The pathogenic theory of homeostasis threshold deviation (HTD) in wide-range oscillatory physiological parameters: novel perspectives for hypertension and metabolic disease treatment

Wei Sun , Jin-Yu Sun , Xiangqing Kong

Life Metabolism ›› 2025, Vol. 4 ›› Issue (6) : loaf029

PDF (1160KB)
Life Metabolism ›› 2025, Vol. 4 ›› Issue (6) :loaf029 DOI: 10.1093/lifemeta/loaf029
Perspective

The pathogenic theory of homeostasis threshold deviation (HTD) in wide-range oscillatory physiological parameters: novel perspectives for hypertension and metabolic disease treatment

Author information +
History +
PDF (1160KB)

Abstract

Based on current research in neuroscience, systems biology, and clinical medicine, we propose a novel theoretical concept: the “Homeostasis Threshold Deviation (HTD) Theory of Wide-Range Oscillatory Physiological Parameters”. HTD posits that, when the external environment undergoes significant and sustained changes or when visceral signals exhibit long-term abnormalities, central nervous system (CNS) network topologies reset first, establishing a new range of physiological setpoints before peripheral organs adapt. This central reset leads to a deviation in the topological information network structure of central nuclei, resulting in the displacement of the original range of physiological parameters that become challenging to restore. This deviation further triggers passive adaption in peripheral organs, ultimately causing complex multi-organ diseases and severe organ dysfunction. By targeting the CNS threshold shift, HTD provides a novel path for precision medicine. That is, restoring original homeostatic setpoints could enable both early prevention and durable treatment of hypertension and metabolic disorders, achieving a “two birds with one stone” effect or even partially curing related conditions.

Keywords

homeostasis threshold deviation / neural topological network / physiological homeostasis regulation / hypertension / metabolic diseases

Cite this article

Download citation ▾
Wei Sun, Jin-Yu Sun, Xiangqing Kong. The pathogenic theory of homeostasis threshold deviation (HTD) in wide-range oscillatory physiological parameters: novel perspectives for hypertension and metabolic disease treatment. Life Metabolism, 2025, 4(6): loaf029 DOI:10.1093/lifemeta/loaf029

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Südhof TC , Malenka RC . Understanding synapses:past, present, and future. Neuron 2008; 60: 469- 76.

[2]

Bain A . Mind and body. The theories of their relation. New York: D. Appleton and Company, 1873.

[3]

Kandel ER . The molecular biology of memory storage:a dialogue between genes and synapses. Science 2001; 294: 1030- 8.

[4]

Herculano-Houzel S . The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost. Proc Natl Acad Sci U S A 2012; 109: 10661- 8.

[5]

Lisberger SG . The neural basis for learning of simple motor skills. Science 1988; 242: 728- 35.

[6]

McCulloch WS , Pitts W . A logical calculus of the ideas immanent in nervous activity. 1943. Bull Math Biol 1990; 52: 99- 115; discussion 73.

[7]

Seguin C , Sporns O , Zalesky A . Brain network communication:concepts, models and applications. Nat Rev Neurosci 2023; 24: 557- 74.

[8]

Jeon I , Kim T . Distinctive properties of biological neural networks and recent advances in bottom-up approaches toward a better biologically plausible neural network. Front Comput Neurosci 2023; 17: 1092185.

[9]

Nature Machine Intelligence Editor Office . The new NeuroAI. Nat Mach Intell 2024; 6: 245.

[10]

Pear JJ . Physiological homeostasis and learning. In Encyclopedia of the Sciences of Learning (ed. Seel, N. M.) 2628-31 (Springer US, Boston, MA, 2012).

[11]

Hammel HT . Regulation of internal body temperature. Annu Rev Physiol 1968; 30: 641- 710.

[12]

Joyner MJ , Charkoudian N , Wallin BG . The sympathetic nervous system and blood pressure in humans:individualized patterns of regulation and their implications. Hypertension 2010; 56: 10- 6.

[13]

Diéguez C , Fruhbeck G , López M . Hypothalamic lipids and the regulation of energy homeostasis. Obes Facts 2009; 2: 126- 35.

[14]

Coll AP , Yeo GS . The hypothalamus and metabolism:integrating signals to control energy and glucose homeostasis. Curr Opin Pharmacol 2013; 13: 970- 6.

[15]

Dantzer R . Neuroimmune interactions:from the brain to the immune system and vice versa. Physiol Rev 2018; 98: 477- 504.

[16]

Avena-Koenigsberger A , Misic B , Sporns O . Communication dynamics in complex brain networks. Nat Rev Neurosci 2018; 19: 17- 33.

[17]

Li P , Liu B , Wu X et al. Perirenal adipose afferent nerves sustain pathological high blood pressure in rats. Nat Commun 2022; 13: 3130.

[18]

Tahsili-Fahadan P , Geocadin RG . Heart-brain axis. Circ Res 2017; 120: 559- 72.

[19]

Matsubara Y , Kiyohara H , Teratani T et al. Organ and brain crosstalk:the liver-brain axis in gastrointestinal, liver, and pancreatic diseases. Neuropharmacology 2022; 205: 108915.

[20]

Azzoni R , Marsland BJ . The lung-brain axis:a new frontier in host-microbe interactions. Immunity 2022; 55: 589- 91.

[21]

Agirman G , Yu KB , Hsiao EY . Signaling inflammation across the gut-brain axis. Science 2021; 374: 1087- 92.

[22]

Ohara TE , Hsiao EY . Microbiota-neuroepithelial signalling across the gut-brain axis. Nat Rev Microbiol 2025; 23: 371- 84.

[23]

Hofer U . Gut-brain axis in ageing. Nat Rev Microbiol 2022; 20: 446.

[24]

Madrer N , Perera ND , Uccelli NA et al. Neural metabolic networks:key elements of healthy brain function. J Neurochem 2025; 169: e70084.

[25]

Ferrario CR , Finnell JE . Beyond the hypothalamus:roles for insulin as a regulator of neurotransmission, motivation, and feeding. Neuropsychopharmacology 2023; 48: 232- 3.

[26]

Amorim MR , Wang X , Aung O et al. Leptin signaling in the dorsomedial hypothalamus couples breathing and metabolism in obesity. Cell Rep 2023; 42: 113512.

[27]

Mendrick DL , Diehl AM , Topor LS et al. Metabolic syndrome and associated diseases:from the bench to the clinic. Toxicol Sci 2018; 162: 36- 42.

[28]

Chew NWS , Ng CH , Tan DJH et al. The global burden of metabolic disease:from 2000 to 2019. Cell Metab 2023; 35: 414- 28.e3.

[29]

Zhou B , Carrillo-Larco RM , Danaei G et al. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019:a pooled analysis of 1201 population-representative studies with 104 million participants. Lancet 2021; 398: 957- 80.

[30]

Mosquera-Lopez C , Jacobs PG . Digital twins and artificial intelligence in metabolic disease research. Trends Endocrinol Metab 2024; 35: 549- 57.

RIGHTS & PERMISSIONS

The Author(s). Published by Oxford University Press on behalf of Higher Education Press.

PDF (1160KB)

124

Accesses

0

Citation

Detail

Sections
Recommended

/