A Study of How Insulin Peglispro (LY2605541) and Insulin Glargine Affect the Breakdown of Fat and Sugar in Type 1 Diabetics: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for A Study of How Insulin Peglispro (LY2605541) and Insulin Glargine Affect the Breakdown of Fat and Sugar in Type 1 Diabetics (NCT01925989), a randomized phase 1 study. The primary outcome is Sleep Respiratory Quotient (RQ) in Type 1 Diabetes Mellitus (T1DM).

2. Clinical Question

3. Trial Design

This was a randomized, open-label, Phase 1 crossover study of insulin peglispro and insulin glargine in participants with type 1 diabetes. In a crossover design, a participant receives more than one treatment in separate periods, so treatment comparisons can reflect within-participant as well as between-participant information when analyzed appropriately. The registry describes two treatment periods. It does not report the sequence-generation method, washout arrangements, or a detailed model for accounting for period or sequence effects.

During each period, participants received once-daily basal insulin for 28 to 35 days, then underwent procedures assessing the body’s use or storage of fats and sugars. Mealtime insulin continued throughout the study. The record identifies two treatment sequences: insulin peglispro followed by insulin glargine, or insulin glargine followed by insulin peglispro. It also lists an untreated control arm of healthy participants, who did not receive study medication. That healthy group is not a randomized insulin-treatment comparator in the primary question.

The listed enrollment was 27. The primary endpoint population was described as randomized participants with type 1 diabetes who received at least one dose. The registry notes that data from two participants could not be used for the primary endpoint, for reasons involving blood glucose control and a change in thyroid medication. The specific timing and handling of these unavailable data are not reported in the registry record.

The study is marked completed. Its listed start was 2013-11 and its primary completion was 2015-10. Masking is recorded as none. The registry describes the study as Phase 1 but does not report the original sample-size rationale, the full statistical analysis plan, or whether interim analyses were conducted.

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.

Period 1

Reasons for not completing this period:

Period 2

Analysis population for the primary outcome, as defined in the registry: All randomized participants with T1DM that received at least 1 dose of study drug. Data from 2 participants could not be used: one due to uncontrolled blood glucose and one due to an increase in thyroid medication.

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 Sleep Respiratory Quotient (RQ) in Type 1 Diabetes Mellitus (T1DM), measured over Day 30 post dose, overnight period (0000 to 0600 hours).

The primary endpoint was mean sleep respiratory quotient (RQ) in participants with type 1 diabetes, measured on Day 30 after dosing during the overnight interval from 0000 to 0600 hours. RQ is calculated as carbon dioxide produced by the body divided by oxygen consumed. The registry says whole room calorimetry was used and describes RQ as an assessment of lipid or glucose metabolism, with basal metabolic rate estimated from carbon dioxide production.

RQ is used as an indirect physiological measure of substrate use rather than a direct measurement of a clinical outcome. Its value can help characterize metabolic activity under the conditions in which it is measured. For this study, the overnight measurement was intended to compare metabolism during sleep after exposure to the two basal insulin regimens. The endpoint therefore addresses a short-term physiological question, not whether participants experienced a patient-centered benefit.

The registry reports a mean and standard deviation for each insulin group. The posted means were 0.822 for insulin peglispro and 0.846 for insulin glargine. The posted comparison expresses the glargine mean relative to the peglispro mean. Interpretation should account for the endpoint’s indirect nature, the overnight measurement window, and the limited analysis information in the registry. The record does not report the confidence-interval method or a sponsor-reported statistical test for the primary comparison.

7. Results Posted in the Registry

• Insulin Glargine: mean 0.85 versus 0.82 on control; difference 0.02 (95% CI 0.00 to 0.05), standardized effect d = 0.81; z-test p = 0.045.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Lipid Oxidation in T1DM and Healthy Participants

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • Insulin Peglispro: mean 0.89 versus 0.91 on control; difference -0.01 (95% CI -0.03 to 0.00), standardized effect d = -0.63; z-test p = 0.112.
  • Insulin Glargine: mean 0.90 versus 0.91 on control; difference -0.01 (95% CI -0.02 to 0.01), standardized effect d = -0.27; z-test p = 0.506.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Insulin Peglispro and the control. The interval gives the range of effects that remain compatible with the data.
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Insulin Glargine and the control. The interval gives the range of effects that remain compatible with the data.

Basal Metabolic Rate (BMR) for T1DM

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • Insulin Glargine: mean 1,404 versus 1,511 on control; difference -107.40 (95% CI -278.31 to 63.51), standardized effect d = -0.49; z-test p = 0.218.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Insulin Glargine and the control. The interval gives the range of effects that remain compatible with the data.

Change in RQ, Pre-breakfast to Post-breakfast Meal, in T1DM

• Endpoint type: Continuous: a mean value per participant, compared as a difference in means.
• Test: Unadjusted two-sample z-test for the difference in means, using the posted standard deviations.
• Result:
  • Insulin Glargine: mean 0.87 versus 0.84 on control; difference 0.04 (95% CI 0.00 to 0.08), standardized effect d = 0.85; z-test p = 0.047.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically higher for Insulin Glargine than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The registry lists secondary questions spanning lipid oxidation, basal metabolic rate, sleep RQ in untreated healthy participants, time spent in lipid oxidation, and the change in RQ around a meal. Numeric results are posted for lipid oxidation, basal metabolic rate, and the pre-breakfast to post-breakfast RQ change. Results for the other listed secondary measures are not reported in the registry record.

Across the posted numeric comparisons, the lipid oxidation and basal metabolic rate intervals include zero, while the breakfast RQ interval is just above zero. This pattern offers limited support for a consistent separation across all the metabolic outcomes. The breakfast comparison points in the same general direction as the primary RQ contrast, but it is a distinct measurement context and does not independently establish the primary result. The secondary outcomes assess related but non-identical aspects of metabolism, so agreement or disagreement among them should not be treated as a simple replication test.

The secondary comparisons are unadjusted recalculations, and the registry reports no sponsor analyses for them. With multiple outcomes, examining each result separately can make a nominally notable p-value less compelling as confirmatory evidence. The registry does not report adjustment for multiple comparisons, an outcome hierarchy, or a prespecified interpretation strategy. For that reason, the secondary results are best viewed as descriptive and hypothesis-generating. They can help characterize the observed pattern, but they carry less inferential weight than a prespecified primary analysis with a reported method.

These results do not resolve whether the observed primary RQ contrast reflects a stable treatment effect. The secondary endpoints are measured on different scales or at different times, and the registry does not provide a single combined analysis relating them to the primary endpoint. They add context about the range of measurements taken, rather than a definitive overall metabolic conclusion.

Secondary outcomes registered for this trial, for reference (only those above were recalculated):

• Lipid Oxidation in T1DM and Healthy Participants
• Basal Metabolic Rate (BMR) for T1DM
• Sleep Respiratory Quotient (RQ) of Untreated Healthy Participants
• Total Number of Minutes of Lipid Oxidation (RQ Below 7.6) in T1DM and Healthy Participants
• Change in RQ, Pre-breakfast to Post-breakfast Meal, in T1DM

9. Statistical Interpretation

10. Statistical Methods Explained

The posted results describe continuous measurements using a mean and standard deviation. A mean is the arithmetic average of the observations contributing to a group summary. A standard deviation describes how spread out individual observations are around that group’s mean; it is not the uncertainty of the mean itself. The group counts, means, and standard deviations allow an unadjusted comparison to be recalculated, but they do not provide all the information that a full trial analysis might use.

What does the mean difference represent?

For the primary comparison, the contrast is defined as the insulin glargine mean minus the insulin peglispro mean. The posted estimate is 0.024. A positive value therefore means that the observed glargine mean is higher on the RQ scale. This direction is a matter of how the contrast is written; it does not establish that one regimen is better. The comparison is a difference in group means, not a relative percentage change or a measure of individual response.

The registry identifies the study as a crossover trial, which matters for analysis. In a parallel-group comparison, observations from one treatment group are generally independent of those in the other group. In a crossover study, each participant may contribute measurements under both treatments, so measurements from the same person are related. A paired or repeated-measures analysis can use that relationship and may account for treatment period or sequence. The posted summary statistics do not show the participant-level pairing. The registry record does not report the sponsor’s analysis method, so a simple summary-statistic recalculation cannot establish whether the original analysis handled the crossover design appropriately.

How should the confidence interval and p-value be read?

A confidence interval expresses uncertainty around an estimated contrast. Its width reflects both the observed variability and the amount of information available, along with the method used to calculate it. The primary interval extends from 0.001 to 0.047. It is close to zero at the lower limit and covers a range of possible differences rather than a single established value. The confidence level and calculation method are not reported in the registry record. The interval used in this analysis is a recalculated interval based on posted summaries.

The unadjusted p-value of 0.045 describes how compatible the observed comparison is with a null hypothesis of no difference under the assumptions of the calculation. It does not give the probability that the null hypothesis is true, and it does not measure the probability that the result will replicate. It also does not measure the clinical importance of the contrast. Because there is no reported sponsor analysis and the trial has a crossover structure, the p-value should be understood as a descriptive, unadjusted recalculation rather than as a substitute for the trial’s planned inference.

Why can variability and design matter?

Standard deviations show that participant measurements vary around each reported mean. Greater variability generally makes a mean less precisely estimated when the amount of information is unchanged. In a crossover design, the variation in within-person treatment differences can be important, but it cannot be reconstructed from separate group standard deviations alone. The posted data do not provide the within-person covariance or paired differences. Consequently, the recalculation may not reflect all the statistical information available to the original investigators.

Over-dispersion is a general statistical concern when observed variability exceeds what a model assumes. In that situation, model-based standard errors can be too small if the extra variability is ignored. This trial reports continuous means and standard deviations, not a count model, and over-dispersion is not identified as an issue in the registry record. The broader principle is that an analysis should match both the outcome’s distribution and the design that generated the observations.

What do the secondary calculations add?

The registry lists secondary outcomes related to lipid oxidation, basal metabolic rate, sleep RQ in untreated healthy participants, minutes of lipid oxidation, and the change in RQ around breakfast. Numeric results are posted for lipid oxidation, basal metabolic rate, and breakfast RQ. The corresponding comparisons in this analysis are unadjusted calculations from the posted group summaries. They should be interpreted cautiously because the record does not report a multiplicity strategy, a hierarchy of outcomes, or sponsor analyses for these comparisons.

When many outcomes are examined, the chance of observing at least one apparently unusual result can increase even when no underlying treatment effect exists. This general issue is called multiplicity. Adjusting for multiplicity or prioritizing outcomes in advance can change how individual p-values are interpreted. The registry does not report whether such procedures were part of the study’s original plan, so the posted secondary p-values should not be treated as confirmatory evidence on their own.

The sample-size calculations are planning illustrations based on observed effects, with the assumptions stated in the sample-size discussion. They are not a record of the trial’s original design calculation. Such illustrations can show how the required number of participants changes under alternative target effects and power levels, but they depend on the assumptions used and on whether the observed effect is a reasonable planning value. The registry’s original sample-size justification is not reported.

11. Confidence Intervals and Precision

The primary mean difference is 0.024, and its confidence interval runs from 0.001 to 0.047. The lower limit is close to zero, while the upper limit permits a larger positive contrast. This means the point estimate gives only one summary of the comparison; the interval communicates uncertainty about its magnitude. The interval does not establish that every value within it is equally plausible, nor does it describe the range of individual participant outcomes.

The unadjusted p-value is 0.045, but that value does not replace the interval’s information about effect size and precision. The interval is relatively informative about direction under its calculation assumptions, yet it leaves substantial uncertainty about how large the difference may be. The confidence level and method used for the registry’s original analysis are not reported in the registry record. The interval is a recalculation from posted summary statistics.

Precision is also affected by the study’s design and available data. Separate means and standard deviations do not show the correlation between a participant’s measurements across the crossover periods. That information can be important for estimating uncertainty in a paired comparison. The primary endpoint description also notes that data from two participants could not be used, for reasons involving uncontrolled blood glucose and an increase in thyroid medication. The record does not describe a missing-data method or provide enough detail to assess how those unavailable measurements affect the interval.

Accordingly, the confidence interval should be read as uncertainty around a simple recalculated group-mean contrast, not as a complete account of uncertainty under every feature of the trial design. It is evidence about the precision of this particular summary comparison, not a guarantee of reproducibility or a statement about clinical importance.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The planning figures are expressed as participants per arm, not as a total across the study. They are based on a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. The effect to detect is set to the stated fraction of the observed effect. These are planning illustrations and are not observed power.

The 80% power column gives the number of participants per arm the illustration estimates would be needed to have an 80% chance of detecting the specified effect under its assumptions, if that effect were real. The 90% power column expresses the same idea using a higher target chance of detection. These are design calculations, not statements about the probability that the study’s result is true or about the probability that the observed result arose by chance.

Rows using 100% of the observed effect plan around an effect as large as the estimate from the posted data. Rows using 75% or 50% plan around smaller effects. Smaller target effects generally require more participants to distinguish them from random variation at the same significance level and power target. Thus, the displayed participant requirements rise as the assumed target effect becomes smaller.

The calculations use observed effects as planning inputs, which may be unstable in a small study. A future study could have a different underlying effect and different variability. The registry’s own original sample-size assumptions and justification are not reported, so these illustrative figures should not be interpreted as the basis on which this trial was designed.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, Δ is the difference in means to detect and σ is the common standard deviation.

Worked calculation for this trial

1. Observed difference Δ = 0.024; pooled standard deviation σ = 0.030.
2. n = 2 × (1.96 + 0.842)² × 0.030² / 0.024² = 23.8, rounded up to 24 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
delta <- 0.024                      # observed difference in means
sigma <- 0.0295554011837166                     # pooled standard deviation
n_per_arm <- function(power, frac = 1) {
  ceiling(round(2 * (z_a + qnorm(power))^2 * sigma^2 / (delta * frac)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (t-based, so very slightly larger)
cat("power.t.test:", round(power.t.test(delta = delta, sd = sigma, power = 0.80)$n, 1), "\n")

# Simulation: exact two-sample t-test using simulated means and variances
set.seed(1925989)
n    <- n_per_arm(0.80)
reps <- 20000
m_diff <- rnorm(reps, delta, sigma * sqrt(2 / n))             # difference in sample means
s2     <- sigma^2 * rchisq(reps, 2 * (n - 1)) / (2 * (n - 1))   # pooled sample variance
tstat  <- m_diff / sqrt(s2 * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(tstat) > qt(0.975, 2 * (n - 1))), reps))

Output from running the code

100% of observed effect: n per arm = 24 (80% power), 32 (90% power)
 75% of observed effect: n per arm = 43 (80% power), 57 (90% power)
 50% of observed effect: n per arm = 96 (80% power), 128 (90% power)
power.t.test: 24.8 
Simulated power at n = 24 per arm: 0.784 (20000 replicates)

The simulation draws many sets of group means and variances at the planned sample size and counts how often the two-sample t-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Multiplicity, Missing Data and Analysis Populations

The registry lists a primary endpoint and multiple secondary outcomes, but it does not report how multiplicity was handled. A multiplicity strategy might specify a hierarchy for testing outcomes or adjust the interpretation of repeated tests. Without such information, nominal p-values from separate secondary comparisons should not be read as if each were the only planned test. The registry record also does not report whether the primary endpoint was protected by a prespecified testing sequence or whether exploratory analyses were distinguished from confirmatory ones.

No sponsor-reported analyses are listed for the primary or numeric secondary comparisons discussed in this analysis. The available comparisons are unadjusted recalculations from the posted group summaries. The record does not report whether analyses accounted for the within-participant structure of the crossover design, treatment period, treatment sequence, or other covariates. It also does not provide the original statistical analysis plan or the method used to calculate the reported summary results.

For the primary endpoint, the analysis population is described as randomized participants with type 1 diabetes who received at least one dose of study drug. The record says two participants’ data could not be used, one because of uncontrolled blood glucose and one because of an increase in thyroid medication. It does not explain whether those data were excluded from all analyses, whether other observations were missing, or how missing measurements were handled. Missing-data assumptions and sensitivity analyses are not reported in the registry record.

The posted group counts vary across outcomes, but the record does not explain each difference in available observations. It is therefore not possible to determine from the registry alone whether outcome-specific exclusions, measurement failures, or other reasons account for those counts. Details of adherence, protocol deviations, and analysis populations beyond the primary description are not reported. These omissions limit evaluation of selection bias and the robustness of the recalculated comparisons.

Interim analyses, stopping rules, and the original sample-size justification are not reported in the registry record. Their absence from the record should not be taken as evidence that they did or did not occur; it means the posted information does not allow those aspects of the study’s statistical conduct to be assessed.

15. Safety Results

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

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 5% reporting threshold.

Most frequent non-serious adverse event terms

The posted serious adverse event figures show zero affected participants in the insulin peglispro group, zero in the insulin glargine group, and zero in the untreated control group. The corresponding at-risk counts were 13, 15, and 12. These are registry counts for the safety reporting groups; they should not be assumed to match the efficacy analysis populations, which are described separately.

No serious adverse event terms are listed. Death counts are not reported in the registry record. The record also lists other, non-serious adverse events affecting 5 participants in the insulin peglispro group, 3 in the insulin glargine group, and 1 in the control group. Those figures are distinct from serious adverse events and do not provide details about event type, severity, duration, or relationship beyond the recorded counts.

Zero reported serious events in these groups is a description of the posted safety data, not proof that serious harms cannot occur. The study was limited in size and duration, and the registry counts alone cannot characterize uncommon events or longer-term safety. No conclusion about overall safety should be drawn from these counts without fuller event details and context.

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

The posted primary result describes an observed difference in overnight mean RQ between insulin peglispro and insulin glargine in participants with type 1 diabetes. The mean was higher in the glargine group, and the simple recalculated interval is just above zero at its lower boundary. This is a short-term physiological finding under a particular measurement protocol; it is not a direct measure of clinical benefit or harm.

The result does not establish that either insulin regimen is superior for health outcomes. RQ is an indirect metabolic measure, and the registry does not connect the observed contrast to symptoms, complications, quality of life, or longer-term outcomes. It also does not establish whether the difference is clinically important. A p-value near a conventional threshold cannot answer those questions, and the size and precision of the estimate should be considered alongside the limitations of the available analysis.

The study’s crossover design could support an efficient comparison because participants receive both treatments, but interpretation depends on accounting for the repeated measurements and treatment periods. The registry does not report the sponsor’s analysis method, and participant-level paired information is not posted. The unadjusted recalculation therefore cannot substitute for an analysis that fully addresses the study design.

The secondary outcomes provide some additional metabolic measurements, but their mixed pattern and unadjusted status do not provide definitive corroboration. Nor do the posted serious adverse event counts establish that rare or longer-term risks are absent. The findings should be understood within the enrolled population, study duration, measurement window, and information available in the registry record.

In short, the registry supports a cautious description of the observed means and their uncertainty. It does not support conclusions about treatment choice, long-term outcomes, or clinical effectiveness. Those questions are not answered by the reported physiological summaries.

17. Why This Trial Matters Statistically

This trial is statistically informative because it illustrates the distinction between a numerical contrast and a complete analysis of a clinical study. The primary means and standard deviations permit a straightforward unadjusted comparison, but the crossover design creates within-participant dependence that cannot be recovered from the separate group summaries. The registry’s lack of a reported sponsor analysis makes that distinction especially important.

The result also shows why effect estimates and intervals matter alongside p-values. The primary estimate points in one direction, but its lower confidence limit is close to zero. Reporting the estimate and its uncertainty makes the scale and precision of the observed contrast visible, while the p-value alone would conceal that range. At the same time, statistical evidence about a physiological measurement does not establish clinical importance.

Multiplicity is another relevant issue. Several related outcomes were considered, but the registry does not report an adjustment plan or hierarchy. An apparently notable secondary comparison has a different meaning when it is one among several outcomes than when it is the sole prespecified test. The posted results therefore offer an opportunity to distinguish exploratory patterns from confirmatory evidence.

Finally, the planning illustrations show how sample-size requirements depend on the assumed effect and the desired power. They are not the trial’s original design rationale, and they are not observed power. Keeping these calculations separate from the observed results helps prevent a planning exercise from being mistaken for evidence about what this completed study could detect.

18. 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.

Several limitations are specific to interpreting this study’s posted results. The study enrolled participants with type 1 diabetes and untreated healthy participants, but the primary question concerns the insulin comparison in the diabetes group. The healthy control arm is not a treated comparator for that question. Conclusions from the metabolic measurements should therefore remain tied to the population and conditions described in the record.

The primary endpoint was measured during a defined overnight window on Day 30. The results do not establish whether the same pattern would be observed at other times, after longer exposure, or on clinical outcomes. The registry describes a two-period crossover and treatment sequences, but does not report the washout details, period effects, or the analysis used to account for repeated observations. Separate group summaries do not reveal the within-participant variation needed to assess a paired comparison independently.

Data from two participants could not be used for the primary endpoint for the stated reasons, but the record does not give a complete account of missing observations across all outcomes. Outcome-specific counts differ, and the reasons are not reported. The registry also does not describe sensitivity analyses, protocol deviations, or the handling of missing data.

The comparison results discussed are unadjusted recalculations, while sponsor-reported analyses are not listed. The confidence-interval method and confidence level are not reported. Multiplicity procedures and the original sample-size rationale are also absent from the registry record. These limitations constrain the strength of inference and make the results more useful as descriptive evidence than as a definitive treatment comparison.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: A Study of How Insulin Peglispro (LY2605541) and Insulin Glargine Affect the Breakdown of Fat and Sugar in Type 1 Diabetics — NCT01925989.
• Registry record last updated 2019-03-01; status last verified 2018-10.
• Results first posted 2019-03-01.
• 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.

22. Record Summary

NCT01925989 is a completed, randomized, open-label, Phase 1 crossover study of insulin peglispro and insulin glargine in participants with type 1 diabetes. The registry describes two treatment periods, once-daily basal insulin for 28 to 35 days per period, and continued mealtime insulin. Untreated healthy participants also enrolled.

The primary endpoint was sleep RQ on Day 30 during 0000 to 0600 hours. The posted means were 0.822 for insulin peglispro and 0.846 for insulin glargine. The unadjusted recalculated mean difference was 0.024, with a confidence interval from 0.001 to 0.047 and p = 0.045. No sponsor-reported analyses are listed.

Numeric secondary results are available for selected metabolic measures, and the registry reports zero serious adverse event counts in each listed safety group. The original statistical plan, multiplicity handling, and sample-size justification are not reported in the registry record.