Botanical Oil Supplementation in Diabetic and Metabolic Syndrome Subjects: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Botanical Oil Supplementation in Diabetic and Metabolic Syndrome Subjects (NCT01145066), a randomized phase 2 study. The primary outcome is Fasting Insulin.

2. Clinical Question

This distinction is central to interpreting the record. The comparisons use the borage and echium combination as the reference and report differences in baseline fasting insulin for the fish oil and corn oil groups. They are not reported treatment effects, because the endpoint timeframe is baseline and no follow-up primary result is posted in the registry record for this analysis.

The registry record lists serum fatty acids, pro- and anti-inflammatory cytokines, fasting glucose, and hemoglobin levels among secondary outcomes. These measures address aspects of metabolism, fatty acid status, or inflammatory biology, but the posted secondary values described in detail are also labeled baseline. Consequently, the clinical question about the effect of supplementation on inflammation remains distinct from the descriptive comparisons of participants’ starting measurements.

3. Trial Design

The registry record describes a randomized, single-masked, parallel-group phase 2 trial. It lists an enrollment of 80 and a completed status. Wake Forest University Health Sciences is the sponsor. The recorded study start is 2009-05, and primary completion is listed as 2011-03.

Three intervention groups are named: a borage and echium oil combination, fish oil, and corn oil. The combination is specified as containing 0.85g/day SDA and 1.7 g/day GLA; the fish oil is described as Croda 18:12 fish oil. No formulation details are given for corn oil. The registry record does not report who was masked, the treatment duration, or details of the randomization procedure beyond identifying allocation as randomized.

The posted primary outcome is fasting insulin, measured in μIU/mL as a mean with standard deviation. Its timeframe is baseline. The group summaries list 22.0 participants for the borage and echium combination, 16.0 for fish oil, and 21.0 for corn oil. These outcome-specific counts differ from the enrollment figure and are not a basis for inferring why participants are absent from a particular result.

The record lists 21 outcome measures and 15 posted secondary results. It does not include reported analyses for the posted comparisons in the registry analysis fields. The displayed pairwise estimates and planning illustrations are therefore best read as unadjusted calculations from the posted summary statistics, rather than as a complete account of the trial’s prespecified statistical analysis. The trial’s original analysis plan, masking details, and rules for handling missing observations are not reported in the registry record.

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.

Before assignment: 80 participants signed consent, only 71 were randomized to an arm/group

Overall Study

Reasons for not completing this period:

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 Fasting Insulin, measured over baseline.

Fasting insulin is the concentration of insulin measured in blood after fasting, expressed in μIU/mL. In general, fasting insulin is used in metabolic research as a measure of insulin present in the circulation under fasting conditions. It can help characterize metabolic status, but a single measurement does not by itself describe a change over time or establish a treatment effect.

The registry record identifies fasting insulin as the primary outcome and specifies a mean and standard deviation. The stated timeframe is baseline. The posted values are 15.4 for the borage and echium oil combination, 19.1 for fish oil, and 20.2 for corn oil. These are group means at baseline, not post-treatment values or changes from baseline.

The endpoint therefore provides a starting-point comparison among the groups in this record. It is not the inflammation outcome emphasized in the registry’s brief summary, and no follow-up fasting-insulin result is included in the registry entries. Whether fasting insulin was also assessed later, or how it was intended to contribute to the study’s broader objectives, is not reported in the registry record.

Standard deviations describe spread among individual measurements around each group mean. They help indicate how much participants’ values varied within a group, but they are not confidence intervals for the mean and do not show the precision of a between-group effect by themselves. The pairwise comparisons and their confidence intervals provide a separate description of the estimated differences in baseline means.

7. Results Posted in the Registry

• Fish Oil: mean 19.10 versus 15.40 on control; difference 3.70 (95% CI 1.34 to 6.06), standardized effect d = 1.13; z-test p = 0.002.
• Corn Oil: mean 20.20 versus 15.40 on control; difference 4.80 (95% CI 3.20 to 6.40), standardized effect d = 1.81; z-test p = <0.001.

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.

Fasting Glucose

• 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:
  • Fish Oil: mean 133.50 versus 125.90 on control; difference 7.60 (95% CI -1.13 to 16.33), standardized effect d = 0.63; z-test p = 0.088.
  • Corn Oil: mean 130.00 versus 125.90 on control; difference 4.10 (95% CI -1.08 to 9.28), standardized effect d = 0.48; z-test p = 0.121.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Fish Oil 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 Corn Oil and the control. The interval gives the range of effects that remain compatible with the data.

Hemoglobin Levels

• 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:
  • Fish Oil: mean 7.42 versus 6.81 on control; difference 0.61 (95% CI 0.41 to 0.81), standardized effect d = 1.95; z-test p = <0.001.
  • Corn Oil: mean 7.15 versus 6.81 on control; difference 0.34 (95% CI 0.15 to 0.53), standardized effect d = 1.06; z-test p = <0.001.
• Interpretation:
  • The 95% confidence interval excludes no difference, so the mean is statistically higher for Fish Oil than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.
  • The 95% confidence interval excludes no difference, so the mean is statistically higher for Corn Oil than for the control. This is an unadjusted, secondary comparison, so it is suggestive rather than confirmatory.

The secondary results with detailed values concern fasting glucose and hemoglobin levels, both measured at baseline. The glucose comparisons are less conclusive in the unadjusted summaries, with intervals spanning zero in both comparisons. The hemoglobin-level comparisons have intervals above zero in both comparisons. This overall pattern shows that the baseline secondary measures do not all behave alike; it does not establish a biological response to supplementation.

The secondary outcomes listed in the registry include serum fatty acids, pro- and anti-inflammatory cytokines, fasting glucose, and hemoglobin levels. In particular, the cytokine outcomes are relevant to the inflammation-focused objective described in the brief summary. The detailed results available for interpretation do not include cytokine estimates, so the baseline fasting-insulin comparisons cannot stand in for those results. The record lists 15 secondary results as posted, but the analysis details for the secondary comparisons are not reported.

These secondary measures offer context about the groups and the broader outcomes of interest. Because the displayed values are baseline, their relationship to the primary result is descriptive: they show other starting measurements rather than corroborating a treatment effect on fasting insulin. The registry record does not report a planned hierarchy that would establish how secondary endpoints were intended to support or qualify the primary endpoint.

Unadjusted secondary findings carry less weight when many outcomes and pairwise comparisons are considered. In general, examining more comparisons increases the chance that some may look noteworthy by chance alone. Without a reported multiplicity strategy, prespecified outcome hierarchy, or analysis plan, individual secondary p-values should be interpreted cautiously. They can inform what deserves further study, but they should not be treated as definitive evidence of an effect or mechanism.

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

• Serum Fatty Acids
• Serum Fatty Acids
• Pro and Anti-inflammatory Cytokines
• Pro and Anti-inflammatory Cytokines
• Fasting Glucose
• Fasting Glucose
• Hemoglobin Levels
• Hemoglobin Levels

9. Statistical Interpretation

10. Statistical Methods Explained

The posted primary and secondary measures are continuous outcomes summarized by group means and standard deviations. A mean gives the arithmetic average of the measured values in a group. A standard deviation describes the spread of those values around the mean. Neither summary alone describes how precisely a difference between groups has been estimated; that is the role of the confidence interval for the comparison.

What does a mean difference represent?

For each comparison, the borage and echium combination is the reference group. The reported mean difference is the comparator group’s mean minus the reference group’s mean. Thus, a positive estimate means that the recorded baseline mean is higher in the fish oil or corn oil group than in the combination group. This direction is descriptive. Because the outcome timeframe is baseline, it is not the direction of a treatment-induced change.

The fish oil comparison for fasting insulin is 3.7 μIU/mL, and the corn oil comparison is 4.8 μIU/mL. The corresponding estimates for fasting glucose are 7.6 mg/dL and 4.1 mg/dL. For hemoglobin levels, the estimates are 0.61 mg/dL and 0.34 mg/dL. These comparisons are calculated from the posted group means and standard deviations and are unadjusted. They do not account for other participant characteristics or for multiple comparisons.

How should a confidence interval be read?

A confidence interval describes a range of estimates compatible with the observed data under the calculation’s assumptions. A narrower interval indicates greater precision than a wider interval, while an interval spanning zero is compatible with no difference in the direction represented by the estimate. An interval that does not span zero indicates that zero is outside that particular interval; it does not demonstrate causation or clinical importance. The confidence level for these intervals is not reported in the registry record, so it should not be assumed from the interval endpoints alone.

The fasting-insulin intervals are 1.342 to 6.058 for fish oil and 3.196 to 6.404 for corn oil. Both exclude zero. For fasting glucose, the intervals are -1.133 to 16.33 and -1.084 to 9.284, each spanning zero. For hemoglobin levels, the intervals are 0.405 to 0.815 and 0.148 to 0.532, neither spanning zero. These patterns describe the unadjusted baseline comparisons; they do not turn the measurements into post-treatment results.

What do the p-values add?

A p-value measures how unusual data at least as inconsistent with a specified null model would be if that model and the test assumptions were correct. It is not the probability that the null hypothesis is true, and it does not measure the size or importance of a difference. The displayed p-values are unadjusted: fasting insulin has p = 0.002 for fish oil and p < 0.001 for corn oil; fasting glucose has p = 0.088 and p = 0.121; hemoglobin levels have p < 0.001 for both comparisons.

Because these p-values are unadjusted, they do not account for the number of outcomes or comparisons considered. The registry record lists 21 outcome measures, and the registry entries include multiple comparisons across the primary and secondary results. Testing several outcomes can make apparently notable findings more likely to arise by chance. No multiplicity adjustment or prespecified hierarchy is reported in the registry record.

Why distinguish registry analyses from recalculations?

The posted pairwise estimates are simple unadjusted recalculations from summary statistics. The record’s reported-analysis information is empty, and the number of reported analyses is 0. As a result, the recalculated comparisons should not be presented as the trial’s adjusted or prespecified inferential analysis. A trial analysis could use a model, baseline adjustment, or a different analysis population, but whether any such approach was used is not reported in the registry record.

These calculations also rely on summary statistics and do not incorporate participant-level data. They cannot account for the actual distribution of measurements, covariate relationships, protocol deviations, or missing-data mechanisms. General statistical methods can explain what such comparisons mean, but the exact trial analysis plan and its assumptions are not reported in the registry record.

What is the role of standard deviation?

Standard deviations vary across groups and outcomes. In general, greater variability makes a mean difference harder to estimate precisely when other features are comparable, because individual measurements provide a less consistent signal about the group average. Standard deviations are not the same as standard errors: the standard error describes uncertainty in an estimated mean, whereas the standard deviation describes participant-level variation. The posted statistics distinguish these concepts in the calculations but do not provide participant-level information.

Overall, the statistical summaries support a careful description of baseline group differences. They do not provide a substitute for the trial’s own treatment-effect analyses, whose details are not reported in the registry record, and they do not answer whether supplementation affected fasting insulin, inflammation, or the other outcomes over time.

11. Confidence Intervals and Precision

Precision concerns how narrowly the data locate an estimated difference. The primary fasting-insulin comparison with fish oil is 3.7 μIU/mL, with a confidence interval from 1.342 to 6.058. The comparison with corn oil is 4.8 μIU/mL, with a confidence interval from 3.196 to 6.404. Both intervals exclude zero, but their widths still allow a range of plausible baseline differences rather than identifying one exact value.

The point estimates should therefore be read alongside their intervals. An interval is not a guarantee that the true value lies within its endpoints, and it does not express clinical importance. The confidence level is not reported in the registry record. The available summary statistics also cannot show how sensitive the estimates would be to a different analysis model or adjustment for baseline characteristics.

Precision differs across the secondary comparisons. The fasting-glucose intervals are wider relative to their estimates and include zero. The hemoglobin-level intervals are narrower and exclude zero. This difference in precision does not by itself establish which outcome is more important, nor does it remove the limitations of unadjusted testing across multiple outcomes.

The group counts attached to the primary results are 22.0, 16.0, and 21.0, respectively. The reported standard deviations also differ across groups. Both sample information and variability contribute to uncertainty in a mean comparison. Since only summary statistics are available and the registry does not report its own analysis details, the confidence intervals should be treated as descriptive unadjusted estimates rather than as a complete account of inferential precision under the trial’s intended analysis.

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 illustrations give participants per arm, not total events. They use 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 assumptions for an illustration based on the observed difference; the trial’s original design assumptions are not reported in the registry record.

The 80% power column gives the illustrated number of participants per arm for a design intended to have an 80% chance of detecting the specified effect under those assumptions. The 90% power column gives the corresponding number per arm for a design intended to have a 90% chance. These are prospective planning quantities, not measures of what the completed study achieved. The figures are not observed power.

Each comparison has separate planning values. For fasting insulin, planning for 75% of the observed effect gives 22 participants per arm for 80% power and 30 participants per arm for 90% power in the fish oil comparison; the corresponding corn oil illustration is 9 and 12 participants per arm. For 50% of the observed effect, the figures are larger: 50 and 67 for fish oil, and 20 and 26 for corn oil. The full set of scenarios appears in the planning display.

The illustrations are conditional on the observed effect and the stated calculation assumptions. They do not account for adjustments, missing data, multiplicity, or other trial-specific features unless these are built into the stated assumptions. The trial’s original sample-size justification and design assumptions are not reported in the registry record. Smaller effects require more participants to detect under otherwise comparable assumptions, so a design sized around a large observed difference may not be adequate for a more modest effect. These calculations are aids for planning, not evidence that the original trial was designed around those figures.

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 = 3.700; pooled standard deviation σ = 3.282.
2. n = 2 × (1.96 + 0.842)² × 3.282² / 3.700² = 12.4, rounded up to 13 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 <- 3.7                      # observed difference in means
sigma <- 3.2818947779192                     # 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(1145066)
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 = 13 (80% power), 17 (90% power)
 75% of observed effect: n per arm = 22 (80% power), 30 (90% power)
 50% of observed effect: n per arm = 50 (80% power), 67 (90% power)
power.t.test: 13.4 
Simulated power at n = 13 per arm: 0.783 (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 record lists 21 outcome measures, and the displayed results include comparisons for a primary outcome and secondary outcomes. Each outcome can generate more than one comparison. When many statistical tests are considered, the chance of at least one apparently unusual result can increase, even when there is no underlying effect. This general issue is known as multiplicity.

The reported p-values for the displayed comparisons are unadjusted. The registry record does not report a multiplicity correction, a prespecified testing hierarchy, or a strategy for deciding which outcomes were confirmatory and which were exploratory. Without those details, the p-values should not be interpreted as if they were adjusted for the full set of outcomes and comparisons. This is particularly important for secondary results, which may be useful for generating hypotheses but are less persuasive as stand-alone evidence.

The registry analysis fields list no reported analyses, and the number of reported analyses is 0. The trial’s statistical analysis plan, including any planned covariate adjustment, model specification, or treatment of repeated measurements, is not reported in the registry record. Consequently, the recalculated comparisons cannot establish whether they match the investigators’ planned analysis or whether the trial used additional analyses not captured in the record.

Missing-data methods and analysis populations are also not reported in the registry record. The counts shown for particular outcomes indicate the participants represented in those summaries, but they do not explain why those counts differ from the enrollment figure or whether missingness was related to participant characteristics or outcomes. The record does not specify whether analyses were based on all randomized participants, participants with available measurements, or another population.

In general, missing observations can affect estimates if the likelihood of being measured is related to participant status or outcome values. Without information about completeness, reasons for missingness, and the methods used to address it, the direction or size of any such influence cannot be assessed. These reporting gaps limit evaluation of how robust the posted baseline comparisons are and how a later treatment-effect analysis, if available, should be interpreted.

15. 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 1% reporting threshold.

Most frequent non-serious adverse event terms

The adverse-event summaries distinguish serious events from other events and give the number at risk for each arm. For serious adverse events, the registry record lists 0 affected among 26 at risk in the borage and echium oil combination group, 0 among 20 at risk in the fish oil group, and 0 among 25 at risk in the corn oil group. Deaths are listed as 0 in each group.

The other-event summaries list 4 affected among 26 at risk for the combination, 5 among 20 at risk for fish oil, and 6 among 25 at risk for corn oil. These figures are counts, not comparative risk estimates, and the record does not provide event descriptions in the registry record. No serious-event terms are listed.

Zero recorded serious events in these summaries does not establish that the interventions are free of risk. The registry record does not report the observation period for safety, event-collection methods, or whether all adverse events were captured. The groups and event counts should therefore be read as the recorded safety summaries, not as a definitive comparison of safety between oils.

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

The posted primary results describe baseline fasting-insulin means in three randomized groups. The combination group’s mean is lower than the fish oil and corn oil group means at baseline, and the unadjusted pairwise intervals exclude zero. That is a statistical description of the starting measurements, not evidence that the combination changed insulin or that either comparator caused a change.

The registry’s brief summary frames the study around inflammation, while the primary outcome posted in the record is fasting insulin at baseline. The detailed information available does not provide a post-treatment primary result or detailed cytokine estimates. Thus, the key question of whether the oils altered inflammation is not answered by these baseline comparisons. Nor do the baseline secondary results establish changes in glucose or hemoglobin levels.

A small p-value is not a measure of clinical importance, and a confidence interval does not resolve whether a difference matters to participants. The registry record does not state a threshold for a meaningful fasting-insulin difference. It also does not report the original analysis plan, adjustments, or missing-data approach, all of which would be relevant to evaluating treatment comparisons.

Randomized assignment is a design feature intended to reduce systematic differences between groups. It does not mean that baseline measurements must be identical, and the observed starting differences remain important context for any later analysis. The posted summaries do not tell whether an adjusted analysis accounted for those differences. No conclusion about a treatment’s benefit, lack of benefit, or suitability for an individual follows from these results.

The record is most informative as a description of the study design, intervention groups, baseline measurements, and recorded adverse-event counts. It also identifies the limits of what can be inferred: treatment effects on the stated inflammation objective, changes over time, and the trial’s prespecified inferential conclusions are not reported in the registry record.

17. Why This Trial Matters Statistically

This trial illustrates why the measurement timeframe is as important as the endpoint name. A result labeled fasting insulin might sound like an outcome comparison, but the registry specifies baseline. The posted comparisons therefore characterize group starting values. Without follow-up or change-from-baseline results, interpreting those estimates as effects of supplementation would confuse a baseline description with a treatment effect.

The record also highlights a gap between a broad study objective and the outcome available for detailed interpretation. The brief summary focuses on inflammation, and cytokines are among the named secondary outcomes, but the primary result is baseline fasting insulin and the detailed cytokine estimates are not available in the registry record used for this analysis. Statistical interpretation must follow the measurement and timeframe actually reported rather than infer an answer to the broader objective.

Reporting unadjusted calculations alongside their uncertainty can make the size and direction of baseline differences easier to assess. At the same time, the absence of reported trial analyses, multiplicity methods, and missing-data procedures limits how far the comparisons can be taken. The baseline differences are a reminder that randomization does not guarantee identical observed starting values, particularly in groups of the sizes represented in the outcome summaries.

The planning illustrations also show why sample-size figures depend on the effect chosen for planning. The required number per arm grows as the target effect becomes smaller, under the stated assumptions. Such illustrations can inform future study planning, but they do not reconstruct the trial’s original justification or establish its achieved power.

For clinical researchers, the statistical lesson is to keep endpoint, timeframe, comparison, uncertainty, and analysis status together. A result can be statistically distinct at baseline without demonstrating a treatment effect, and an unadjusted secondary finding can remain exploratory when multiplicity procedures are not reported.

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.

The most important trial-specific limitation is that the posted primary endpoint is fasting insulin at baseline. The available results therefore do not provide a treatment-period estimate or a change from baseline. The inflammation objective described in the registry summary cannot be assessed from the detailed primary results, and cytokine values are not included among the registry entries available for interpretation.

The pairwise differences are unadjusted recalculations from group summary statistics. The registry does not report its own analyses, the analysis plan, a prespecified multiplicity procedure, or the analysis population. It is therefore not possible to determine whether the displayed comparisons reproduce an investigator-planned analysis or whether adjustment for baseline variables was used elsewhere.

Outcome-specific participant counts are reported, but the reasons for their differences from the enrollment figure are not. Missing-data methods, follow-up completeness, and safety observation duration are not reported in the registry record. These omissions limit interpretation of both efficacy and adverse-event summaries.

The record identifies the study as randomized, single-masked, and parallel-group, but does not report who was masked, the randomization procedure, or treatment duration. No formulation details are listed for corn oil. These gaps restrict assessment of implementation and comparability beyond the information explicitly recorded.

Finally, baseline comparisons and safety counts are not substitutes for a complete report of treatment effects, clinically meaningful thresholds, and the prespecified statistical plan. The posted findings are informative about the recorded baseline summaries, but they do not provide enough detail to evaluate the trial’s primary objective as a treatment-effect question.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Botanical Oil Supplementation in Diabetic and Metabolic Syndrome Subjects — NCT01145066.
• Registry record last updated 2019-07-18; status last verified 2018-12.
• Results first posted 2018-12-26.
• 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

The registry record describes a completed randomized, single-masked, parallel-group phase 2 study sponsored by Wake Forest University Health Sciences. It lists an enrollment of 80 and a study population with diabetes or metabolic syndrome. The intervention groups are borage and echium oil combination, fish oil, and corn oil.

The primary outcome is fasting insulin in μIU/mL, reported as a mean and standard deviation at baseline. The posted unadjusted pairwise comparisons show higher baseline means in the fish oil and corn oil groups than in the combination group. These are not treatment-effect estimates. Detailed secondary baseline comparisons are available for fasting glucose and hemoglobin levels; the registry also names fatty-acid and cytokine outcomes.

The registry analysis fields contain no reported analyses, and the record does not report the original statistical plan, multiplicity strategy, or missing-data methods. Serious adverse events and deaths are listed as 0 in each group’s safety summary. The results should be interpreted as descriptive baseline and safety information, not as evidence about changes caused by supplementation.