Sarcopenia, defined as a progressive loss of muscle mass and function, is typified by mitochondrial dysfunction and loss of mitochondrial resilience. Sarcopenia is associated not only with aging, but also with various metabolic diseases characterized by mitochondrial dyshomeostasis. Pyruvate dehydrogenase kinases (PDKs) are mitochondrial enzymes that inhibit the pyruvate dehydrogenase complex, which controls pyruvate entry into the tricarboxylic acid cycle and the subsequent adenosine triphosphate production required for normal cellular activities. PDK4 is upregulated in mitochondrial dysfunction-related metabolic diseases, especially pathologic muscle conditions associated with enhanced muscle proteolysis and aberrant myogenesis. Increases in PDK4 are associated with perturbation of mitochondria-associated membranes and mitochondrial quality control, which are emerging as a central mechanism in the pathogenesis of metabolic disease-associated muscle atrophy. Here, we review how mitochondrial dysfunction affects sarcopenia, focusing on the role of PDK4 in mitochondrial homeostasis. We discuss the molecular mechanisms underlying the effects of PDK4 on mitochondrial dysfunction in sarcopenia and show that targeting mitochondria could be a therapeutic target for treating sarcopenia.
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Background Type 2 diabetes mellitus (T2DM) is known to be associated with cognitive decline and brain structural changes. This study systematically reviews and estimates human brain volumetric differences and atrophy associated with T2DM.
Methods PubMed, PsycInfo and Cochrane Library were searched for brain imaging studies reporting on brain volume differences between individuals with T2DM and healthy controls. Data were examined using meta-analysis, and association between age, sex, diabetes characteristics and brain volumes were tested using meta-regression.
Results A total of 14,605 entries were identified; after title, abstract and full-text screening applying inclusion and exclusion criteria, 64 studies were included and 42 studies with compatible data contributed to the meta-analysis (n=31,630; mean age 71.0 years; 44.4% male; 26,942 control; 4,688 diabetes). Individuals with T2DM had significantly smaller total brain volume, total grey matter volume, total white matter volume and hippocampal volume (approximately 1% to 4%); meta-analyses of smaller samples focusing on other brain regions and brain atrophy rate in longitudinal investigations also indicated smaller brain volumes and greater brain atrophy associated with T2DM. Meta-regression suggests that diabetes-related brain volume differences start occurring in early adulthood, decreases with age and increases with diabetes duration.
Conclusion T2DM is associated with smaller total and regional brain volume and greater atrophy over time. These effects are substantial and highlight an urgent need to develop interventions to reduce the risk of T2DM for brain health.
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The association of diabetes with Alzheimer's disease biomarkers and vascular burden across European aging and memory clinic cohorts Veerle van Gils, Willemijn J. Jansen, Wiesje M. van der Flier, Pablo Martinez‐Lage, Jakub Hort, Inez H. G. B. Ramakers, Olivier Rouaud, Markku Laakso, Sebastiaan Engelborghs, Julius Popp, Alberto Lleó, Anders Wallin, Magda Tsolaki, Charlotte E. Teunissen, Alzheimer's & Dementia.2025;[Epub] CrossRef
Cerebrovascular mechanisms between type 2 diabetes and Alzheimer's disease: Insights from ultrasound localization microscopy Xuan Ren, Gaobo Zhang, Boqian Zhou, Wenting Gu, Qiuchen Zhu, Xin Liu Medical Physics.2025;[Epub] CrossRef
Cardiometabolic health and risk of dementia and brain atrophy: a community-based prospective cohort study of 0.5 million adults in China Clara Bueno Lopez, Andri Iona, Daniel Avery, Iain Turnbull, Ling Yang, Huaidong Du, Yiping Chen, Ningmei Zhang, Junshi Chen, Pei Pei, Jun Lv, Canquing Yu, Dianjianyi Sun, Liming Li, Derrick Bennett, Cornelia van Dujin, Robert Clarke, Zhengming Chen, Fiona The Lancet Regional Health - Western Pacific.2025; 64: 101743. CrossRef
Hormonal–lipid interplay in metabolic disorders: Evaluating the prolactin–testosterone ratio in diabetic and hypothyroid-diabetic patients with a special emphasis on thyroid dysfunction Mohammad Nadeem Khan, Mandayal Jamatia, Ashok Kumar Thyroid Research and Practice.2025; 21(3): 119. CrossRef
Association between metabolic syndrome, diabetes mellitus, inflammation and cognitive dysfunctions in schizophrenia: a cross-sectional analysis Alexander Kancsev, Marie Anne Engh, András Attila Horváth, Péter Hegyi, Oguz Kelemen, Szabolcs Kéri Schizophrenia.2025;[Epub] CrossRef
The psychological basis of hunger and its dysfunctions Richard J Stevenson Nutrition Reviews.2024; 82(10): 1444. CrossRef
Diabetes, antidiabetic medications and risk of dementia: A systematic umbrella review and meta‐analysis Alvin Kuate Defo, Veselko Bakula, Alessandro Pisaturo, Christopher Labos, Simon S. Wing, Stella S. Daskalopoulou Diabetes, Obesity and Metabolism.2024; 26(2): 441. CrossRef
Cognitive deficits among people with schizophrenia and prediabetes or diabetes Alexander Panickacheril John, Thynn Mya, Darren Haywood Acta Psychiatrica Scandinavica.2024; 149(1): 65. CrossRef
The association of glucose metabolism measures and diabetes status with Alzheimer’s disease biomarkers of amyloid and tau: A systematic review and meta-analysis Veerle van Gils, Marianna Rizzo, Jade Côté, Wolfgang Viechtbauer, Giuseppe Fanelli, Jordi Salas-Salvadó, Theresa Wimberley, Mònica Bulló, Fernando Fernandez-Aranda, Søren Dalsgaard, Pieter Jelle Visser, Willemijn J. Jansen, Stephanie J.B. Vos Neuroscience & Biobehavioral Reviews.2024; 159: 105604. CrossRef
ECHDC3 Variant Regulates the Right Hippocampal Microstructural Integrity and Verbal Memory in Type 2 Diabetes Mellitus Qiyu Zhao, Xin Du, Feng Liu, Yang Zhang, Wen Qin, Quan Zhang Neuroscience.2024; 538: 30. CrossRef
The hemodynamic response function as a type 2 diabetes biomarker: a data-driven approach Pedro Guimarães, Pedro Serranho, João V. Duarte, Joana Crisóstomo, Carolina Moreno, Leonor Gomes, Rui Bernardes, Miguel Castelo-Branco Frontiers in Neuroinformatics.2024;[Epub] CrossRef
What have clinical trials taught us about brain health? Keon-Joo Lee, Hee-Joon Bae Cerebral Circulation - Cognition and Behavior.2024; 6: 100199. CrossRef
Understanding the relationship between type-2 diabetes, MRI markers of neurodegeneration and small vessel disease, and dementia risk: a mediation analysis Leslie Grasset, Eric Frison, Catherine Helmer, Gwénaëlle Catheline, Geneviève Chêne, Carole Dufouil European Journal of Epidemiology.2024; 39(4): 409. CrossRef
Vulnerability of the Hippocampus to Insults: Links to Blood–Brain Barrier Dysfunction Terry L. Davidson, Richard J. Stevenson International Journal of Molecular Sciences.2024; 25(4): 1991. CrossRef
Diabetes mellitus in older persons with neurocognitive disorder: overtreatment prevalence and associated structural brain MRI findings Pauline Putallaz, Laurence Seematter-Bagnoud, Bogdan Draganski, Olivier Rouaud, Hélène Krief, Christophe J. Büla BMC Geriatrics.2024;[Epub] CrossRef
Accelerometer‐Measured Behavior Patterns in Incident Cerebrovascular Disease: Insights for Preventative Monitoring From the UK Biobank Stephanie J. Zawada, Ali Ganjizadeh, Gian Marco Conte, Bart M. Demaerschalk, Bradley J. Erickson Journal of the American Heart Association.2024;[Epub] CrossRef
Effects of glycaemic control on memory performance, hippocampal volumes and depressive symptomology Gulin Yatagan Sevim, Erkan Alkan, Tamara P. Taporoski, Jose E Krieger, Alex C Pereira, Simon L. Evans Diabetology & Metabolic Syndrome.2024;[Epub] CrossRef
The effect of diabetes in the multifaceted relationship between education and cognitive function Constantin Reinke BMC Public Health.2024;[Epub] CrossRef
Analysis of Brain Age Gap across Subject Cohorts and Prediction Model Architectures Lara Dular, Žiga Špiclin Biomedicines.2024; 12(9): 2139. CrossRef
Discovery of High-Risk Clinical Factors That Accelerate Brain Aging in Adults: A Population-Based Machine Learning Study Jing Sun, Luyao Wang, Yiwen Gao, Ying Hui, Shuohua Chen, Shouling Wu, Zhenchang Wang, Jiehui Jiang, Han Lv Research.2024;[Epub] CrossRef
The effects of aerobic exercise on cognitive function in middle-aged and older individuals with type 2 diabetes: A pilot randomized controlled trial Tingting Liu, Wen-Juo Lo, Jie Chen, Jing Wang, Changwei Li Geriatric Nursing.2024; 60: 677. CrossRef
1H NMR-Based Metabolomic Signatures in Rodent Models of Sporadic Alzheimer’s Disease and Metabolic Disorders Ananya Shukla, Khushbhu Meena, Ashish Gupta, Rajat Sandhir ACS Chemical Neuroscience.2024; 15(24): 4478. CrossRef
The gut microbiota‐astrocyte axis: Implications for type 2 diabetic cognitive dysfunction Zi‐Han Li, Ya‐Yi Jiang, Cai‐Yi Long, Qian Peng, Ren‐Song Yue CNS Neuroscience & Therapeutics.2023; 29(S1): 59. CrossRef
NHANES 2011–2014 Reveals Decreased Cognitive Performance in U.S. Older Adults with Metabolic Syndrome Combinations Edgar Díaz-Camargo, Juan Hernández-Lalinde, María Sánchez-Rubio, Yudy Chaparro-Suárez, Liseth Álvarez-Caicedo, Alexandra Fierro-Zarate, Marbel Gravini-Donado, Henry García-Pacheco, Joselyn Rojas-Quintero, Valmore Bermúdez International Journal of Environmental Research and Public Health.2023; 20(7): 5257. CrossRef
People with Diabetes Have Poorer Self-Rated Health (SRH) and Diabetes Moderates the Association between Age and SRH Weixi Kang, Antonio Malvaso Diseases.2023; 11(2): 73. CrossRef
Cognitive dysfunction in diabetes: abnormal glucose metabolic regulation in the brain Shan Zhang, Yueying Zhang, Zhige Wen, YaNan Yang, Tianjie Bu, Xiangwei Bu, Qing Ni Frontiers in Endocrinology.2023;[Epub] CrossRef
Associations of Glucose Metabolism Status with Brain Macrostructure and Microstructure: Findings from the UK Biobank Ruyi Li, Tingting Geng, Lin Li, Qi Lu, Rui Li, Xue Chen, Yunjing Ou, Sen Liu, Xiaoyu Lin, Qingying Tian, Zixin Qiu, Kai Zhu, Ziyue Tang, Kun Yang, An Pan, Gang Liu The Journal of Clinical Endocrinology & Metabolism.2023; 109(1): e234. CrossRef
Association Between Frequency of Social Contact and Brain Atrophy in Community-Dwelling Older People Without Dementia Naoki Hirabayashi, Takanori Honda, Jun Hata, Yoshihiko Furuta, Mao Shibata, Tomoyuki Ohara, Yasuko Tatewaki, Yasuyuki Taki, Shigeyuki Nakaji, Tetsuya Maeda, Kenjiro Ono, Masaru Mimura, Kenji Nakashima, Jun-ichi Iga, Minoru Takebayashi, Toshiharu Ninomiya, Neurology.2023;[Epub] CrossRef
A diagnosis model for brain atrophy using deep learning and MRI of type 2 diabetes mellitus Saba Raoof Syed, Saleem Durai M. A. Frontiers in Neuroscience.2023;[Epub] CrossRef
Diabetes: a tipping point in neurodegenerative diseases Jose A. Santiago, Mridula Karthikeyan, Madison Lackey, Diana Villavicencio, Judith A. Potashkin Trends in Molecular Medicine.2023; 29(12): 1029. CrossRef
Changes in Starburst Amacrine Cells in Mice with Diabetic Retinopathy Jae-Rim Son, Myung-Jun Lee, Chang-Jin Jeon Frontiers in Bioscience-Landmark.2023;[Epub] CrossRef
Association between Type 2 Diabetes Mellitus and Brain Atrophy: A Meta-Analysis (Diabetes Metab J 2022;46:781-802) Tianqi Zhang, Marnie Shaw, Nicolas Cherbuin Diabetes & Metabolism Journal.2022; 46(5): 815. CrossRef
Association between Type 2 Diabetes Mellitus and Brain Atrophy: A Meta-Analysis (Diabetes Metab J 2022;46:781-802) Se Hee Min Diabetes & Metabolism Journal.2022; 46(5): 813. CrossRef
MORPHOFUNCTIONAL CHANGES OF THE BRAIN IN DIABETES MELLITUS A. V. Smirnov, A. I Bisinbekova, T. I Faibisovich Journal of Volgograd State Medical University.2022; 19(3): 3. CrossRef
The insulin pump is an effective glycemic control device those function is analogous to the physiologic regulation of insulin in vivo. When sufficient patient education and proper selection of patients is done, the insulin pump is one of the most effective treatment modalities for diabetic patients. However, various side effects and complications might occur during its application. We report here on an unusual case of diabetic ketoacidosis that was caused by acute inflammatory colitis and insulin pump malfunction. Peculiarly, the cause of pump malfunction was far removed from its mechanical problem. We concluded that the cause of the insulin pump malfunction was due to abdominal subcutaneous fat atrophy and the lipohypertrophied nodules of the patient that developed due to the prolonged usage of the insulin pump.