5
Capra, B. T., Hudson, S., Phillips, K. M., Marinik, E. L., Laskaridou, E., Johnson, A. L., Wilson, E. A., Dong, M., David, L. A., Ives, N., et al. (2026) · Journal of Nutrition · doi:10.1016/j.tjnut.2026.101370 · Cited by 4
Zanfardino, A., Özen, G., Ippolito, G., Roberti, D., Perrotta, S., Iafusco, D., del Giudice, E. M., & Casale, M. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112076 · Cited by 3
Pazos-Couselo, M., Lado‐Baleato, Ó., Izquierdo, V., Moreno-Fernández, J., Alonso‐Sampedro, M., Fernández‐Merino, C., & Gudé, F. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112286 · Cited by 3
On misempowerment & mobile health
Gray, J., & Mertes, H. (2025) · Medicine Health Care and Philosophy · doi:10.1007/s11019-025-10277-4 · Cited by 3
“One study found that blood-glucose levels of healthy CGM users remained in the target range 96% of the time (Shah et al. 2019).”
Continuous Glucose Measurements for Diet Monitoring in Healthy Adults
Ong, L., Lamoth, C. J. C., van Beek, A. P., Cao, M., Verkerke, G. J., & Wilhelm, E. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251361555 · Cited by 3
“One participant had a CV of 20.8% in combination with an average CGM of more than 5.8 mmol/L. Shah et al 35 reported that in a cohort of healthy, adults without diabetes, the mean CGM glucose was consistently below 6.2 mmol/L, and the within-individual coefficient of variation (CV) ranged from…”
InfluentialPark, J., Titman, A., Lancaster, G., Selvarajah, B., Collingwood, C., Powell, D., Das, U., Dharmaraj, P., Didi, M., Senniappan, S., et al. (2022) · Journal of the Endocrine Society · doi:10.1210/jendso/bvac043 · Cited by 2
Continuous Glucose Monitoring in Children and Adolescents with Congenital Adrenal Hyperplasia
Dubinski, I., Pozza, S. B., Debor, B., Nowotny, H. F., Reisch, N., Tschaidse, L., & Schmidt, H. (2023) · Journal of Clinical Research in Pediatric Endocrinology · doi:10.4274/jcrpe.galenos.2023.2023-3-5 · Cited by 4
Renard, É., Tubiana‐Rufi, N., Chaillous, L., Bonnemaison, É., Hanaire, H. P., Bismuth, É., Joubert, M., Coutant, R., Schaepelynck, P., Beltrand, J., et al. (2024) · Médecine des Maladies Métaboliques · doi:10.1016/j.mmm.2024.04.002 · Cited by 7
LeBlanc, E. S., Smith, N. X., Hwang, D., Young, D. R., Oshiro, C. E. S., Mayhew, M., Massimino, S., Catlin, C., & Clarke, G. N. (2024) · Contemporary Clinical Trials · doi:10.1016/j.cct.2024.107796 · Cited by 3
Glycemic variability and iatrogenic hypoglycemia: how to resolve
Monnier, L., Colette, C., Renard, É., Benhamou, P., Aouinti, S., Molinari, N., & Owens, D. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112360 · Cited by 3
Bender, B. F., & Berry, J. A. (2023) · Advances in Geriatric Medicine and Research · doi:10.20900/agmr20230002 · Cited by 5
Maia, A., Subías, D., Yuste, C., Albert, L., Vilaverde, J., Cardoso, M. H., & Rigla, M. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241236456 · Cited by 2
“3,7 When evaluating continuous glucose monitoring profiles in healthy individuals with negative islet antibodies and normal HbA 1c using CGM in blinded mode, Shah et al 7 reported an average mean glucose level ranging from 98 to 99 mg/dL (5.4-5.5 mmol/L) and a median time spent between 70 and 140 mg/dL (3.9-7.8 mmol/L…”
InfluentialŠimunović, M., Kumrić, M., Rušić, D., Šimunović, M. P., & Božić, J. (2024) · Diagnostics · doi:10.3390/diagnostics14242801 · Cited by 3
“In addition, there is more and more available data on the use of the CGM system in healthy subjects, further strengthening the current knowledge about the importance of understanding the complex metabolism of glucose and the establishment of adequate normative for future studies [72–74].”
Chimatapu, S. N., Mittelman, S. D., Klomhaus, A. M., Vu, M. H., Habib, M., Wisk, L. E., Black, D. S., & Vidmar, A. P. (2025) · Pediatric Obesity · doi:10.1111/ijpo.70035 · Cited by 2
Sanchiz, M. S. T., Navas-Moreno, V., Sebastián‐Valles, F., López, J. J. R., Ganga, C. S. L., Carrillo-López, E., Castañar, S. G., Amar, S., Vargas, M. L., Martín, J. A. A., et al. (2025) · Diabetology · doi:10.3390/diabetology6070069 · Cited by 2
CHANDRAN, S. R., Sng, G. G. R., Wong, C. Y.-H., Ang, W. M., & Gardner, D. S.-L. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251384682 · Cited by 3
“20,32 Second, the higher TBR70 in our study could also contribute to an overall lower mean glucose and account for the differences.”
Metadata
- Authors
- Shah, V. N., DuBose, S. N., Li, Z., Beck, R. W., Peters, A. L., Weinstock, R. S., Kruger, D., Tansey, M., Sparling, D., Woerner, S., Vendrame, F., Bergenstal, R., Tamborlane, W. V., Watson, S. E., & Sherr, J.
- Affiliations
- Barbara Davis Center for Diabetes, Aurora, Colorado
- Jaeb Center for Health Research (US)
- University of Southern California (US)
- SUNY Upstate Medical University (US)
- Henry Ford Health (US)
- University of Iowa (US)
- University of Oklahoma Health Sciences Center (US)
- Indiana University School of Medicine
- University of Miami (US)
- Park Nicollet Clinic (US)
- Yale University (US)
- University of Louisville (US)
- Corresponding author
- DuBose, Stephanie N
- ORCID
- Venue
- The Journal of Clinical Endocrinology & Metabolism
- Publisher
- The Endocrine Society
- Published
- 2019-04-25
- Volume
- 104
- Issue
- 10
- Pages
- 4356-4364
- ISSN
- 0021-972X (ISSN-L)
- 1945-7197
- PMC
- PMC7296129
- OpenAlex
- W2941246007
- Semantic Scholar
- 22170f815dcf3fd4385ff1485a7902d3413b5ae7
- Cited by
- 315
- References
- 17
- Citations by year
- 2026: 57 · 2025: 71 · 2024: 63 · 2023: 41 · 2022: 38 · 2021: 26 · 2020: 15 · 2019: 4
- FWCI
- 14.29
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Journal of Clinical Endocrinology & Metabolism (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- Funders
- Leona M. and Harry B. Helmsley Charitable Trust
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › Medical Laboratory Technology › Clinical Laboratory Practices and Quality Control
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- continuous glucose monitoring, glycemic variability, hypoglycemia, hyperglycemia, Dexcom G6, reference values
- MeSH terms
- Adolescent, Adult, Age Factors, Aged, Blood Glucose, Child, Circadian Rhythm, Female, Humans, Hyperglycemia, Hypoglycemia, Male, Middle Aged, Monitoring, Physiologic, Prospective Studies, Reference Values, Sex Factors, Blood Glucose Self-Monitoring, Benchmarking, Young Adult, Healthy Volunteers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-14, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-14 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
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