This page describes the registered study objective, design, endpoint, and the statistical framework appropriate for evaluating a genotype-diet interaction. The ClinicalTrials.gov record contains no posted statistical analyses or numerical efficacy results.
1. Trial at a Glance
A to Z was a study follow-up focused on collection of DNA data from buccal swabs. The registered primary objective was to identify genotype-diet interaction on weight, body fat, and BMI over a 1-year time frame.
| Feature | A to Z |
|---|---|
| Acronym | A to Z |
| ClinicalTrials.gov identifier | NCT00866151 |
| Study title | A to Z Study Follow-up: Collection of DNA Data From Buccal Swabs |
| Status | Unknown |
| Start date | 2008-08 |
| Primary completion date | 2009-06 |
| Enrollment | 150 |
| Number of arms | 1 |
| Lead sponsor | Interleukin Genetics, Inc. |
| Sponsor type | Industry |
| Conditions | Weight Management; Diet Conditions; Dietary Recommendations; Overweight Management; Obesity |
2. Clinical Question
The statistical question registered for the study was whether genetic information could be evaluated together with dietary exposure to identify a genotype-diet interaction associated with weight, body fat, and BMI over 1 year.
Population
The registry describes participants in the context of weight management, diet conditions, dietary recommendations, overweight management, and obesity. Enrollment was 150.
Study focus
The study title identifies collection of DNA data from buccal swabs as the follow-up activity.
Comparator
The registry records 1 study arm. It does not describe a separate randomized comparator arm.
Primary question
Does the relationship between dietary exposure and weight, body fat, or BMI differ according to genotype?
3. Trial Design
The registry describes a follow-up study with 150 participants and 1 arm. Its title identifies collection of DNA data from buccal swabs, while the registered primary objective concerns genotype-diet interaction on weight, body fat, and BMI.
A to Z follow-up
- Follow-up study
- Collection of DNA data from buccal swabs
- Single registered study arm
- Primary objective centered on genotype-diet interaction
- Weight, body fat, and BMI are the registered outcome domains
- Primary endpoint time frame: 1 year
Interaction-focused analysis
- Genotype functions as a potential effect modifier
- Diet represents the exposure whose relationship with outcome may vary by genotype
- Weight, body fat, and BMI are continuous outcome measures
- The central statistical term is the genotype-by-diet interaction
- The registry does not post numerical statistical results
4. Study Timeline
Study start
The registry lists 2008-08 as the study start date.
Primary completion
The registry lists 2009-06 as the primary completion date.
Primary endpoint timeframe
The registered primary objective is evaluated over a 1-year time frame.
5. Primary Endpoint
| Endpoint | Registry definition | Time frame |
|---|---|---|
| Primary objective | The PRIMARY objective of this study is to identify genotype-diet interaction on weight, body fat, and BMI. | 1 year |
This endpoint is different from a conventional comparison of mean weight change between two randomized treatment groups. The central object is an interaction: whether the relationship between diet and an outcome changes according to genotype.
Here, Y represents an outcome such as weight, body fat, or BMI; G represents genotype; D represents a dietary exposure; and the coefficient β3 represents the genotype-diet interaction.
6. Statistical Methodology
Interaction rather than a simple main effect
The defining statistical feature of the registered objective is the interaction between genotype and diet. A main-effect analysis asks whether genotype is associated with the outcome or whether diet is associated with the outcome. An interaction analysis asks a different question: does the diet-outcome relationship differ across genotype categories?
For example, if genotype is represented by categories and diet by a continuous exposure, the analysis can estimate separate diet-outcome slopes for the genotype groups. The interaction term tests whether those slopes differ.
Continuous outcomes
Weight, body fat, and BMI are continuous measurements. A general linear-model framework is therefore a natural statistical approach when the outcome is measured on an approximately continuous scale and model assumptions are reasonable.
A nonzero interaction coefficient indicates that the estimated association between diet and the outcome changes according to genotype. The direction and magnitude of that difference must be interpreted in the scale used for the model.
Adjustment for baseline measurements
When a continuous outcome is measured at follow-up and its baseline value is available, an analysis can use the baseline measurement as a covariate. An ANCOVA-style model can improve precision by accounting for baseline outcome differences while estimating the association of interest at follow-up.
The registry does not provide baseline outcome measurements or specify a baseline-adjusted statistical model. Consequently, the appropriate adjustment strategy depends on the detailed study protocol and measurement structure.
Repeated measurements
If weight, body fat, or BMI were measured at multiple time points, a longitudinal model could account for within-participant correlation and evaluate whether genotype modifies change over time. A mixed-effects model is one common framework for this situation.
Cross-sectional interaction
A model at a single follow-up time estimates whether the diet-outcome association differs by genotype at that time point.
Longitudinal interaction
A repeated-measures model can evaluate whether outcome trajectories over time differ according to genotype and dietary exposure.
7. Statistical Methods Explained
Why is an interaction term needed?
A genotype main effect cannot answer whether diet works differently across genotypes. The interaction term directly tests whether the association between diet and the outcome changes as genotype changes.
What does a genotype-diet interaction mean?
It means that the estimated relationship between dietary exposure and an outcome such as BMI is not constant across genotype groups. In a regression model, this difference is represented by the coefficient of the genotype-by-diet product term.
Why are weight, body fat, and BMI suitable for regression analysis?
Each is a quantitative outcome. Regression methods can estimate associations while allowing genotype, diet, and potentially other measured characteristics to enter the same statistical model.
Why might ANCOVA be useful?
If both baseline and follow-up measurements are available, adjusting for the baseline value can account for starting differences and often improves precision. The resulting estimate concerns the follow-up outcome conditional on the baseline measurement included in the model.
Why might a mixed-effects model be useful?
If outcomes are measured repeatedly within the same participant, observations from that participant are correlated. Mixed-effects models can represent this within-participant dependence and estimate changes over time without treating repeated observations as independent.
What does statistical significance of an interaction mean?
A statistical test of the interaction evaluates whether the observed difference in diet-outcome associations across genotype groups is compatible with a model in which the interaction is absent. It does not, by itself, establish that one genotype group benefits from a dietary strategy or that the relationship is clinically important.
Why is effect size more informative than a p-value alone?
An interaction p-value addresses evidence against a null interaction. It does not describe the magnitude of the interaction or its practical importance. A useful analysis should report the estimated interaction effect with an appropriate confidence interval and describe the outcome scale clearly.
8. Understanding the Genotype-Diet Interaction
The statistical question can be visualized as a set of diet-outcome relationships. Suppose diet is placed on the horizontal axis and weight change on the vertical axis. Separate fitted lines can represent different genotype groups.
The important point is that an interaction is a comparison of associations. It is not simply the finding that one genotype has a different average weight from another genotype.
9. Planned Analysis
The registry identifies a primary objective of identifying genotype-diet interaction on weight, body fat, and BMI over 1 year. For outcomes of this type, an appropriate statistical analysis would generally model each continuous outcome as a function of genotype, dietary exposure, and their interaction.
| Analysis component | Typical statistical approach | Purpose |
|---|---|---|
| Weight | Linear regression or an appropriate longitudinal model if repeated measurements are available | Estimate the association between diet and weight and test whether it varies by genotype. |
| Body fat | Linear regression or an appropriate longitudinal model if repeated measurements are available | Estimate the association between diet and body-fat outcome and test for genotype modification. |
| BMI | Linear regression or an appropriate longitudinal model if repeated measurements are available | Estimate the association between diet and BMI and test for genotype modification. |
| Genotype-diet interaction | Product term between genotype and dietary exposure | Test whether the diet-outcome association differs according to genotype. |
The registry does not provide the genotype coding scheme, dietary variable definition, baseline measurement schedule, covariate list, model specification, missing-data strategy, or multiplicity procedure. Those details are important because they determine the exact estimand and statistical test.
10. Analysis Populations and Missing Data
The registry reports an enrollment of 150 and 1 study arm, but it does not define a separate statistical analysis population. It also does not provide participant-level completion information or a posted missing-data analysis.
Why the analysis population matters
Interaction analyses can be particularly sensitive to sample size because the relevant evidence comes from variation in both genotype and dietary exposure. If observations are missing disproportionately across genotype or exposure groups, the estimated interaction can be affected.
Missing outcome measurements
For continuous outcomes measured at follow-up, a complete-case analysis is one possible approach, but it relies on assumptions about why observations are missing. Multiple imputation or a longitudinal model may be appropriate in other circumstances, depending on the measurement schedule and missingness mechanism.
The registry does not specify which missing-data method was planned or used.
11. Confounding and Covariate Adjustment
An interaction analysis is not automatically protected from confounding. If dietary exposure is not randomly assigned, factors associated with both diet and the outcome can distort the estimated diet-outcome relationship.
Main effects still matter
A model containing a genotype-diet interaction generally also contains the corresponding genotype and diet main effects. Omitting lower-order terms can make the interaction difficult to interpret.
Covariate selection
Potential confounders should be selected using the study design and subject-matter knowledge rather than solely by automated significance screening.
Continuous exposure
If diet is represented quantitatively, the functional form of its relationship with the outcome should be considered rather than assuming that every unit increase has the same effect.
Genotype coding
The statistical interpretation depends on whether genotype is coded categorically, additively, dominantly, recessively, or by another prespecified scheme.
12. Multiplicity and Multiple Outcomes
The registered primary objective names three outcome domains: weight, body fat, and BMI. An interaction could potentially be evaluated separately for each outcome.
When multiple outcomes or multiple genotype-diet interactions are tested, the probability of obtaining at least one apparently positive result by chance increases unless the analysis plan accounts for multiplicity.
Possible approaches include prespecifying a primary outcome, controlling a familywise error rate, controlling a false discovery rate, or presenting a clearly labeled exploratory analysis. The appropriate choice depends on the scientific purpose of the study.
The registry does not report a multiplicity adjustment or statistical hierarchy for weight, body fat, and BMI.
13. Statistical Interpretation of an Interaction
An interaction coefficient quantifies how much the estimated diet-outcome association changes with genotype, according to the coding and scale used in the model. It is therefore a measure of effect modification, not simply a comparison of average outcomes between genotype groups.
An interaction does not by itself prove that a particular dietary recommendation is effective for an individual genotype. Statistical interaction describes a relationship in the analyzed population and depends on how genotype, diet, and outcome were measured and modeled.
A confidence interval around an interaction estimate describes statistical uncertainty around the estimated effect modification. A wide interval indicates limited precision, while a narrower interval indicates greater precision under the assumed model.
A p-value evaluates evidence against a specified null hypothesis. It does not tell the reader how large the genotype-dependent difference in the diet-outcome relationship is. Effect estimates and confidence intervals are needed to describe magnitude and precision.
14. Limitations
- No posted numerical results: the registry does not post statistical estimates, confidence intervals, or p-values for the registered primary objective.
- Single-arm design: the registry records 1 study arm, so the study is not described as a conventional randomized treatment-versus-control comparison.
- Interaction complexity: genotype-diet interaction requires sufficient variation and information across both genotype and dietary exposure.
- Unspecified genotype coding: the registry does not specify how genotype would be represented in the statistical model.
- Unspecified dietary exposure: the registry does not provide the quantitative definition or measurement method for diet in the primary endpoint description.
- Unspecified baseline measurements: the registry does not provide baseline values for weight, body fat, or BMI or specify whether baseline adjustment was used.
- Unspecified longitudinal structure: although the primary endpoint has a 1-year timeframe, the registry does not describe the complete schedule of outcome measurements.
- Multiple outcomes: the primary objective includes weight, body fat, and BMI, but the registry does not describe a multiplicity strategy.
- Missing data: the registry does not specify an imputation or missing-data method.
- Limited statistical detail: the registry does not provide a posted statistical analysis plan sufficient to determine the exact fitted model or estimand.
15. Why This Trial Matters Statistically
A to Z illustrates a statistical problem that is different from the standard two-arm clinical-trial comparison. The central question is not simply whether one treatment produces a different average outcome from another. Instead, it concerns whether a dietary relationship with weight-related outcomes varies according to genetic information.
| Concept | How it appears in A to Z |
|---|---|
| Effect modification | The primary objective is explicitly a genotype-diet interaction. |
| Interaction term | The statistical model can represent whether the diet-outcome association changes according to genotype. |
| Continuous outcomes | Weight, body fat, and BMI are quantitative outcome domains. |
| Regression modeling | A regression framework can estimate main effects and genotype-diet interaction simultaneously. |
| Longitudinal analysis | If measurements were collected repeatedly, within-participant correlation would need to be considered. |
| Baseline adjustment | If baseline outcome measurements were available, adjustment could improve precision for follow-up outcomes. |
| Multiplicity | Multiple outcome domains can create multiple statistical tests requiring prespecified interpretation. |
| Missing data | Incomplete outcome or exposure measurements can affect interaction estimates and precision. |
| Genotype coding | The interpretation of the interaction depends on how genetic categories are represented in the model. |
| Nonrandomized exposure | If dietary exposure is not randomly assigned, confounding must be considered when interpreting associations. |
16. A Practical Statistical Workflow
A rigorous analysis of the registered objective would proceed from the study design to the estimand rather than beginning with a p-value.
Step 1: Define the estimand
The analysis should state precisely what dietary exposure is being compared or modeled, how genotype is represented, and which outcome and time point define the primary estimand.
Step 2: Examine the distributions
Weight, body fat, BMI, diet, and genotype variables should be inspected for their distributions, missingness, coding, and plausible ranges before fitting an interaction model.
Step 3: Fit the interaction model
The primary model should contain genotype, diet, and the genotype-by-diet interaction. The interaction coefficient answers the central statistical question.
Step 4: Quantify uncertainty
The analysis should report an effect estimate and confidence interval rather than relying solely on a hypothesis-test p-value.
Step 5: Evaluate robustness
Reasonable sensitivity analyses can examine alternative genotype coding, exposure functional forms, baseline adjustment, and missing-data assumptions when justified by the study design.
17. What the Registry Establishes — and What It Does Not
| Question | Registry information |
|---|---|
| What is the study? | A to Z Study Follow-up: Collection of DNA Data From Buccal Swabs. |
| How many participants were enrolled? | 150. |
| How many arms are registered? | 1. |
| What is the primary objective? | To identify genotype-diet interaction on weight, body fat, and BMI. |
| What is the endpoint timeframe? | 1 year. |
| When did the study start? | 2008-08. |
| When was primary completion recorded? | 2009-06. |
| Are numerical statistical results posted? | No formal statistical analyses were posted to ClinicalTrials.gov. |
| Are endpoint-specific estimates available? | The registry does not provide estimates, confidence intervals, or p-values for the registered primary objective. |
| Is a detailed statistical model specified? | The registry does not provide the detailed model specification. |
18. Sources
- ClinicalTrials.gov: A to Z — NCT00866151.
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Clinical trial statistics become easier to interpret when the study question, estimand, model, uncertainty, and design are considered together.
19. Record Summary
A to Z is a useful statistical case because its registered primary objective centers on genotype-diet interaction rather than a conventional treatment-group comparison. The study enrolled 150 participants in 1 registered arm, collected DNA data from buccal swabs, and specified a 1-year primary endpoint concerning weight, body fat, and BMI. The appropriate analytical framework would focus on interaction modeling, careful definition of genotype and dietary exposure, appropriate handling of continuous outcomes, and explicit reporting of effect estimates and uncertainty.
The key statistical distinction is between asking whether genotype or diet is associated with an outcome and asking whether the diet-outcome relationship itself differs by genotype. That second question requires an interaction term and a clear definition of the underlying variables.