Metabolic syndrome and PET

The metabolic syndrome (MetS) is a cluster of cardiovascular risk factors, including insulin resistance (elevated fasting glucose or type 2 diabetes), dyslipidemia (elevated fasting and postprandial triglyceride levels and/or low HDL), obesity (central or abdominal), proinflammatory and prothrombotic state, and hypertension (HTN). Metabolic syndrome contributes to cardiovascular morbidity and mortality, and chronic kidney disease (CKD, or diabetic kidney disease, DKD).

The glomerular filtration rate is often elevated in early type 2 diabetes, related to changes in the renal vasculature and autoregulation. High plasma concentrations of vasopressin are associated with metabolic syndrome. Epidemiological evidence connects metabolic syndrome, obesity, diabetes, and non-alcoholic fatty liver disease to reduced bone health and osteoporosis (Musso et al., 2013). Impaired microvascular endothelial function precedes the development of hypertension (Noon et al., 1997; Levy et al., 2001), atherosclerosis (Suwaidi et al., 2000; Davignon and Ganz, 2004), and insulin resistance (Serné et al., 2007). Insulin resistance predicts cerebral amyloid accumulation (Ekblad et al., 2018 and 2023) and cognitive decline (Ekblad et al., 2017; Toppala et al., 2019 and 2021).

Low-grade chronic inflammation is characteristic for metabolic syndrome. The amount of monocytes/macrophages and mast cells is increased in the adipose tissue (Gurung et al., 2019). Expanded adipose tissue is a major source of inflammatory cytokines, including leptin, adiponectin, resistin, and visfatin. Higher release of adiponectin from adipose tissue is associated with lower incidence of metabolic syndrome.

Skeletal muscle is responsible for up to 80% of insulin mediated glucose uptake in healthy subjects, but this function is impaired in type 2 diabetes.

Myocardial blood flow (MBF) is higher in subjects with metabolic syndrome; as adenosine-stimulated MBF is similar, the coronary flow reserve (CFR) is reduced (Di Carli et al., 2011).

Obesity

Body mass index (BMI), calculated as person's weight (kg) divided by height squared (m2), is commonly used to measure obesity. A person with BMI<25.0 is considered as non-obese; BMI<30.0 is considered overweight; person with BMI≥30.0 is considered obese, and morbidly obese, if BMI>40.0. BMI does not account for differences in fat distribution, nor does it distinguish between lean and fat mass. Visceral fat can be assessed using MRI and CT. Waist circumference is a relatively reliable measure of abdominal adiposity, and can be used for obesity classification.

Bariatric surgery, including gastric bypass and sleeve gastrectomy, is the most effective treatment for obesity (Sjöström, 2013). The mechanisms behind the weight loss, including incretin and insulin signalling, can be studied with PET (Bini et al., 2021). [18F]FDG PET has shown that gastric bypass surgery decreases cerebral glucose metabolic rate in various brain areas during oral glucose load, while cognitive scores were improved (Dardano et al., 2022). [18F]FTHA PET study has shown that the uptake of saturated fatty acids in brain is elevated in morbid obesity, and is not decreased after bariatric surgery (Rebelos et al., 2020).

Accumulation of ectopic fat in non-adipose tissues such as liver, muscles, and pancreas leads to local inflammation and insulin resistance. Obesity contributes to microvascular dysfunction. Systemic arterial narrowing and venular widening can be observed in retinal microvasculature, as well as the reversion of damage following bariatric surgery (Viljanen et al., 2018). The effects of obesity, insulin resistance, and weight loss on different organs has been studied using PET, for instance glucose and fatty acid metabolism, and perfusion, in the liver, adipose tissue, skeletal muscle, cardiac muscle, and kidneys (Hannukainen et al., 2014; Iozzo, 2015; Rebelos et al., 2019).

The brain, and especially hypothalamus, regulates eating ehaviour, and PET studies on the associations between different neurotransmitter systems and obesity have been conducted (Guzzardi & Iozzo, 2018; Nummenmaa et al., 2018; Pak et al., 2018; Schinke et al., 2019). Many of the neuropeptides are involved in the regulation of energy homeostasis (van der Klaauw, 2018). High fat diet induces inflammation in hypothalamus, and saturated fatty acids promote leptin and insulin resistance (Van Dyken & Lacoste, 2018). Obesity risk-group comparison study has shown increased brain glucose uptake and dysregulated insulin action in brain-liver axis already in pre-obese state in young healthy adults with only mild extra adiposity (Pekkarinen et al., 2022).

Intestinal microbiome is linked to obesity and its complications. Obesity, causing inflammatory and oxygenated state in the gut, may be the main driver of altered gut microbiome functions (de la Cuesta-Zuluaga et al., 2023). Bariatric surgery leads to partial restoration of normal microbiota (Koffert et al., 2020).

Obesity is a risk factor for cognitive decline and Alzheimer's disease (Loef & Walach, 2013; Nguyen et al., 2014; Albanese et al., 2017). Obesity has been linked to worse BBB integrity (Gustafson et al., 2007; Van Dyken & Lacoste, 2018). The low-level chronic inflammation triggers neuroinflammation, exacerbating amyloid-β induced degenerative cascades (Heneka et al., 2015). Higher levels of BMI and insulin resistance are associated with higher cerebral TSPO radioligand uptake in older cognitively impaired individuals (Ekblad et al., 2023). [18F]FDG PET has been used to assess neuronal loss in dementia, but it may not be suitable for studying early phases of brain degeneration where glucose metabolism may be increased due to neuroinflammation. Synaptic density and inflammation could be studied with specific PET radiopharmaceuticals.

Diabetes

Diabetes mellitus (DM) is characterized by hyperglycemia caused by insulin resistance, insufficient insulin secretion, or excessive glucagon secretion. Type 1 diabetes (T1D), or insulin-dependent diabetes mellitus (IDDM) is an autoimmune disorder, where pancreatic β-cells are being destroyed. Type 2 diabetes (T2D), or non-insulin-dependent diabetes mellitus (NIDDM), is strongly associated with metabolic syndrome, characterized by insulin resistance and impaired regulation of glucose levels. Classification into T1D and T2D is based on the subjects ability to secrete insulin and the presence or absence of autoantibodies. Patients with T1D must be treated with insulin already at diagnosis. Patients with T2D are usually treated with dietary regimes, biguanides (such as metformin), or sulfonylureas which stimulate production of insulin, but numerous other options are available (Kerru et al., 2018). T2D accounts for 90-95% of all diabetes cases, but it is also a heterogeneous disease. Patients with latent autoimmune diabetes in the adult (LADA) initially look like having T2D but later develop a T1D phenotype with antibodies against β-cells. LADA accounts for ∼5-10% of diabetes cases, depending on the population. Several genotypes in T2D have been recognized. Based on phenotypes and genotypes, diabetes can be divided into different clusters (Ahlqvist et al., 2018; Udler et al., 2018). T2D is a risk factor for Alzheimer's disease.

Individuals with normal weight and normal glucose tolerance are highly sensitive to insulin in skeletal muscle, white adipose tissue, and liver. Due to its large proportion of body mass, skeletal muscle disposes most of the blood glucose during hyperinsulinemia, except in obese individuals and individuals with type 2 diabetes who are insulin resistant. Decreased glucose uptake in muscle, increased endogenous hepatic glucose production (EGP), and impaired insulin secretion contribute to hyperglycaemia and type 2 diabetes (Honka et al., 2018). Skeletal muscle is insulin resistant also in T1D (Nuutila et al., 1993).

Gestational diabetes, similar to T2D, affects pregnant women, and usually disappears or improves after the delivery.

NAFLD and NASH

Increased fat content in the liver (non-alcoholic fatty liver disease, NAFLD) is typical in individuals with metabolic syndrome. Metabolic dysfunction-associated fatty liver disease (MAFLD) is a more recent term that emphasizes the metabolic risk factors that cause the progression of NAFLD-associated pathology (Eslam et al., 2020); MAFLD can also be caused by excessive alcohol use and viral hepatitis. NAFLD activity score (NAS) is based on histological features in liver biopsy samples, including steatosis, lobular inflammation, hepatocellular ballooning, fibrosis, and others (Kleiner et al., 2005). Prevalence of NAFLD is ∼25% in the adult population (Paul & Davis, 2018). Hepatocellular lipid accumulation, when continued, can lead to nonalcoholic steatohepatitis (NASH), with inflammation and fibrosis. NASH can progress to cirrhosis or hepatocellular carcinoma (Reccia et al., 2017). Alcoholic fatty liver disease has similar disease stages.

Excessive food intake, saturated fatty acids, cholesterol, and fructose contribute to the development of NAFLD and NASH (Reccia et al., 2017; Musso et al., 2018). NASH and NAFLD are reversible, but the last stage, liver cirrhosis (scarred tissue) cannot be reversed, but requires liver transplantation.

In NAFLD the ability of insulin to suppress gluconeogenesis and production of VLDL is impaired, and the release of C-reactive protein (CRP), fibrinogen, and coagulation factors is increased.

Liver fat content in NAFLD and METS is associated with risk for cardiovascular disease (Arulanandran et al., 2015; Laine et al., 2022). In a FDG study, high liver-to-blood ratio was found to predict cardiovascular events in asymptomatic individuals with NAFLD (Moon et al., 2017), but due to low number of events more studies are needed (Dimitriu-Leen & Scholte, 2017).

Liver function can be assessed with SPECT and PET imaging, using [18F]FDGal, or radioligands for ASGP receptor and mitochondrial MC-I. Liver fat content can be assessed using MRS. Fibrosis can be detected with integrin radioligands with PET. Several ultrasound and MRI methods for detecting fibrosis in liver have been developed (Lucero & Brown, 2016; Petitclerc et al., 2017; Di Lascio et al., 2018).

Exercise training improves insulin sensitivity and reduces liver fat. Even short term sprint interval training (SIT) and moderate-intensity continuous training (MICT) reduces liver fat content in pre-diabetics and T2D with increased liver fat, and SIT improves liver glucose uptake (improves insulin sensitivity) as measured with FDG PET (Motiani et al., 2019).


See also:


Literature

Al-Alsheikh A, Alabdulkader S, Miras AD, Goldstone AP. Effects of bariatric surgery and dietary interventions for obesity on brain neurotransmitter systems and metabolism: A systematic review of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) studies. Obesity Rev. 2023: e13620. doi: 10.1111/obr.13620.

Armani A, Berry A, Cirulli F, Caprio M. Molecular mechanisms underlying metabolic syndrome: the expanding role of the adipocyte. FASEB J. 2017; 31(10): 4240-4255. doi: 10.1096/fj.201601125RRR.

Di Carli MF, Charytan D, McMahon GT, Ganz P, Dorbala S, Schelbert HR. Coronary circulatory function in patients with the metabolic syndrome. J Nucl Med. 2011; 52(9): 1369-1377. doi: 10.2967/jnumed.110.082883.

Grundy SM (ed.): Atlas of Atherosclerosis and Metabolic Syndrome, 5th ed., Springer, 2011. doi: 10.1007/978-1-4419-5839-6.

Grundy SM. Metabolic syndrome update. Trends Cardiovasc Med. 2016; 364-373. doi: 10.1016/j.tcm.2015.10.004.

Han TS, Lean ME. A clinical perspective of obesity, metabolic syndrome and cardiovascular disease. JRSM Cardiovasc Dis. 2016; 5:2048004016633371. doi: 10.1177/2048004016633371.

Iozzo P. Metabolic imaging in obesity: underlying mechanisms and consequences in the whole body. Ann N Y Acad Sci. 2015; 1353: 21-40. doi: 10.1111/nyas.12880.

Juonala M, Saarikoski LA, Viikari JS, Oikonen M, Lehtimäki T, Lyytikäinen LP, Huupponen R, Magnussen CG, Koskinen J, Laitinen T, Taittonen L, Kähönen M, Kivimäki M, Raitakari OT. A longitudinal analysis on associations of adiponectin levels with metabolic syndrome and carotid artery intima-media thickness. The Cardiovascular Risk in Young Finns Study. Atherosclerosis 2011; 217(1): 234-239. doi: 10.1016/j.atherosclerosis.2011.03.016.

Karmi A, Iozzo P, Viljanen A, Hirvonen J, Fielding BA, Virtanen K, Oikonen V, Kemppainen J, Viljanen T, Guiducci L, Haaparanta-Solin M, Någren K, Solin O, Nuutila P. Increased brain fatty acid uptake in metabolic syndrome. Diabetes 2010; 59(9): 2171-2177. doi: 10.2337/db09-0138.

Labazi H, Trask AJ. Coronary microvascular disease as an early culprit in the pathophysiology of diabetes and metabolic syndrome. Pharmacol Res. 2017; 123: 114-121. doi: 10.1016/j.phrs.2017.07.004.

Lautamäki R, Borra R, Iozzo P, Komu M, Lehtimäki T, Salmi M, Jalkanen S, Airaksinen KE, Knuuti J, Parkkola R, Nuutila P. Liver steatosis coexists with myocardial insulin resistance and coronary dysfunction in patients with type 2 diabetes. Am J Physiol Endocrinol Metab. 2006; 291(2): E282-E290. doi: 10.1152/ajpendo.00604.2005.

Petersen MC, Shulman GI. Mechanisms of insulin action and insulin resistance. Physiol Rev. 2018; 98(4): 2133-2223. doi: 10.1152/physrev.00063.2017.

Poretsky L (ed.): Principles of Diabetes Mellitus, 3rd ed., Springer, 2017. ISBN 978-3-319-18741-9. doi: 10.1007/978-3-319-18741-9.

Rebelos E: Novel aspects of insulin resistance: focus on the brain. Studies using positron emission tomography. Annales Universitatis Turkuensis Ser D Tom 1498, 2020. http://urn.fi/URN:ISBN:978-951-29-8151-9.

Tune JD, Goodwill AG, Sassoon DJ, Mather KJ. Cardiovascular consequences of metabolic syndrome. Transl Res. 2017; 183: 57-70. doi: 10.1016/j.trsl.2017.01.001.

Yki-Järvinen H. Non-alcoholic fatty liver disease as a cause and consequence of metabolic syndrome. Lancet Diabetes Endocrinol. 2014; 2: 901-910. doi: 10.1016/S2213-8587(14)70032-4.

Zhang X, Lerman LO. The metabolic syndrome and chronic kidney disease. Transl Res. 2017; 183: 14-25. doi: 10.1016/j.trsl.2016.12.004.



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Updated at: 2023-09-15
Created at: 2017-11-07
Written by: Vesa Oikonen