Untangling the Hidden Links

The Illusion of Cause and Effect

In medical research, appearances can be deceiving. What looks like a clear relationship between two factors—such as blood sugar levels and cardiovascular disease—might be completely distorted by hidden third factors that researchers haven't properly accounted for. This distortion is called confounding, and it represents one of the most significant challenges in determining what truly causes disease and what merely appears to do so.

The relationship between glycemia (blood sugar levels) and cardiovascular risk represents a perfect case study of confounding in action. While we have overwhelming evidence that people with higher blood sugar tend to have more heart attacks and strokes, untangling whether high blood sugar directly causes cardiovascular problems or simply marks other risk factors has proven extraordinarily complex. This article will explore how epidemiologists identify and account for confounding, using the ongoing research into glycemia and cardiovascular disease as our central example.

The Confounding Puzzle: What Is It Exactly?

The Three Faces of a Confounder

Confounding occurs when the observed association between an exposure (like high blood sugar) and an outcome (like heart disease) is distorted by a third variable that is associated with both the exposure and the outcome. For a variable to be considered a potential confounder, it must meet three specific criteria ^{1 5} :

The classic example comes from a hypothetical study of foot size and reading ability in elementary school children ¹ . If researchers measured foot size and reading speed, they might find that children with larger feet read significantly faster. The crude analysis might suggest that foot size influences reading ability, with an odds ratio of 28.8—meaning children with feet over 8.25 inches were nearly 29 times more likely to be fast readers ¹ .

But we instinctively know this relationship isn't causal. The missing confounder? Grade level. Older children have larger feet and are better readers because they're more developed educationally. Grade level is associated with foot size (criterion 1), directly affects reading ability (criterion 2), and isn't on the causal pathway between foot size and reading (criterion 3).

Glycemia and Cardiovascular Risk: A Case Study Packed With Confounders

The Historical Evidence

The association between various indices of glycemia—fasting blood glucose, post-meal glucose, and hemoglobin A1c (a long-term measure of blood sugar control)—and cardiovascular disease risk is strong and consistent ⁶ . Multiple large studies across diverse populations have confirmed that people with elevated blood sugar levels have significantly higher risks of heart attacks, strokes, and other cardiovascular events.

The relationship holds true even in the pre-diabetic range, where blood sugar levels are elevated but not yet high enough to diagnose diabetes ⁶ . This consistent finding across populations makes the association statistically robust, but it doesn't necessarily prove causation.

Biological Plausibility: Mechanisms Matter

For an association to be considered causal, there should be a plausible biological mechanism. In the case of hyperglycemia (high blood sugar) and cardiovascular disease, several compelling mechanisms have been proposed ⁶ :

These mechanisms provide biological plausibility to the idea that high blood sugar directly causes cardiovascular damage. However, the presence of plausible mechanisms doesn't eliminate the possibility of confounding.

A Deep Dive Into a Key Experiment: The Swedish Severe Hypoglycemia Study

Methodology: A Natural Experiment

A 2025 study published using Swedish registry data provides an excellent example of both sophisticated confounding control and important findings about glycemic control and cardiovascular risk ² . This comparative retrospective cohort study examined whether severe hypoglycemic events (SHEs)—dangerously low blood sugar episodes requiring assistance—increase the risk of subsequent cardiovascular complications in adults with type 1 diabetes.

The researchers identified 14,829 adults with type 1 diabetes from the Swedish National Diabetes Register, of whom 1,313 had experienced a severe hypoglycemic event ² . They then compared cardiovascular hospitalization rates between those with and without prior SHEs. Crucially, they also compared outcomes between users of two different glucose monitoring systems: intermittently scanned continuous glucose monitoring (isCGM) and traditional blood glucose monitoring (BGM).

To control for potential confounders, the researchers used propensity score-based inverse probability of treatment weighting ² . This sophisticated statistical technique creates a weighted sample where the exposed and unexposed groups are balanced on all measured potential confounders—including age, sex, BMI, baseline HbA1c, insulin delivery method, lipid profile, kidney function, smoking status, physical activity, and pre-existing conditions ² .

Results and Analysis: Unexpected Findings

The study yielded several important findings that illustrate the complexity of the relationship between glycemic control and cardiovascular risk:

First, the study confirmed that severe hypoglycemic events are associated with substantially increased cardiovascular risk. Patients who experienced SHEs had more than double the risk of subsequent cardiovascular hospitalizations compared to those without SHEs ² .

More surprisingly, the type of glucose monitoring dramatically modified this risk. Among those who experienced severe hypoglycemia, isCGM users had a 78% lower rate of cardiovascular hospitalizations compared to BGM users ² . This protective association remained strong even after statistical adjustment for potential confounders.

These findings are important for several reasons. They suggest that the relationship between glycemic control and cardiovascular risk is modified by both hypoglycemic events and the technology used to monitor glucose. The dramatically different outcomes between isCGM and BGM users with similar hypoglycemic events indicates that factors beyond glucose levels—perhaps including how quickly hypoglycemia is detected and treated—significantly influence cardiovascular risk.

The Scientist's Toolkit: Key Concepts and Methods

Controlling for Confounding: A Researcher's Arsenal

Researchers have developed multiple approaches to address confounding, both during study design and in statistical analysis ^{1 4} :

Each method has strengths and limitations. Restriction reduces confounding but limits generalizability. Stratification becomes difficult when multiple confounders need simultaneous control. Multivariate regression can handle multiple confounders but relies on correct model specification ^{4 5} .

Conclusion: Beyond Simple Explanations

The relationship between glycemia and cardiovascular risk illustrates why confounding remains a central concern in epidemiology. While blood sugar levels clearly correlate with cardiovascular disease risk, the causal pathways are complex and interwoven with numerous other factors—including age, blood pressure, cholesterol levels, kidney function, and lifestyle factors.

The Swedish study highlights an additional layer of complexity: the relationship appears bidirectional. Not only does hyperglycemia potentially increase cardiovascular risk, but hypoglycemia does as well, and the technology used to manage blood sugar modifies this relationship ² . This complexity doesn't mean we should abandon the connection between blood sugar and heart health, but rather that we need sophisticated approaches to understand it.

As research continues, what becomes clear is that in epidemiology, few relationships are as simple as they first appear. The history of confounding teaches us humility in interpreting associations and respect for the complex web of factors that influence human health.