Clearance: oxidation or conversion to glycogen/lactate
So 200g of glucose would behave very differently in:
1) Someone who is sedentary - less tissue uptake due to GLUT4, less oxidation capacity due to reduced mitochondria, inefficient oxidation -> result: blood glucose high, insulin response high, high serum lactate
2) Someone running a marathon - high oxidative capacity, high oxidative demand -> blood glucose normal, minimal insulin response since glucose is being oxidized, any lactate is oxidized
- Triglycerides linger in plasma.
- ApoB particle count rises.
- Fatty acids spill into liver and muscle, forming DAGs and ceramides (more on this later)
2) Endurance-trained:
High LPL activity, dense mitochondria, strong oxidative demand. High LDL-R activity
- Fatty acids rapidly taken up and oxidized to CO2 + H2O.
- Plasma triglycerides and ApoB particles fall quickly.
- Minimal lipid spillover, high insulin sensitivity.
Is fat bad?
Yes. If you can’t clear it.
Chronic Flux Imbalance Over Time Leads to Maladaptive Responses
- Too little fat clearance -> fatty acids spill over into tissue (e.g. muscle/liver) forming DAGs and ceramides that impair insulin signaling. The cell interprets this as energy overload and downregulates glucose uptake, causing a negative feedback loop that further worsens clearance symmetry
- Too little glucose clearance -> Chronic hyperglycemia and hyperinsulinemia drive de novo lipogenesis, adding more lipid to an already congested system (see too little lipid clearance)
Glycolytic overflow increases lactate and ROS, feeding back into mitochondrial stress and potentially creating a more cancer-permissive milieu (The cancer–lactate connection remains under investigation, but elevated lactate is consistently observed in metabolically unhealthy states. And higher cancer rates are seen in the metabolically unhealthy).
Persistent flux imbalance forces the body into defensive adaptation.
Energy accumulates rather than flows, and chronic accumulation creates maladaptive states that form negative feedback loops, ultimately manifesting in disease.
We tend to treat what we eat as independent of the conditions required to clear it.
We tend to view exercise as something that’s simply good for the heart.
But these aren’t separate domains.
They are parts of the same dynamic system: intake and clearance, supply and demand, flux in and flux out.
Every bite changes the clearance requirement.
Every step changes the clearance capacity.
Multiple combinations can be healthy depending on flux context.
And this is arguably why high movement is the more important part of the equation.
It is a lot easier to quantify the output and know that clearance requirements will mostly be satisfied with 17k or so steps.
It’s a lot more difficult to quantify the clearance requirements of various inputs.
And this is not to say eat whatever you want and move 17k steps. No!
Quite the opposite. Follow a diet that will minimize clearance requirements AND maximize clearance.
Is this just a fancy way of saying eat less, move more. No, it is very different.
Eat less, move more is more about energy balance. Substrate clearance is somewhat independent of that.
You could be in energy balance, or even deficit, and that does not guarantee all substrate will be properly cleared.
It is related in a way to energy balance, but not the same thing.
It is much harder to have good clearance kinetics over time when in energy surplus.
And it’s not just what you eat and how much you clear.
Personal genetics impact the clearance equation.
The Amish are a great example. Despite higher clearance requirements from higher saturated fat and sweets, they have much lower rates of heart disease and hypertension.
But around 8% of the population has an APOB clearance gene called R3500Q. This segment of the population has much higher rates of heart disease despite high movement.
They just can’t clear it as efficiently.
So for this part of the population specifically, lowering clearance burden and increasing clearance (even pharmacologically) is very important.
They can’t eat what others in their community eat just because their clearance is impaired.
Other populations such as the Maasai have clearance advantages, such as polymorphisms in APOE, CETP, and HMGCR.
This allows them to “get away” with eating large quantities of saturated fat. Combine this with 12-16 miles of walking per day, and they are able to clear 100-200g of saturated fat a day, although not all of it because they still have fatty streaks.
The Tsimane have the lowest ever recorded CAC levels.
They achieve this through a diet that has low clearance requirements (low saturated fat) combined with 17k steps daily through hilly jungle terrain.
So it’s not just what you hear and how much you clear. It’s tailoring your clearance to your genetics as well.
Health is therefore not one-size-fits-all, but rather matching your clearance strategy to your genetics.
And this is also different from metabolic flexibility. Metabolic flexibility is being able to select the right fuel for the right context.
Clearance symmetry is whether the total flux matches intake and storage turnover.
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As a case study, I outlined how we could predict how long weight loss would take with Angus Barbieri, who famously lost 276 pounds by not eating for over a year.
Do low carb diets increase energy expenditure or fat loss independent of calories?
This is the central prediction of the carbohydrate-insulin model.
Lower insulin -> greater fat oxidation -> more fat loss.
But does it work in practice? 👇
Kevin Hall and Juen Guo analyzed 32 controlled feeding studies (563 participants) testing how carb-to-fat ratio affects body energy change when total calories and protein are the same.
Hall & Guo pooled ward and tightly controlled feeding trials where:
- Food intake was precisely provided.
- Protein was matched.
- Only carbohydrate <> fat ratio varied.
- Body composition and energy expenditure were measured directly.
Very interesting paper. For the past 6-7 years, there has been talk of a constrained energy model, where calories don’t scale linearly with physical activity. But this paper says the opposite. Why the conflicting data? 👇
First, some background. In 2016, Pontzer et al. studied subsistence populations.
When weight matched with sedentary westerners, they appeared to have the same TEE, despite the vast difference in movement (~17k steps versus mostly sedentary).
The thinking was that at a certain point, the body would start shutting down certain processes (inflammation, etc.) and allocate more energy to locomotion.
So the difference in calorie burn was explained by more locomotion in subsistence populations and less energy elsewhere (inflammation?) and less energy in locomotion in westerners and more energy elsewhere (inflammation?).