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Watve, M.

Publications and source records attributed to Watve, M..

2 recordsLinked to original sources

Foraging theory and the propensity to be obese: an alternative to thrift

The evolutionary origin of obesity is classically believed to be genetic or developmentally induced thrift, as an adaptation to ancestral feast and famine conditions. However, recently the thrift family of hypotheses have attracted serious criticism necessitating alternative thinking. Optimization of foraging behaviour is an important aspect of behavioural evolution. For a species evolved for optimizing nutritional benefits against predation or other foraging risks, reduction in foraging risk below a threshold dramatically increases the steady-state body weight. In modern life where feeding is detached from foraging, the behavioural regulation mechanisms are likely to fail resulting into escalation of adiposity. At a proximate level the signalling pathways for foraging optimization involve fear induced signal molecules in the brain including Cocaine and Amphetamine Regulated Transcript (CART) interacting with adiposity signals such as leptin. While leptin promotes the expression of the fear peptides, the fear peptides promote anorectic action of leptin. This interaction promotes foraging drive and risk tolerance when the stored energy is low and suppresses hunger and foraging drive when the perceived risk is high. The ecological model of foraging optimization and the molecular model of interaction of these peptides converge in the outcome that the steady state adiposity is an inverse square root function of foraging risk. The foraging optimization model is independent of thrift or insurance hypotheses, but not mutually exclusive. We review existing evidence and suggest testable predictions of the model. Understanding obesity simultaneously at proximate and ultimate levels is likely to suggest effective means to curb the obesity epidemic.

evolutionary biology

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 show here that in a homeostatic system with three or more inter-correlated variables, it is possible to make causal inferences from steady-state data. 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] is predicted by the product of [Formula] and [Formula] 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. 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-{beta} are used to represent insulin resistance and glucose-stimulated insulin response by {beta} cells respectively. Our analysis shows that if we assume the HOMA indices to be faithful indicators, the classical pathway must in turn, be rejected. Among the populations sampled, the classical pathway and faithfulness of the HOMA indices cannot be simultaneously true. The principles and tools described here can find wide application in inferring plausible regulatory mechanisms in homeostatic systems based on epidemiological data.

physiology