Precision Diets for Diabetes Prevention: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Precision Diets for Diabetes Prevention (NCT03919877), a na na study. The primary outcome is Change in Glycemic Control as Measured by Change Blood Sugar Values.

2. Clinical Question

3. Trial Design

NCT03919877, titled Precision Diets for Diabetes Prevention, is recorded as completed. The registry lists an enrollment of 115 and gives a start date of 2018-05-24 and a primary completion date of 2023-10-18. The phase is listed as NA. The design is single group, allocation is NA, and masking is none. These entries describe a study without a randomized, masked treatment comparison in the registry record.

The registry lists one arm, Optimizing Diet for Glycemic Control, and dietary and oral food challenge interventions. Its available arm description outlines a metabolic-testing phase that includes an oral glucose tolerance test, CGM use during the test, and requests for participants to repeat the test at home twice. It also names an insulin sensitivity test, described as Steady State Plasma Glucose. The rest of the arm description is incomplete in the registry record, so further procedural details are not reported there.

The study’s stated rationale is that people may follow different physiological paths toward prediabetes and diabetes. Investigators aimed to characterize those differences, identify potential biomarkers of particular metabolic defects, and use the information to support personalized diet recommendations. Those aims explain the emphasis on metabolic tests, omics, CGM, and meal data, but they do not establish that a biomarker or personalized recommendation was successfully identified.

Because the registry describes a single group and no randomized comparator, observed changes would be interpreted as within-study measurements rather than as a randomized estimate of the effect of one diet relative to another. The registry record does not report a formal protocol rationale for the enrollment target or a statistical plan that explains how the study’s aims were to be tested.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Overall Study

Analysis population for the primary outcome, as defined in the registry: Participants with all metabolic testing results, omics, CGM, and meal data

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

6. Primary Endpoint

The registry lists the primary outcome as Change in Glycemic Control as Measured by Change Blood Sugar Values, measured over Assessed at a meal (2 to 6 weeks after baseline), starting just prior eating, for a period of 3 hours.

The primary endpoint is change in glycemic control, measured as change in blood sugar values in mg/dL. It is derived from continuous glucose monitor data and is described as a mean with standard error. The registry says the change is assessed from baseline through all study phases and stratified by food type and metabolic subtype. Its stated assessment window is at a meal, 2 to 6 weeks after baseline, beginning just before eating and continuing for a period of 3 hours.

CGM measurements can characterize glucose over time rather than relying only on a single measurement. In general, examining a meal-related pattern can describe how glucose values change across the specified observation period. Considering food type and metabolic subtype may help describe whether patterns differ across those categories, although the registry record does not report the resulting estimates or category-specific findings.

The registry identifies the primary outcome population as participants with all metabolic testing results, omics, CGM, and meal data. That definition makes completeness of several data types relevant to inclusion in the outcome analysis. The record does not report how many participants met that definition or how incomplete data were handled. No numerical endpoint estimate, standard error, confidence interval, or test result is reported in the registry record, so the magnitude and precision of the measured change cannot be evaluated from the available outcome information.

7. Why No Re-analysis Is Shown

8. Statistical Interpretation

9. Statistical Methods Explained

The registry describes a continuous outcome summarized as a mean, with standard error as its dispersion measure. It also describes CGM-based values collected around a meal and a change from baseline through study phases. These entries identify the type of data and the intended summary, but they do not specify the statistical model, the exact contrast used to define change, or the method for handling repeated measurements. Those details are not reported in the registry record.

What does a mean represent?

In general, a mean is a measure of the center of a set of numeric observations. For a continuous outcome such as blood sugar in mg/dL, a mean can provide a compact description of the average value or average change in the analyzed group. A group mean does not imply that every participant experienced the same pattern. Individual trajectories can vary around the average, and a mean alone does not describe the full distribution of measurements.

The registry’s endpoint is described as change in glycemic control, measured from baseline through all phases and stratified by food type and metabolic subtype. An analysis of change usually requires a defined baseline value and a rule for combining or comparing later values. The registry description does not state the precise calculation, whether all time points are included in one model, or whether the outcome is first summarized for each participant. Those choices can affect the interpretation of an estimated mean change.

How should standard error be read?

In general, standard error describes the uncertainty in an estimated statistic, such as a sample mean. It is different from the standard deviation, which describes spread among observations. A smaller standard error generally indicates a more precise estimate under the assumptions used, whereas a larger standard error indicates more uncertainty. The standard error does not show the range of individual responses, and it should not be read as a measure of clinical importance.

A confidence interval can combine an estimate and its uncertainty into a range of plausible values under a statistical model. A test result may also address compatibility with a specified null hypothesis. These summaries answer different questions: the estimate concerns size and direction, the interval concerns precision, and a test concerns evidence relative to a particular hypothesis and assumptions. None of the numerical estimate, standard error, confidence interval, or p-value for the primary endpoint is reported in the registry record.

Why do repeated glucose measurements matter?

In general, measurements taken repeatedly from the same participant are related rather than independent. A statistical analysis should account for that dependence if it uses multiple CGM observations from each person. Ignoring correlation can misstate uncertainty. The appropriate approach depends on the observation schedule, the estimand, and the data structure. The registry states that CGM data are used for the outcome and describes a meal observation period, but it does not report the repeated-measures model or other method used.

In general, over-dispersion matters when observed variability is greater than a model assumes. If a model understates variability, its uncertainty estimates may be too small and inferential conclusions may appear more precise than warranted. This is a general statistical consideration, not a reported feature of this study. The registry record does not report a model specification, a dispersion assessment, or adjustments for over-dispersion.

What do the planning assumptions mean?

The record’s sample-size assumptions describe a planning illustration based on the observed effect, using a two-sided significance level of 0.05, equal allocation between two arms, and a normal approximation. The effect to detect is described as a stated fraction of the observed effect. Those are assumptions for planning calculations, not a description of the enrolled study design: the registry lists a single group and no randomized two-arm comparison. Any sample-size illustration is a planning illustration, not observed power, and it should not be read as evidence that the study had a particular probability of detecting an effect.

The registry record does not report the original sample-size justification, the target effect used in the study’s own design, or whether the planning assumptions correspond to the protocol. The numerical outputs of the planning illustration are not included in the registry entries. Therefore, they cannot be used to infer the study’s actual design rationale or the precision of its endpoint estimates.

Why does the study design shape inference?

In general, random allocation helps make groups comparable on average, while masking can reduce some forms of bias in treatment assignment or outcome assessment. This registry record lists allocation as NA, a single-group model, and masking as none. Thus, an endpoint change would describe measurements in the study group, not a randomized contrast with a control group. The study’s metabolic characterization may be useful for describing patterns, but the registry entries do not establish that a given dietary approach caused a particular pattern.

The registry lists an enrollment of 115 and defines the primary outcome population by the availability of metabolic testing, omics, CGM, and meal data. The count enrolled and the count included in that defined analysis population are not necessarily interchangeable. The registry record does not report the number analyzed for the primary endpoint, missing-data procedures, or the statistical analysis plan. These omissions limit evaluation of reproducibility and precision.

10. Multiplicity, Missing Data and Analysis Populations

The registry identifies a primary outcome and lists a secondary outcome concerning change in the area under the curve of blood glucose level. It also records three outcome measures and four analyses. Those entries indicate that more than one outcome measure or analysis is involved, but the registry record does not give enough detail to determine the complete set of hypotheses, the relationship between each measure and analysis, or which comparisons were considered confirmatory.

In general, testing multiple hypotheses can increase the chance of observing at least one apparently notable result by chance. Statistical plans may address this through a hierarchy of outcomes, adjustment procedures, or explicit interpretation of analyses as exploratory. The registry record does not report whether multiplicity was addressed, which adjustment method might have been used, or whether the analyses were designated as confirmatory or exploratory. With no numerical outcome results reported, the impact of any multiplicity approach cannot be assessed.

Missing-data methods are also not reported in the registry record. The primary outcome population is described as participants with all metabolic testing results, omics, CGM, and meal data, but the record does not state how many participants met this definition. It does not describe whether incomplete records were excluded, imputed, or handled through a model, nor does it report sensitivity analyses for missingness. In general, conclusions can depend on why data are missing and on the assumptions used to address them.

The analysis population is not described beyond the stated complete-data definition for the primary endpoint. The record does not report whether analyses used all enrolled participants, a subset with available measurements, or another defined population. The statistical model, covariates, subgroup methods, and multiplicity procedures are not reported in the registry record. These details would be needed to understand how the reported outcome estimates were obtained and how robust they might be to analytic choices.

11. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Deaths

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 0% reporting threshold.

The registry reports one study group, Optimizing Diet for Glycemic Control, with 115 participants at risk for serious adverse events and 115 at risk for other adverse events. It records zero participants affected by serious adverse events, zero affected by other adverse events, and zero deaths. No top serious adverse-event terms are listed.

These are the safety counts recorded in the registry. They do not describe the glycemic outcome, and they do not establish that every possible adverse experience was assessed or captured. In general, an adverse-event count depends on the event definitions, collection period, reporting process, and completeness of follow-up. Those details are not reported in the registry record.

The counts are group-level summaries. They do not provide event rates by time, severity categories beyond the serious and other groupings, or details about individual events. The registry record also does not report comparative safety estimates, which is consistent with its single-group design rather than a randomized comparison. The zero counts should therefore be read as the reported registry entries, not as a basis for conclusions beyond those entries.

12. What the Result Does, and Does Not, Mean

The record describes a completed study intended to investigate physiological differences related to prediabetes and diabetes, including the possibility of distinct metabolic pathways and biomarkers. It specifies a CGM-based primary endpoint focused on change in blood sugar values around a meal, with analysis by food type and metabolic subtype. It also documents a single-group design and an enrollment of 115.

The numerical primary-outcome findings are not reported in the registry record. Without an estimate and its uncertainty, the direction, size, and precision of any glycemic change cannot be assessed. The record also does not report whether the intended biomarker or subtype work produced findings or whether personalized diet recommendations were tested. A stated research objective should not be mistaken for an established result.

In general, a single-group study can describe measurements over time and explore patterns within participants. A change observed in such a study is not, by itself, a randomized estimate of what would have happened under a different diet or control condition. Other influences may coincide with time or study participation, and the registry does not report a comparator that would separate those influences from an intervention effect.

The study’s emphasis on metabolic testing, omics, CGM, and meal data may support detailed characterization, but the availability of detailed measurements does not alone establish a causal conclusion. The registry record does not provide the statistical model, the number included in the primary analysis, or procedures for missing data and multiplicity. Those limits matter when judging how far any eventual estimates could be generalized or how stable they might be to analysis choices.

The appropriate conclusion from the record is narrow: it documents the study’s objective, design, endpoint definition, and safety counts. It does not permit a conclusion about whether a dietary approach changed glycemic control, which food type performed differently, or whether metabolic subtypes predict response. Those questions require outcome results and adequate information about their analysis.

13. Why This Trial Matters Statistically

This study is statistically notable for combining a single-group design with a broad metabolic characterization objective. The registry describes measurements from metabolic tests, omics, CGM, and meal data, while defining the primary outcome around meal-related changes in glucose. Such measurements can create opportunities to describe patterns across people and metabolic subtypes, but they also make clear outcome definitions and analysis methods especially important.

The primary endpoint is continuous and is specified as a mean with standard error. In general, continuous summaries can convey the scale and direction of an average pattern, while uncertainty estimates help readers judge precision. For a meal-related CGM outcome, the observation schedule and method for handling repeated readings can affect both the estimate and its uncertainty. The registry record does not report those analytic details or numerical results, so the potential statistical value of the detailed measurements cannot be evaluated from the posted outcome information.

The study also illustrates the distinction between characterization and comparison. A single-group study may be used to investigate biological variation and generate hypotheses about subtypes or biomarkers. A randomized comparator is generally needed for a direct estimate of the effect of one intervention relative to another. Here, the registry lists no randomized comparison, so the design should be read in light of its stated exploratory and personalization aims, not as a controlled diet-effect comparison.

Finally, the record makes analysis-population and missingness questions salient: the primary outcome population requires several types of complete data, but the number meeting that definition and the handling of incomplete data are not reported. Multiplicity procedures are also not reported despite multiple outcome measures and analyses being recorded. These are important considerations for interpreting complex, data-rich clinical research.

14. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

The registry record does not report numerical primary-endpoint results, including the estimated mean change, its standard error, a confidence interval, or a p-value. This prevents assessment of effect direction, magnitude, and precision. The secondary outcome is named, but its numerical results are not reported in the available information either.

The design is single group, with allocation NA and no masking. The registry does not describe a randomized comparator, so the study record does not support a direct randomized contrast between dietary strategies or against a control condition. The enrollment count is 115, but the number included in the primary outcome population is not reported.

The primary outcome population requires all metabolic testing results, omics, CGM, and meal data. The record does not explain how incomplete records were handled or whether the complete-data requirement led to exclusions. Missing-data assumptions and sensitivity analyses are not reported. The statistical model, the precise definition of change, treatment of repeated CGM measurements, and any subgroup-analysis methods are also not reported.

The registry lists three outcome measures and four analyses, but does not report a multiplicity strategy or identify the full set of analyses. It also does not report the study’s own sample-size justification, interim analyses, or a detailed statistical plan. The arm description is incomplete in the registry record, limiting assessment of the full intervention and measurement procedures. These limitations concern what can be established from the record; they do not show that an unreported procedure was or was not performed.

15. Related Tutorials

16. Related Statistical Calculators

17. Sources

• ClinicalTrials.gov: Precision Diets for Diabetes Prevention — NCT03919877.
• Registry record last updated 2026-05-22; status last verified 2026-04.
• Results first posted 2026-05-22.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

18. Record Summary

NCT03919877, Precision Diets for Diabetes Prevention, is recorded as completed, with enrollment of 115. The registry lists a start date of 2018-05-24 and a primary completion date of 2023-10-18. Its design is single group, allocation is NA, masking is none, and phase is NA.

The stated aim is to investigate physiological differences and possible metabolic pathways related to prediabetes and diabetes, with interest in biomarkers and personalized diet recommendations. The primary outcome is change in CGM-derived glycemic control in mg/dL, summarized as a mean with standard error and assessed around a meal. The primary outcome population is described as participants with all metabolic testing results, omics, CGM, and meal data.

The record lists dietary and oral food challenge interventions, one secondary outcome, and safety counts of zero affected participants for serious and other adverse events, with zero deaths. Numerical glycemic outcome results, the analysis population count, and detailed statistical methods are not reported in the registry record.