Mathematics is the language of nature, and Gerald Hsu is teaching us how to speak it to our bodies.
Imagine trying to understand a symphony by examining only one violin. For centuries, this has resembled medicine's approach to complex chronic diseases—studying individual parts while missing the harmonious whole. Enter Gerald C. Hsu, an unlikely medical revolutionary who arrived not with a white coat, but with a mathematician's intuition.
After receiving a dire type 2 diabetes diagnosis that threatened his life, this stubbornly persistent engineer turned his analytical mind toward solving one of medicine's most persistent puzzles: how can we truly understand the complex interplay of factors that determine our health? The result was the GH-Method: Math-Physical Medicine, a novel approach that uses mathematical rigor to decode the hidden language of human metabolism.
Presented at Eurometabolomics-2019, Hsu's methodology represents a fundamental shift from traditional biological approaches, offering instead a physics-based lens through which to view health—one that has allowed him to reverse his severe diabetic condition and potentially unlock new pathways for managing chronic diseases worldwide.
The GH-Method treats the human body as a complex system that follows mathematical rules and physical principles, allowing for prediction of health outcomes with remarkable accuracy.
Traditional medicine often begins with biology—studying chemical pathways, cellular processes, and molecular interactions. Math-physical medicine flips this approach, starting instead with the mathematical relationships and physical patterns that underlie biological phenomena.
Hsu's approach treats the human body as an extraordinarily complex system that obeys certain mathematical rules and physical principles. By identifying these rules, we can predict how the system will behave under different conditions—much like physicists can predict the trajectory of a planet based on gravitational laws.
This methodology aligns with a broader movement toward quantitative thinking in biomedical science, as exemplified by textbooks like Mathematics for Biomedical Physics 5 , which highlights how mathematical topics can be applied to interdisciplinary fields of biology and medicine.
The GH-Method rests on four foundational pillars:
Hsu developed a comprehensive mathematical model that quantifies metabolic health using approximately 500 elements across 10 categories . This index generates a single score representing overall metabolic status.
Since 2012, Hsu has collected approximately 1.5 million data points about his own body and daily habits . This includes everything from blood glucose measurements to food intake, exercise, and stress levels.
Using this wealth of data, Hsu created four prediction tools for weight, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and HbA1C with remarkable 95% to 99% accuracy 4 .
Instead of focusing exclusively on molecular-level explanations, the GH-Method emphasizes identifying mathematical patterns and correlation coefficients between different health parameters .
Hsu's self-study revealed strong connections between key metabolic parameters:
Correlation between weight and glucose
Correlation between glucose and blood pressure
Correlation between weight and blood pressure
Quantitative score combining ~500 health elements across 10 categories to generate a single metabolic health score.
Collection of ~1.5 million data points on lifestyle factors and biomarkers since 2012 for comprehensive analysis.
Development of prediction tools for weight, FPG, PPG, and HbA1C with 95-99% accuracy based on collected data.
Identification of mathematical patterns and correlation coefficients between different health parameters.
Elements in Metabolism Index
Prediction Accuracy
Health Categories Analyzed
One of Hsu's most compelling experiments—and a perfect example of the GH-Method in action—involved a simple slice of toast 4 . Here was his approach:
This experiment wasn't conducted in a laboratory with specialized equipment, but in the real world, using tools available to many people with diabetes: a glucose meter, a step counter, and a meticulous recording habit.
| Meal Type | Number of Meals | Avg PPG (mg/dL) | Avg Carbs (g) | Avg Steps |
|---|---|---|---|---|
| Toast meals | 30 | 110.6 | 21.4 | 4,208 |
| All meals | 517 | 110.8 | 13.4 | 4,427 |
| Coffee (decaf) | 9 | ~100 | ~0 | Not specified |
Hsu interpreted these results as evidence of his pancreatic beta cells repairing themselves at a slow rate of approximately 2.3% per year 4 . While this rate seems modest, over six years it translated to roughly 14% improvement in insulin production function—a finding that contradicts the common medical assertion that beta cell damage is irreversible.
[Chart: Toast meal PPG values showing significant reduction from ~200 mg/dL in 2014 to ~110 mg/dL in 2020]
The data shows a clear trend of improved glucose tolerance to starchy foods over time, suggesting pancreatic beta cell recovery and improved insulin sensitivity.
Reduction in PPG response to toast meals from 2014 to 2020, indicating significant metabolic improvement.
[Chart: Triangular correlation analysis showing relationships between weight, glucose, and blood pressure]
Hsu's triangular dual-analysis revealed strong interconnections between weight, glucose, and blood pressure, suggesting these biomarkers function as a closely-linked system.
[Chart: Estimated pancreatic beta cell function improvement of ~2.3% per year over 6 years]
Based on the toast experiment results, Hsu estimated his pancreatic beta cells were repairing at a rate of approximately 2.3% annually, challenging the notion that beta cell damage is irreversible.
Hsu extended his methodology beyond glucose metabolism to assess broader health risks. He developed a sophisticated risk assessment model that incorporates:
Using this model, Hsu calculated his own risk probability for cardiovascular diseases or stroke had decreased from 82%-94% in 2010 to 49%-54% in 2017, before slightly rising to 55%-56% in 2019 due to hectic travel schedules . Similarly, his risk for renal complications dropped from 69% in 2000 to 33% in 2019 .
| Year | CVD/Stroke Risk (%) | Renal Complications Risk (%) | Key Factors |
|---|---|---|---|
| 2000 | Not specified | 69 | Pre-method baseline |
| 2010 | 82-94 | 57 (2012) | Weight >200 lbs, BMI >30 |
| 2017 | 49-54 | ~40 (estimated) | Weight <170 lbs, BMI <25 |
| 2019 | 55-56 | 33 | Heavy travel schedule |
One of Hsu's key analytical approaches involves examining the interrelationships between three fundamental metabolic biomarkers: weight, glucose, and blood pressure. He calls this "triangular dual-analysis," examining both time-series correlations and spatial pattern relationships .
Strong correlation between body weight and blood glucose levels
Significant correlation between glucose levels and blood pressure
Moderate correlation between body weight and blood pressure
These interconnected relationships suggest that these three biomarkers function as a closely-linked system rather than independent factors—a insight with significant implications for holistic approaches to metabolic health.
For those interested in applying approaches inspired by the GH-Method, several key tools and concepts are essential. While Hsu's own research uses specialized mathematical modeling, many principles can be adapted for personal health monitoring.
| Tool/Concept | Function in GH-Method | Real-World Analog |
|---|---|---|
| Metabolism Index (MI) | Quantitative score combining ~500 health elements | Basic health scoring system tracking key metrics |
| Big Data Collection | Gathering ~1.5M data points on lifestyle & biomarkers | Consistent tracking of weight, diet, exercise, glucose |
| Predictive Modeling | Forecasting weight, FPG, PPG, HbA1C with 95-99% accuracy | Pattern recognition to anticipate health responses |
| Correlation Analysis | Calculating mathematical relationships between biomarkers | Noting connections between lifestyle choices and health outcomes |
| Waveform Analysis | Examining glucose curve patterns over time | Using CGM to track food response patterns |
Use health tracking apps to consistently log meals, exercise, and biomarkers for pattern analysis.
Utilize fitness trackers and continuous glucose monitors for automated data collection.
Create charts and graphs to identify patterns and correlations in your health data.
Gerald Hsu's GH-Method represents more than just one man's journey to reverse his diabetes—it offers a fundamentally different approach to understanding human health. By applying mathematical rigor and physical principles to the complex system of human metabolism, Hsu has demonstrated that even severe metabolic disorders may be reversible through meticulous lifestyle modification.
Perhaps the most inspiring aspect of Hsu's work is his demonstration that pancreatic beta cells may possess self-repair capabilities even in later life 4 . This finding alone challenges fatalistic narratives about chronic diseases and offers new hope for those struggling with metabolic conditions.
The GH-Method also highlights the tremendous potential of personalized data collection in transforming health outcomes. In an era of wearable sensors and health tracking apps, Hsu's work points toward a future where each of us can become the primary investigator of our own health, using data rather than dogma to guide our decisions.
As Hsu himself proved, you don't need a medical degree to make profound contributions to health science—sometimes what you need is a different perspective, a willingness to collect meticulous data, and the mathematical intuition to see patterns where others see only chaos.