Science
Research is rarely as simple as “works” or “doesn't work.” We look at how studies are designed, what they actually found, who was studied, and what questions still remain.
How We Read the Research ↓
Science isn't a collection of certainty. It's a process for asking better questions, testing ideas, examining evidence, and changing what we think when the evidence changes.
How We Look at Science.
A study can sound impressive in a headline and still leave important questions unanswered. Before drawing conclusions, it helps to understand what was actually studied and how.
What Was the Question?
Good research begins with a specific question. We start by identifying what the researchers were actually trying to investigate rather than assuming the study answers a broader question.
Who—or What—Was Studied?
Human clinical research, observational studies, and laboratory experiments can each tell us different things. Findings from one type of research don't automatically translate into another.
How Was It Designed?
Study size, duration, controls, randomization, blinding, dosage, participant characteristics, and measurement methods can all influence how findings should be interpreted.
What Did They Actually Find?
We separate the measured results from the interpretation surrounding them. Statistical significance, biological relevance, and real-world importance are not always the same.
Has It Been Repeated?
One study can be interesting. A larger body of evidence can tell us whether findings are consistent across researchers, populations, methods, and settings.
What's Still Unknown?
Uncertainty isn't a failure of science. Clearly identifying unanswered questions is part of understanding the evidence responsibly.
Understanding the Evidence.
Different types of research answer different questions. One isn't automatically “good” and another “bad”—the important question is what conclusions each type of evidence can reasonably support.
Laboratory Research
Controlled experiments can help researchers investigate mechanisms and biological processes, often under conditions very different from everyday human exposure.
Observational Research
Researchers observe patterns in human populations. These studies can identify associations, but association alone does not establish causation.
Clinical Research
Controlled human studies can directly investigate specific interventions and outcomes, although study quality, population, duration, and design still matter.
Detection Is Only the Beginning.
Researchers have reported microplastic and nanoplastic particles in environmental samples, food and water, and human biological samples.
But finding a particle and understanding what that finding means for human health are two different scientific questions.
Explore the Science.
Start with a subject and follow the evidence—from biological mechanisms and study design to what researchers know and what remains uncertain.
Microplastics
Exposure pathways, particle detection, biological interactions, and emerging human research.
Absorption & Bioavailability
Why the amount consumed isn't necessarily the same as the amount that reaches circulation or tissues.
Oxidative Stress
Free radicals, antioxidant systems, redox balance, and what researchers mean when they discuss oxidative stress.
The Gut Microbiome
The complex microbial ecosystem researchers are studying throughout digestion, metabolism, and human physiology.
Inflammation
What inflammation actually is, why it is part of normal physiology, and why context matters when interpreting inflammatory markers.
Cellular Energy
ATP, mitochondria, energy metabolism, and the pathways cells use to capture and use energy.
What Does That Actually Mean?
Research comes with its own vocabulary. Understanding a few common terms can completely change how you read a study—or a headline about one.
Association
Two things occur together statistically. An association by itself does not prove that one caused the other.
Causation
Evidence supporting the conclusion that one factor contributes to producing a particular outcome.
Statistical Significance
A statistical measure used to evaluate whether an observed result is unlikely under a specified null hypothesis. It does not automatically tell us whether the effect is large or practically important.
Clinical Significance
Whether a measured difference is meaningful enough to matter in a real-world clinical context.
Confounding Variable
A factor related to both an exposure and an outcome that can complicate interpretation of an observed association.
Peer Review
Evaluation of research by other experts before publication. Peer review adds scrutiny, but it does not guarantee that a study is correct.
Questions Worth Asking.
Now Meet the Ingredients.
Our Ingredient Deep Dives bring the same approach to individual compounds, nutrients, and botanicals: what they are, how they are studied, what the evidence suggests, and where uncertainty remains.
Explore Ingredient Deep Dives →