Inferring causal pathways among three or more variables from steady-state correlations in a homeostatic system

On the causal relationships between hyperinsulinaemia, insulin resistance, obesity and dysglycaemia in type 2 diabetes

Hundreds of millions of people are affected by hyperinsulinaemia, insulin resistance, obesity and the dysglycaemia that mark a common progression from metabolic health to type 2 diabetes. Although the relative contribution of these features and the order in which they appear may differ between individuals, the common clustering and seemingly progressive nature of type 2 diabetes aetiology has guided research and clinical practice in this area for decades. At the same time, lively debate around the causal relationships between these features has continued, as new data from human trials and highly controlled animal studies are presented. This 'For debate' article was prompted by the review in Diabetologia by Esser, Utzschneider and Kahn ( https://doi.org/10.1007/s00125-020-05245-x ), with the purpose of reviewing established and emerging data that provide insight into the relative contributions of hyperinsulinaemia and impaired glucose-stimulated insulin secretion in progressive stages between health, obesity and diabetes. It is concluded that these beta cell defects are not mutually exclusive and that they are both important, but at different stages.

Hyperinsulinemia in Obesity, Inflammation, and Cancer

The relative insufficiency of insulin secretion and/or insulin action causes diabetes. However, obesity and type 2 diabetes mellitus can be associated with an absolute increase in circulating insulin, a state known as hyperinsulinemia. Studies are beginning to elucidate the cause-effect relationships between hyperinsulinemia and numerous consequences of metabolic dysfunctions. Here, we review recent evidence demonstrating that hyperinsulinemia may play a role in inflammation, aging and development of cancers. In this review, we will focus on the consequences and mechanisms of excess insulin production and action, placing recent findings that have challenged dogma in the context of the existing body of literature. Where relevant, we elaborate on the role of specific signal transduction components in the actions of insulin and consequences of chronic hyperinsulinemia. By discussing the involvement of hyperinsulinemia in various metabolic and other chronic diseases, we may identify more effective therapeutics or lifestyle interventions for preventing or treating obesity, diabetes and cancer. We also seek to identify pertinent questions that are ripe for future investigation.

Identifying Relative Changes in Social Risk Factors: An Analytic Approach

BACKGROUND

Individuals often report concurrent social risk factors such as food insecurity, unstable housing, and transportation barriers. Comparing relative changes between pairs of social risk factors may identify those that are more resistant to change.

OBJECTIVE

The objective of this study was to develop a method to describe relative changes in pairs of social risk factors.

RESEARCH DESIGN

This was a prospective cohort study.

SUBJECTS

Participants in a randomized controlled trial of hypertension care in an Urban Indian Health Organization.

MEASURES

We measured 7 social risk factors (housing, transportation, food, clothing, health care, utilities, and debts) at enrollment, 6, and 12 months among 295 participants in the trial. We hypothesized that pairwise comparisons could identify social risk factors that were less likely to change over time. We used conditional odds ratios (ORs) with 95% confidence intervals (CIs) to rank each pair.

RESULTS

Food, clothing, health care, utilities, and debts had more changes between 0 and 6 months relative to housing (OR=2.3, 3.4, 4.7, 3.5, and 3.4, respectively; all 95% CI excluded 1.0). These same social risk factors also had more changes between baseline and 6 months relative to transportation (OR=2.8, 3.4, 4.9, 4.7, and 4.1, respectively; all 95% CI excluded 1.0). Changes in housing and transportation risk factors were comparable (OR=0.7, 95% CI: 0.4-1.4). Relative changes between 6 and 12 months were similar.

CONCLUSIONS

Housing and transportation exhibited fewer relative changes than other social risk factors and might be more resistant to change. Awareness of the relationships between social risk factors can help define priorities for intervention.

Driver versus navigator causation in biology: the case of insulin and fasting glucose

BACKGROUND

In biomedicine, inferring causal relation from experimental intervention or perturbation is believed to be a more reliable approach than inferring causation from cross-sectional correlation. However, we point out here that even in interventional inference there are logical traps. In homeostatic systems, causality in a steady state can be qualitatively different from that in a perturbed state. On a broader scale there is a need to differentiate driver causality from navigator causality. A driver is essential for reaching a destination but may not have any role in deciding the destination. A navigator on the other hand has a role in deciding the destination and the path but may not be able to drive the system to the destination. The failure to differentiate between types of causalities is likely to have resulted into many misinterpretations in physiology and biomedicine.

METHODS

We illustrate this by critically re-examining a specific case of the causal role of insulin in glucose homeostasis using five different approaches (1) Systematic review of tissue specific insulin receptor knock-outs, (2) Systematic review of insulin suppression and insulin enhancement experiments, (3) Differentiating steady state and post-meal state glucose levels in streptozotocin treated rats in primary experiments, (4) Mathematical and theoretical considerations and (5) Glucose-insulin relationship in human epidemiological data.

RESULTS

All the approaches converge on the inference that although insulin action hastens the return to a steady state after a glucose load, there is no evidence that insulin action determines the steady state level of glucose. Insulin, unlike the popular belief in medicine, appears to be a driver but not a navigator for steady state glucose level. It is quite likely therefore that the current line of clinical action in the field of type 2 diabetes has limited success largely because it is based on a misinterpretation of glucose-insulin relationship. The insulin-glucose example suggests that we may have to carefully re-examine causal inferences from perturbation experiments and set up revised norms for experimental design for causal inference.