Higher Acetyl-L-Carnitine Intake Predicts Slightly Lower Body Weight for Population

Higher Acetyl-L-Carnitine Intake Predicts Slightly Lower Body Weight for Population

Contents

Variables

A
Acetyl-L-Carnitine 160
A
Body Weight 1140

Categories

A
Treatments 9356
A
Physique 41

Tags

High Confidence
Very Weak Effect Size
Negative Relationship
Population Study
cause image gauge image effect image
Participants reported a 0% average increase in Body Weight following above average Acetyl-L-Carnitine Intake.

Abstract

<h5>Not Enough Shared Data</h5>Please create a study and share it with your friends so we can collect enough data to determine the effect of Acetyl-L-Carnitine on Body Weight. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" onclick="" target="_blank" > Create a Study </a> <h5>Solution: Create a Study</h5>Please create a study and share it with your friends so we can collect enough data to determine the effect of Acetyl-L-Carnitine on Body Weight. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" onclick="" target="_blank" > Create a Study </a>

Body Weight was generally 1% higher than average after a total of 167 milligrams of Acetyl-L-Carnitine over the previous 21 days.

Aggregated data from 3 study participants suggests with a HIGH degree of confidence (p=0.00878, 95% CI -0.944 to 0.609) that Acetyl-L-Carnitine has a weakly negative predictive relationship (R=-0.168) with Body Weight.

The highest quartile of Body Weight measurements were observed following an average 1 milligrams Acetyl-L-Carnitine per day.

The lowest quartile of Body Weight measurements were observed following an average 2680 milligrams of Acetyl-L-Carnitine per day.

After an onset delay of 30 minutes, Body Weight is typically 1% lower than average over the 21 days following around 2680 milligrams of Acetyl-L-Carnitine Acetyl-L-Carnitine.

Keywords: Acetyl-L-Carnitine, Body Weight, N-of-1 trials, real-world evidence, causal inference, observational study

Preliminary: Based on 3 participants. Results may change as more data is collected.

Results

Primary Findings

Analysis of 1,199 paired observations from 3 participants revealed a minimal reduction in Body Weight following above-average Acetyl-L-Carnitine exposure.

0.0%
Change from Baseline
Minimal effect on Body Weight
0.02
Predictor Impact Score
Insufficient evidence for causal relationship

Supporting Statistics

High
Confidence
-0.168
Correlation (r)
p = 0.671
Significance
z = 0.00
Effect Magnitude
φ = 0.24
Temporality

What This Means

When participants had above-average Acetyl-L-Carnitine:

  • Body Weight decreased by 0.0% on average
  • Temporal analysis suggests possible reverse causation—interpret with caution

Interpreting the Predictor Impact Score

The Predictor Impact Score (PIS) integrates multiple Bradford Hill causal criteria into a single metric. Use this guide to interpret the score:

PIS Range Interpretation Recommended Action
≥ 0.5 Strong evidence High priority for RCT validation
0.3 - 0.5 Moderate evidence Consider for experimental investigation
0.1 - 0.3 Weak evidence Monitor for additional data
< 0.1 Insufficient evidence Low priority; may be noise

Note: PIS is a prioritization heuristic, not proof of causation. High scores indicate relationships worth investigating, not confirmed causal effects. With only 3 participants, these scores are preliminary and will become more reliable as additional data is collected.

Optimal Daily Values (Precision Dosing)

Based on the observed relationship, we can estimate the predictor values associated with the best and worst outcomes. These values enable personalized dosing recommendations.

⚠️ Preliminary Data: With 3 participants and 1,199 observations, these optimal values are preliminary estimates. As more data is collected, precision will improve significantly.

167.3 mg
Value Predicting Higher Body Weight
Average Acetyl-L-Carnitine when Body Weight exceeded its mean
135.0 mg
Value Predicting Lower Body Weight
Average Acetyl-L-Carnitine when Body Weight was below its mean

What This Suggests

Body Weight tended to be lowest (best) when Acetyl-L-Carnitine was around 135.0 mg.

Important: These values reflect correlations, not guaranteed causal effects. Individual responses may vary. Use as a starting point for personal experimentation, not as a definitive prescription. Consult healthcare providers before making treatment decisions.

Population Correlation

Acetyl-L-Carnitine Distribution

Body Weight Distribution

Relationship Analysis

Statistical Summary

Relationship Statistics

Property Value
Cause Variable Name Acetyl-L-Carnitine Intake
Effect Variable Name Body Weight
Sinn Predictive Coefficient 0.021732391244214
Confidence Level HIGH
Confidence Interval 0.77673381883262
Forward Pearson Predictive Coefficient -0.1677
Critical T Value 1.646
Total Acetyl-L- Carnitine Intake Over Previous 21 days Before ABOVE Average Body Weight 1 milligrams
Total Acetyl-L- Carnitine Intake Over Previous 21 days Before BELOW Average Body Weight 2680 milligrams
Duration of Action 21 days
Effect Size weakly negative
Number of Paired Measurements 1199
Optimal Pearson Product 0.088401227080569
P Value 0.0087770983965754
Statistical Significance 0.6712
Strength of Relationship 0.77673381883262
Study Type population
Analysis Performed At 2026-01-04
Number of Participants 3

Acetyl-L-Carnitine Info

Property Value
Variable Name Acetyl-L-Carnitine
Aggregation Method SUM
Analysis Performed At 2021-07-05
Duration of Action 21 days
Filling Value 0
Kurtosis 25.578657320011
Mean 7468.9675821429 milligrams
Median 7320.0291321429 milligrams
Minimum Allowed Value 0 milligrams
Number of Aggregate Predictors 0
Number of Aggregate Outcomes 160
Number of Measurements 261
Number of Measurements (including those generated by tagged, joined, or child variables) 1492
Public true
Onset Delay 30 minutes
Standard Deviation 271.6668210555
Unit Milligrams
User Variables 22
UPC 885222361371
Variable Category Treatments
Variable ID 1248
Variance 400888.9173154

Body Weight Info

Property Value
Variable Name Body Weight
Aggregation Method MEAN
Analysis Performed At 2020-09-23
Duration of Action 7 days
Kurtosis 29.271534088526
Maximum Allowed Value 1000 pounds
Mean 168.9619340574 pounds
Median 168.27481272906 pounds
Minimum Allowed Value 0 pounds
Number of Aggregate Predictors 883
Number of Aggregate Outcomes 257
Number of Measurements 108822
Number of Measurements (including those generated by tagged, joined, or child variables) 21092
Public true
Onset Delay 0 seconds
Standard Deviation 8.7190661755282
Unit Pounds
User Variables 417
UPC 875011003902
Variable Category Physique
Variable ID 1486
Variance 594.35417755402

Introduction

Background

Acetyl-L-Carnitine (Treatments) and Body Weight (Physique) are both important factors in understanding human health and well-being. This study investigates the relationship between these two variables using real-world observational data.

Traditional randomized controlled trials (RCTs), while the gold standard for causal inference, are often impractical, expensive, or unethical for studying many health relationships. Aggregated N-of-1 observational studies offer a complementary approach that leverages within-subject comparisons across large populations to identify meaningful patterns.

Research Question

Does Acetyl-L-Carnitine affect Body Weight?

Additionally, we seek to determine:

  1. What is the direction and magnitude of any effect?
  2. How confident can we be in this relationship based on the available data?
  3. What are the optimal levels of Acetyl-L-Carnitine for maximizing Body Weight?

Study Objective

The objective of this study is to determine the nature of the relationship (if any) between Acetyl-L-Carnitine and Body Weight. Additionally, we attempt to determine the Acetyl-L-Carnitine values most likely to produce optimal Body Weight values.

Study Overview

This is a population-level observational study using aggregated N-of-1 methodology. By aggregating individual N-of-1 experiments, we can identify population-level patterns while accounting for the substantial individual variation that exists in most health relationships. Effect sizes are reported as percent change from baseline, enabling intuitive interpretation and comparison across different measures.

Full Methodology: Framework for Real-World Evidence-Based Pharmacovigilance: Aggregated N-of-1 Trials for Quantifying Treatment Effects

Discussion

Interpretation of Findings

Participants experienced a 0.0% reduction in Body Weight following above-average Acetyl-L-Carnitine exposure. The Predictor Impact Score (PIS) of 0.02 indicates insufficient evidence for a causal relationship.

Statistical Significance

Using a two-tailed t-test with alpha = 0.05, it was determined that the change in Body Weight is statistically significant at a 95% confidence interval. The p-value of 0.6712 indicates there is less than a 67.12% probability that this result occurred by chance.

T-Test Details
Observed t-value: 6.291
Critical t-value: 1.646

Since t = 6.29 > 1.65, we reject the null hypothesis.

Biological Plausibility

A plausible bio-chemical mechanism between predictor and outcome is critical for interpreting observational findings. This is where human judgment excels beyond statistical analysis.

Community feedback on the biological plausibility of this relationship is still being collected. Consider the known mechanisms by which Acetyl-L-Carnitine might influence Body Weight.

Bradford Hill Criteria Assessment

The Bradford Hill criteria provide a framework for assessing causality in observational studies. Our methodology operationalizes six of the nine criteria through the Predictor Impact Score (PIS):

Criterion How Addressed Metric
Strength Effect size magnitude Percent change from baseline (Δ%), z-score
Consistency Cross-participant replication Number of users (N), number of pairs (n)
Temporality Predictor precedes outcome Temporality factor (φ), onset delay (δ > 0)
Biological Gradient Dose-response relationship Gradient coefficient (φgradient)
Plausibility Biological mechanism assessment Community votes on mechanism plausibility
Specificity Category appropriateness Interest factor (finterest)

Predictor Impact Score (PIS)

The PIS integrates multiple Bradford Hill criteria into a composite metric quantifying how reliably a predictor affects an outcome. Higher scores indicate stronger evidence:

Population-Level PIS:

$$\text{PIS}_{\text{agg}} = |r_{\text{forward}}| \cdot w \cdot \phi_{\text{users}} \cdot \phi_{\text{pairs}} \cdot \phi_{\text{change}} \cdot \phi_{\text{gradient}}$$

Where φ-factors are saturation functions approaching 1 as evidence accumulates:

  • φusers = 1 - e-N/10 (user saturation)
  • φpairs = 1 - e-n/nsig (pair saturation)
  • φchange = 1 - e-Δspread/Δsig (effect spread saturation)
  • w = weighted average of plausibility votes

Temporality Assessment

We assess evidence for correct causal direction using the temporality factor:

$$\phi_{\text{temporal}} = \frac{|r_{\text{forward}}|}{|r_{\text{forward}}| + |r_{\text{reverse}}|}$$

Values approaching 1 indicate the predictor precedes the outcome (supporting causation); values near 0.5 suggest ambiguous directionality; values near 0 suggest reverse causation or confounding by indication.

Limitations

The accuracy of this study may be limited by the fact that <h5>Not Enough Shared Data</h5>Please create a study and share it with your friends so we can collect enough data to determine the effect of Acetyl-L-Carnitine on Body Weight. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" onclick="" target="_blank" > Create a Study </a> <h5>Solution: Create a Study</h5>Please create a study and share it with your friends so we can collect enough data to determine the effect of Acetyl-L-Carnitine on Body Weight. <a href="https://web.quantimo.do/#/app/study-creation" title="Create a Study" onclick="" target="_blank" > Create a Study </a> . A greater amount of data and more variance in the data would help to resolve this issue.

As with any observational study, correlation does not prove causation. Key limitations include:

  • Unmeasured confounders: Variables not tracked may influence results
  • Self-selection bias: Health trackers may differ from the general population
  • Measurement error: Self-reported data may contain recall bias
  • Confounding by indication: Sicker individuals may use more treatments

However, within-subject comparison and temporal precedence analysis partially mitigate these limitations. If the relationship is merely coincidental, as participants independently modify their Acetyl-L-Carnitine values, the observed strength will decline over time. Spurious correlations naturally dissipate as more data is collected.

Future Directions

Future research should examine:

  • Subgroup analyses to identify individual differences in response
  • Potential confounders and mediators of the observed relationship
  • Optimal dosing and timing for Acetyl-L-Carnitine
  • Confirmation through prospective or randomized designs
  • Biological mechanisms underlying the observed effects

Conclusion

📊 Preliminary Findings: With 3 participants, these results are based on limited data. Effect sizes and confidence will improve as more participants contribute data. Consider these findings directional rather than definitive.

Above-average Acetyl-L-Carnitine was associated with a 0.0% reduction in Body Weight—a minimal effect. The Predictor Impact Score of 0.02 indicates this relationship is requiring additional data before conclusions.

Bottom Line: Based on a PIS of 0.02 and a 0.0% effect size, this relationship currently lacks sufficient evidence. Continue monitoring as more data becomes available. Note: These conclusions may strengthen or change direction as more data is collected.

These findings contribute to our understanding of how Acetyl-L-Carnitine may influence Body Weight in real-world conditions. While preliminary, these results may inform future research directions. As more participants contribute data, the reliability and precision of these findings will improve substantially.

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Methods

Study Design

This study is based on data donated by 3 participants. Thus, the study design is equivalent to the aggregation of 3 separate n=1 observational natural experiments.

This within-subject design is powerful because it controls for all stable individual characteristics (genetics, baseline health status, socioeconomic factors) that might otherwise confound the relationship between variables.

Data Analysis

Temporal Assumptions

The analysis incorporates temporal assumptions about the relationship between variables:

  • Onset Delay: It was assumed that 30 minutes would pass before a change in Acetyl-L-Carnitine would produce an observable change in Body Weight.
  • Duration of Action: It was assumed that Acetyl-L-Carnitine could produce an observable change in Body Weight for as much as 21 days after the stimulus event.

Statistical Methods

For each participant, we calculated the Pearson correlation coefficient between Acetyl-L-Carnitine values and subsequent Body Weight values. Individual correlations were then aggregated using Fisher's z-transformation to produce a population-level estimate:

Individual Correlation:

$$r_i = \frac{\sum(x_{ij} - \bar{x}_i)(y_{ij} - \bar{y}_i)}{\sqrt{\sum(x_{ij} - \bar{x}_i)^2 \sum(y_{ij} - \bar{y}_i)^2}}$$

Fisher's Z-Transformation:

$$z_i = \frac{1}{2} \ln\left(\frac{1 + r_i}{1 - r_i}\right)$$

Aggregated Correlation:

$$\bar{r} = \tanh(\bar{z}) \quad \text{where} \quad \bar{z} = \frac{1}{N}\sum_{i=1}^{N} z_i$$

Effect Size Calculation

Effect sizes are reported as percent change from baseline. For each participant, we compare the outcome following above-average predictor values to the overall baseline outcome:

$$\Delta\%_{\text{baseline}} = \frac{\bar{O}_{\text{follow-up}} - \bar{O}_{\text{baseline}}}{\bar{O}_{\text{baseline}}} \times 100$$

Effect Magnitude (Z-Score)

To assess effect magnitude relative to natural variability, we calculate the z-score:

$$z = \frac{|\Delta\%_{\text{baseline}}|}{\text{RSD}_{\text{baseline}}}$$

where RSDbaseline is the relative standard deviation of outcome during baseline period

A z-score > 2 indicates statistical significance (p < 0.05), meaning the observed change exceeds typical baseline fluctuation and is unlikely due to random variation.

Statistical Significance

Correlation significance is assessed using a two-tailed t-test:

$$t = \frac{r\sqrt{n-2}}{\sqrt{1-r^2}}$$

We reject the null hypothesis (ρ = 0) at α = 0.05 when |t| exceeds the critical value, providing statistical evidence that the observed relationship is not due to chance.

Data Sources

Acetyl-L-Carnitine data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

Body Weight data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

Data Quality

Data quality measures were applied to ensure reliable results:

  • Minimum Data Requirement: Only participants with sufficient paired observations were included in the analysis.
  • Outlier Handling: Extreme values were winsorized to reduce the influence of measurement errors.
  • Missing Data: Days with missing values were handled using appropriate filling strategies based on the variable type.
  • Test User Exclusion: Test accounts and invalid users were excluded from all analyses.

Principal Investigator

Program & Methods

Mike P. Sinn
Sinn, M. P.

Designed and implemented data collection, aggregation, causal inference pipeline, and automated study generation framework. Developed the Predictor Impact Score methodology operationalizing Bradford Hill criteria for ranking causal relationships in observational data. When he tells people this at parties, they usually say they have to go check on their car.

Individual study outputs are automated, reproducible, and open to external audit. (Which I would seriously recommend.)

Contact

Cite This Study

APA Format
Sinn, M. P. (2026). Causal Analysis: Does Acetyl-L-Carnitine Affect Body Weight?. The Journal of Citizen Science. https://studies.crowdsourcingcures.org/study/cause-1248-effect-1486-population-study
BibTeX 
@misc{sinn_cause_1248_effect_1486_population_study_2026,
  author = {Sinn, Mike P.},
  title = {Causal Analysis: Does Acetyl-L-Carnitine Affect Body Weight?},
  year = {2026},
  publisher = {The Journal of Citizen Science},
  url = {https://studies.crowdsourcingcures.org/study/cause-1248-effect-1486-population-study},
  note = {Accessed: January 10, 2026}
}
Chicago/Turabian
Sinn, Mike P. "Causal Analysis: Does Acetyl-L-Carnitine Affect Body Weight?." The Journal of Citizen Science. Accessed January 10, 2026. https://studies.crowdsourcingcures.org/study/cause-1248-effect-1486-population-study.
Harvard
Sinn, M.P., 2026. Causal Analysis: Does Acetyl-L-Carnitine Affect Body Weight?. [Aggregated N-of-1 Study] The Journal of Citizen Science. Available at: https://studies.crowdsourcingcures.org/study/cause-1248-effect-1486-population-study [Accessed January 10, 2026].

Study Type: Aggregated N-of-1 Observational Mega-Study
Evidence Level: Level II (Real-World Evidence)
Methodology: Bradford Hill Criteria with Predictor Impact Score (PIS)

References

This framework was originally developed in 2013 based on the Bradford Hill criteria. Subsequent literature has independently validated similar approaches to causal inference from observational data:

  1. Hill, A.B. (1965). The environment and disease: association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295-300. [Bradford Hill criteria]
  2. Lillie, E.O., et al. (2011). The n-of-1 clinical trial: the ultimate strategy for individualizing medicine? Personalized Medicine, 8(2), 161-173. [N-of-1 methodology]
  3. Pearl, J. (2009). Causality: Models, Reasoning, and Inference . Cambridge University Press. [Causal inference]
  4. Hernán, M.A., & Robins, J.M. (2020). Causal Inference: What If . Chapman & Hall/CRC. [Free textbook]
  5. FDA (2018). Framework for FDA's Real-World Evidence Program . U.S. Food and Drug Administration. [Regulatory context]
  6. Duan, N., et al. (2013). Single-patient (n-of-1) trials: a pragmatic clinical decision methodology . Journal of Clinical Epidemiology, 66(8), S21-S28.
  7. Platt, R., et al. (2018). The FDA Sentinel Initiative—an evolving national resource . New England Journal of Medicine, 379(22), 2091-2093.

This information is for research and educational purposes only, not medical advice. Consult a healthcare provider before making health decisions. Terms of Service

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