API

Relationship: 885

Title

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Increased, Catabolism of Muscle Protein leads to Decreased, Body Weight

Upstream event

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Increased, Catabolism of Muscle Protein

Downstream event

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Decreased, Body Weight

Key Event Relationship Overview

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AOPs Referencing Relationship

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AOP Name Directness Weight of Evidence Quantitative Understanding
Antagonist binding to PPARα leading to body-weight loss directly leads to Strong Strong

Taxonomic Applicability

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Term Scientific Term Evidence Link
human Homo sapiens Strong NCBI

Sex Applicability

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Sex Evidence
Male Strong
Female Strong

Life Stage Applicability

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Term Evidence
Adults Strong
Juvenile Strong

How Does This Key Event Relationship Work

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After two to three days of fasting in humans, dietary glucose has been long-since expended and contribution to blood glucose from glycogen metabolism is reduced to zero (Cahill 2006). At this point, about two fifths of fatty acid metabolism in the whole body is dedicated to hepatic ketogenesis, largely in support of the energy demands of the brain, however the brain is still significantly supported by glucose derived from gluconeogenesis (Cahill 2006). As fatty acid stores are depleted, gluconeogenesis from other substrates becomes increasingly important including muscle protein catabolism in situ for supporting muscle function as well as releasing glutamine (Marliss et al 1971) and alanine (Felig et al 1970A) which can be recycled to glucose by gluconeogenesis in the kidney (Goodman et al 1966, Kashiwaya et al 1994, Cahill 2006). Renal gluconeogenesis from glutamine and alanine supports two fifths of new glucose production while the remaining three fifths is produced in liver from, (a) alanine derived from muscle and nonhepatic splanchnic bed, (b) recycled lactate and pyruvate from red blood cells and renal medulla, (c) glycerol from adipose lipolysis and (d) small amounts of β-hydroxybutyrate are recycled to glucose (Cahill 2006). Blood concentrations of alanine exert control over hepatic glucose production and thus also represent a diagnostic of alanine contribution from muscle to support gluconeogenesis (Cahill 2006, Felig et al 1970B). In prolonged starvation events, the catabolism of muscle protein (KE6) for gluconeogenesis in order to support systemic energy needs results in loss of muscle mass which contributes to loss of overall body weight.

Dynamic energy budget theory has provided useful insights on how organisms take up, assimilate and then allocate energy to various fundamental biological processes including maintenance, growth, development and reproduction (Nisbet et al 2000). Regarding energy allocation, somatic maintenance (maintaining homeostasis) must first be met before then growth may occur, followed by maturation and then finally, surplus energy is dedicated to reproduction (Nisbet et al 2000). If somatic maintenance cannot be sustained, energy substrates must be generated using standing biomass from non-essential organs, such as skeletal muscle, to maintain homeostasis ultimately leading to the ultimate AO of weight loss (Cahill 2006). As an example of the importance of energy allocation to ecological fitness, a review by Martin et al (1987) demonstrated that energy availability (availability of food) was the predominant limiting factor in reproductive success and survival for both young and parents in a broad life history review for bird species. This is a likely scenario for many organisms.

Weight of Evidence

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Given the evidence described above, the KER for the KE, “increased, catabolism of muscle protein” -> the AO, “decreased body weight”, received a “strong” weight of evidence score given that catabolism and thus loss of muscle mass to sustain systemic energy requirements has been broadly documented as a source of body weight loss (Cahill 2006).

Biological Plausibility

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Biological plausibility of this KER is strong given the supporting relationships cited in the literature described in the previous bullets above.

Empirical Support for Linkage

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Include consideration of temporal concordance here

During starvation, the loss of muscle mass to sustain systemic energy requirements has been broadly documented as a source of body weight loss (Cahill 2006). Body weight loss can occur via component weight loss from nearly all organ systems. In starvation conditions, loss of muscle mass is essentially always connected to overall body-weight loss. Regarding temporal concordance, the process is reversed after feeding resumes.


Uncertainties or Inconsistencies

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No uncertainties presented.

Quantitative Understanding of the Linkage

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Is it known how much change in the first event is needed to impact the second? Are there known modulators of the response-response relationships? Are there models or extrapolation approaches that help describe those relationships?

Specific thresholds for translating the KE, “increased, catabolism of muscle protein” -> the AO, “decreased body weight” are species specific. Models for investigating these relationships are available including dynamic energy budget models (Nisbet et al 2000) as well as variety of detailed caloric models for humans.

Evidence Supporting Taxonomic Applicability

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This KER is generally applicable to animal systems.

References

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Cahill GF, Jr. Fuel metabolism in starvation. Annu Rev Nutr 2006, 26:1-22.

Felig P, Pozefsky T, Marliss E, Cahill GF, Jr.: Alanine: key role in gluconeogenesis. Science 1970A, 167(3920):1003-1004.

Felig P, Marliss E, Pozefsky T, Cahill GF, Jr.: Amino acid metabolism in the regulation of gluconeogenesis in man. Am J Clin Nutr 1970B, 23(7):986-992.

Kashiwaya Y, Sato K, Tsuchiya N, Thomas S, Fell DA, Veech RL, Passonneau JV: Control of glucose utilization in working perfused rat heart. J Biol Chem 1994, 269(41):25502-25514.

Marliss EB, Aoki TT, Pozefsky T, Most AS, Cahill GF: Muscle and splanchnic glutamine and glutamate metabolism in postabsorptive and starved man. J Clin Invest 1971, 50(4):814-817.

Martin TE: Food as a limit on breeding birds: a life-history perspective. Annu Rev Ecol Syst 1987:453-487.

Nisbet R, Muller E, Lika K, Kooijman S: From molecules to ecosystems through dynamic energy budget models. J Anim Ecol 2000, 69(6):913-926.