Understanding Your Results: Four Clinical Frameworks
Why Different Experts Give Different Answers
Blood test results don't exist in a vacuum. How they're interpreted depends on the clinical framework being applied — and that framework shapes whether your results are labeled "normal," "optimal," or "concerning." At Metabolicum, we believe you deserve to see the full picture. Rather than choosing one interpretation for you, we present four frameworks used by different medical and research communities — each with its own philosophy, strengths, and limitations.
Paradigms vs. Evidence Grades
These are different concepts:
For example, the Metabolic Optimization framework uses both Grade A intervention research (Virta Health trials) and Grade E clinical consensus (practitioner-derived thresholds). The framework itself isn't "Grade E" — it incorporates evidence of multiple grades.
See our Evidence Grading System for how we rate individual citations. →ℹHow Metabolicum Uses These Frameworks
Our calculators and evaluators display results across the first three frameworks: Standard Medical, Research Consensus, and Metabolic Optimization. These frameworks use population data and clinical research to define thresholds.
The fourth framework — Precision Medicine — is included for educational purposes to show where metabolic medicine is heading. Because it requires individual genetic testing and continuous monitoring not available through our tools, we describe it but don't apply it in calculators.
As home testing and consumer genomics evolve, Precision Medicine interpretation may become practical for our platform.
The Problem With "Normal"
Before exploring the frameworks, understand a critical limitation of standard lab ranges.
Most laboratory "normal ranges" represent the central 95% of values from a reference population. In theory, this population should be healthy. In practice, it often includes people with undiagnosed metabolic dysfunction.
The data is sobering:
| Finding | Source | Grade |
|---|---|---|
| Only ~12% of US adults meet criteria for optimal metabolic health | Araújo et al., 2019 (NHANES) | A |
| 38.7% of US adults have metabolic syndrome | JAMA Network Open, 2023 | A |
| 57% of NAFLD patients have "normal" ALT levels | Standard range limitations | B |
When "normal" ranges come from a largely unhealthy population, falling within the "normal" range means you're similar to the average person — not that you're metabolically healthy.
1Framework 1: Standard Medical
"Detect disease and reduce immediate clinical risk."
This is the dominant model in conventional medicine. It was designed to identify values requiring clinical intervention and has well-established diagnostic and treatment pathways.
How It Works
Reference ranges typically represent the 2.5th to 97.5th percentiles of a reference population.
| Marker | Standard "Normal" | What It Means |
|---|---|---|
| Fasting Glucose | 70-99 mg/dL | Below diabetic threshold |
| Fasting Insulin | 2.6-24.9 μIU/mL | Within population range |
| Triglycerides | <150 mg/dL | Below cardiovascular risk threshold |
| TG/HDL Ratio | <3.5 | Within acceptable range |
| HOMA-IR | <2.5 | No significant insulin resistance |
| HbA1c | <5.7% | Below prediabetic threshold |
Strengths
- ✓Excellent for diagnosing established disease
- ✓Evidence-based thresholds for treatment decisions
- ✓Clear action points for clinicians
- ✓Insurance and documentation compatibility
- ✓Decades of outcome data supporting thresholds
Limitations
- •Designed to detect disease after it develops
- •"Normal" may still carry elevated long-term risk
- •Thresholds set for disease detection, not optimal function
- •Reference populations include metabolically unhealthy individuals
- •May provide false reassurance to those seeking optimal health
Thresholds come from clinical guidelines (CLSI, ATP III) with extensive outcome validation. Individual citations supporting these thresholds are graded in our References.
2Framework 2: Research Consensus
"Identify early risk through evidence from large-scale studies."
Derived from large-scale epidemiological studies like Framingham and NHANES. Important caveat: Most research populations followed standard dietary guidelines (low-fat, calorie-focused), so these ranges reflect optimal values within that dietary context — not necessarily the biological ideal for individuals on alternative dietary approaches.
How It Works
Thresholds come from studies examining which biomarker values predict best long-term outcomes.
| Marker | Optimal Range | Basis |
|---|---|---|
| Fasting Glucose | 72-85 mg/dL | Lowest diabetes risk in prospective studies |
| Fasting Insulin | <6 μIU/mL | Associated with metabolic health |
| Triglycerides | <100 mg/dL | Optimal in outcome research |
| TG/HDL Ratio | <1.5-2.0 | Lowest cardiovascular and IR risk |
| HOMA-IR | <1.0-1.5 | Excellent insulin sensitivity |
| HbA1c | <5.0-5.4% | Non-diabetic optimal |
Strengths
- ✓Targets prevention, not just detection
- ✓Based on outcome research, not just population statistics
- ✓Identifies early dysfunction before clinical thresholds
- ✓Appropriate for health optimization seekers
- ✓Catches the "worried well" who actually have reason to worry
Limitations
- •Some thresholds derived from observational data
- •Individual optimal values may vary
- •Achieving these ranges may not be realistic for everyone
- •Less established treatment pathways when values are "suboptimal" but clinically "normal"
Thresholds draw on prospective outcome research and functional medicine literature. Many supporting citations are Grade A or Grade B — see specific citations in our References.
3Framework 3: Metabolic Optimization
(For Low-Carb Approaches)
"Interpret through insulin and hormonal signaling, not caloric balance."
The body is not a simple combustion engine — 100 calories of cookies stimulates insulin dramatically differently than 100 calories of steak. On carbohydrate-restricted diets, certain markers shift in ways that appear "abnormal" by conventional standards but reflect healthy metabolic adaptation. These ranges come from practitioners specializing in low-carb, ketogenic, and carnivore approaches.
How It Works
Thresholds represent values commonly achieved and considered optimal by low-carb practitioners and researchers.
| Marker | Optimal Range | Clinical Basis |
|---|---|---|
| Fasting Glucose | 70-90 mg/dL | May run slightly higher due to physiological IR |
| Fasting Insulin | <5 μIU/mL | Minimal insulin demand on low-carb |
| Triglycerides | <70-100 mg/dL | Naiman: <100 suggests at personal fat threshold |
| TG/HDL Ratio | <1.0 | Achievable with metabolic optimization |
| HOMA-IR | <0.8-1.0 | Excellent sensitivity expected |
| HbA1c | <5.0% | Reflects stable, low glucose |
Important Context
Strengths
- ✓Shows what's achievable, not just acceptable
- ✓Validated by practitioners working with metabolically optimized populations
- ✓Provides aspirational targets for those pursuing dietary intervention
- ✓Recognizes metabolic patterns not well-studied in conventional research
Limitations
- •Limited formal research validation
- •Based heavily on clinical observation
- •Not appropriate for all populations (athletes, some medical conditions)
- •Some thresholds may be unnecessarily stringent for general health
- •Elevated LDL in this context remains controversial (see below)
Thresholds combine Grade A intervention research (Virta Health studies, meta-analyses) with Grade E clinical consensus from low-carb practitioners. Specific threshold values often come from practitioner experience rather than formal trials.
4Framework 4: Precision Medicine
(Future Direction)
"Personalize targets based on individual genetics, continuous monitoring, and response patterns."
This emerging framework recognizes that optimal values vary between individuals based on genetics, environment, and life circumstances. Rather than population thresholds, it emphasizes individual baselines and trajectories.
How It Will Work
| Approach | Example |
|---|---|
| Genetic risk stratification | APOE4 carriers may need lower LDL regardless of other markers |
| Continuous monitoring | CGM reveals 5-fold individual variation in glycemic response |
| Personal baselines | Your "optimal" may differ from population optimal |
| Response tracking | How do YOUR markers respond to interventions? |
| Polygenic risk scores | Integrated genetic risk assessment |
Why It's Not in Our Calculators
Precision Medicine interpretation requires:
- •Genetic testing — Not universally available or affordable
- •Continuous monitoring — CGM, regular testing over time
- •Individual data history — Your personal baseline and trends
- •Professional interpretation — Pattern recognition across your specific data
Our calculators provide population-based reference frameworks. As consumer genomics and continuous monitoring evolve, we may incorporate precision elements.
What This Means For You Now
Even without full precision medicine capability:
- 1.Track over time — Your personal trajectory matters more than any single reading
- 2.Note interventions — Record what changes when you change behaviors
- 3.Consider family history — Genetic risk influences optimal targets
- 4.Question one-size-fits-all — You may thrive at values different from population optimal
Precision medicine draws on emerging genomics research, CGM validation studies, and individual response data. As a developing field, many claims are Grade C or Grade D. The underlying principles (individual variation exists, genetics matter) are well-established.
A Note on LDL Cholesterol and ApoB
Metabolicum focuses on metabolic health markers — TG/HDL ratio, HOMA-IR, fasting insulin, and related measures. However, any discussion of cardiovascular risk must acknowledge the elephant in the room: LDL cholesterol.
The Mainstream Position
The European Atherosclerosis Society and most major cardiology organizations consider elevated LDL and ApoB causal for atherosclerotic cardiovascular disease. This position is based on:
- •Mendelian randomization studies
- •Statin trial data
- •Decades of epidemiological evidence
- •Mechanistic understanding of plaque formation
We acknowledge this consensus. It represents the weight of cardiovascular research.
The Complicating Factors
However, the relationship between LDL and risk is not simple:
High LDL in someone with metabolic syndrome carries different risk than high LDL in someone with excellent metabolic markers. Particle type may matter more than total number.
Some lean, metabolically healthy individuals on low-carbohydrate diets develop very high LDL alongside excellent metabolic markers. Long-term outcome data is pending.
Most cardiovascular outcome trials are pharmaceutical trials. What happens when LDL rises due to dietary fat (in the context of metabolic improvement) may differ from LDL rising with metabolic dysfunction.
Our Position
Metabolicum takes no position on what LDL level is "safe" for individuals with excellent metabolic markers. We present:
- •The mainstream cardiology consensus (LDL is causal)
- •The complicating context (metabolic health, particle type, LMHR)
- •The honest uncertainty (long-term outcome data lacking for specific subgroups)
If you have elevated LDL (especially >200 mg/dL):
- 1.Don't ignore it — The mainstream position exists for reasons
- 2.Don't panic — Context matters
- 3.Get comprehensive testing — ApoB, LDL particle number, coronary calcium score
- 4.Discuss with your doctor — Ideally one familiar with metabolic health
- 5.Monitor over time — Track changes, not just single readings
Framework Comparison: TG/HDL Ratio Example
Here's how the same TG/HDL result would be interpreted across frameworks:
TG/HDL = 1.2
| Framework | Interpretation | |
|---|---|---|
| Standard Medical | ✓ | Normal— Well below 3.5 threshold |
| Research Consensus | ✓ | Optimal— Below 1.5 target |
| Metabolic Optimization | ○ | Good— Slightly above 1.0 ideal |
| Precision Medicine | ? | Depends— on your personal baseline |
TG/HDL = 2.8
| Framework | Interpretation | |
|---|---|---|
| Standard Medical | ✓ | Normal— Below 3.5 threshold |
| Research Consensus | ⚠ | Borderline— Above 2.0 optimal |
| Metabolic Optimization | ⚠ | Elevated— Well above 1.0-2.0 range |
| Precision Medicine | ? | Compare— to your trend over time |
TG/HDL = 4.5
| Framework | Interpretation | |
|---|---|---|
| Standard Medical | ⚠ | Borderline High— Above 3.5 |
| Research Consensus | ✗ | Elevated— Significant IR likely |
| Metabolic Optimization | ✗ | High— Intervention recommended |
| Precision Medicine | ? | Concerning— regardless of framework |
Which Framework Should You Use?
Start with Standard Medical
Understand where you stand relative to conventional medicine. This matters for:
- •Communicating with healthcare providers
- •Insurance and documentation
- •Identifying values requiring immediate attention
Add Research Consensus
If you're pursuing long-term health optimization:
- •Look for early warning signs standard ranges miss
- •Set more ambitious targets
- •Catch "normal but not optimal" patterns
Consider Metabolic Optimization
If you follow a low-carbohydrate approach:
- •Understand achievable targets with dietary intervention
- •Recognize patterns specific to metabolic optimization
- •Apply appropriate context to your results
Watch Precision Medicine
As technology evolves:
- •Track your personal trends over time
- •Consider genetic testing if available
- •Recognize that population averages may not be your optimal
Our Commitment to Honest Interpretation
We show multiple frameworks because we don't have all the answers. Medicine is complex. Individual variation is real. What's optimal for one person may not be optimal for another.
Respect your intelligence
You can evaluate multiple perspectives
Acknowledge uncertainty
We're honest about what's known vs. debated
Avoid false authority
We translate research, not dictate truth
Enable informed decisions
You choose what matters for your goals
Population Diversity Note
Most metabolic research has been conducted in Western, predominantly Caucasian populations. Optimal thresholds may differ for:
- •Asian populations (higher metabolic risk at lower body weights)
- •African and African-American populations (different insulin response patterns)
- •Hispanic/Latino populations (varying metabolic syndrome prevalence)
We note population limitations where known and encourage appropriate caution when applying thresholds derived from different populations.
Learn More
Metabolicum is for educational purposes and does not replace professional medical advice. Always consult your healthcare provider for interpretation of test results.
Last updated: December 2025