A multidimensional functional fitness score has a stronger association with type 2 diabetes than obesity parameters in cross sectional data.

OBJECTIVES: We examine here the association of multidimensional functional fitness with type 2 diabetes mellitus (T2DM) as compared to anthropometric indices of obesity such as body mass index (BMI) and waist to hip ratio (WHR) in a sample of Indian population. RESEARCH DESIGN AND METHOD: We analysed retrospective data of 663 volunteer participants (285 males and 378 females between age 28 and 84), from an exercise clinic in which every participant was required to undergo a health related physical fitness (HRPF) assessment consisting of 15 different tasks examining 8 different aspects of functional fitness. RESULTS: The odds of being diabetic in the highest quartile of BMI were not significantly higher than that in the lowest quartile in either of the sexes. The odds of being a diabetic in the highest WHR quartile were significantly greater than the lowest quartile in females (OR = 4.54 (1.95, 10.61) as well as in males (OR = 3.81 (1.75, 8.3). In both sexes the odds of being a diabetic were significantly greater in the lowest quartile of HRPF score than the highest (males OR = 10.52 (4.21, 26.13); females OR = 10.50 (3.53, 31.35)). After removing confounding, the predictive power of HRPF was significantly greater than that of WHR. HRPF was negatively correlated with WHR, however for individuals that had contradicting HRPF and WHR based predictions, HRPF was the stronger predictor of T2DM. CONCLUSION: The association of multidimensional functional fitness score with type 2 diabetes was significantly stronger than obesity parameters in a cross sectional self-selected sample from an Indian city.

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.

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

Cross-sectional correlations between two variables have limited implications for causality. We examine here whether it is possible to make causal inferences from steady-state data in a homeostatic system with three or more inter-correlated variables. Every putative pathway between three variables makes a set of differential predictions that can be tested with steady state data. For example, among 3 variables, A, B and C, the coefficient of determination, [Formula: see text] is predicted by the product of [Formula: see text] and [Formula: see text] for some pathways, but not for others. Residuals from a regression line are independent of residuals from another regression for some pathways, but positively or negatively correlated for certain other pathways. Different pathways therefore have different prediction signatures, which can be used to accept or reject plausible pathways using appropriate null hypotheses. The type 2 error reduces with sample size but the nature of this relationship is different for different predictions. We apply these principles to test the classical pathway leading to a hyperinsulinemic normoglycemic insulin-resistant, or pre-diabetic, state using four different sets of epidemiological data. Currently, a set of indices called HOMA-IR and HOMA-ß are used to represent insulin resistance and glucose-stimulated insulin response by ß cells respectively. Our analysis shows that if we assume the HOMA indices to be faithful indicators, the classical pathway must in turn be rejected. In effect, among the populations sampled, the classical pathway and faithfulness of the HOMA indices cannot be simultaneously true. The principles and example shows that it is possible to infer causal pathways from cross sectional correlational data on three or more correlated variables.

What to expect from an evolutionary hypothesis for a human disease: The case of type 2 diabetes.

Evolutionary medicine has a promise to bring in a conceptual revolution in medicine. However, as yet the field does not have the same theoretical rigour as that of many other fields in evolutionary studies. We discuss here with reference to type 2 diabetes mellitus (T2DM) what role an evolutionary hypothesis should play in the development of thinking in medicine. Starting with the thrifty gene hypothesis, evolutionary thinking in T2DM has undergone several transitions, modifications and refinements of the thrift family of hypotheses. In addition alternative hypotheses independent of thrift are also suggested. However, most hypotheses look at partial pictures; make selective use of supportive data ignoring inconvenient truths. Most hypotheses look at a superficial picture and avoid getting into the intricacies of underlying molecular, neuronal and physiological processes. Very few hypotheses have suggested clinical implications and none of them have been tested with randomized clinical trials. In the meanwhile the concepts in the pathophysiology of T2DM are undergoing radical changes and evolutionary hypotheses need to take them into account. We suggest an approach and a set of criteria to evaluate the relative merits of the alternative hypotheses. A number of hypotheses are likely to fail when critically evaluated against these criteria. It is possible that more than one selective process are at work in the evolution of propensity to T2DM, but the intercompatibility of the alternative selective forces and their relative contribution needs to be examined. The approach we describe could potentially lead to a sound evolutionary theory that is clinically useful and testable by randomized controlled clinical trials.