1,355,209 research outputs found
Brain insulin sensitivity is linked to body fat distribution-the positron emission tomography perspective
No full textSGLT2 Inhibitor Dapagliflozin Increases Skeletal Muscle and Brain Fatty Acid Uptake in Individuals With Type 2 Diabetes: A Randomized Double-Blind Placebo-Controlled Positron Emission Tomography Study
No full textOBJECTIVE The aim of this study was to investigate the impact of the sodium–glucose cotrans-porter 2 (SGLT2) inhibitor dapagliflozin on tissue fatty acid (FA) uptake in the skeletal muscle, brain, small intestine, and subcutaneous and visceral adipose tissue of individuals with type 2 diabetes by using positron emission tomography (PET). RESEARCH DESIGN AND METHODS In a 6-week randomized double-blind placebo-controlled trial, 53 patients with type 2 diabetes treated with metformin received either 10 mg dapagliflozin or placebo daily. Tissue FA uptake was quantified at baseline and end of treatment with PET and the long-chain FA analog radiotracer 14(R,S)-[18F]fluoro-6-thia-heptadecanoic acid. Treatment effects were assessed using ANCOVA, and the results are reported as least square means and 95% CIs for the difference between groups. RESULTS A total of 38 patients (dapagliflozin n = 21; placebo n = 17) completed the study. After 6 weeks, skeletal muscle FA uptake was increased by dapagliflozin compared with placebo (1.0 [0.07, 2.0] lmol 100 g21 min21; P = 0.032), whereas uptake was not significantly changed in the small intestine or visceral or subcutaneous adipose tissue. Dapagliflozin treatment significantly increased whole-brain FA uptake (0.10 [0.02, 0.17] lmol 100 g21 min21; P = 0.01), an effect observed in both gray and white matter regions. CONCLUSIONS Six weeks of treatment with dapagliflozin increases skeletal muscle and brain FA uptake, partly driven by a rise in free FA availability. This finding is in accordance with previous in-direct measurements showing enhanced FA metabolism in response to SGLT2 inhibition and extends the notion of a shift toward increased FA use to muscle and brain
Insulin resistance is associated with enhanced brain glucose uptake during euglycemic hyperinsulinemia: A large-scale PET cohort
No full textOBJECTIVE Whereas insulin resistance is expressed as reduced glucose uptake in peripheral tissues, the relationship between insulin resistance and brain glucose metabolism remains controversial. Our aim was to examine the association of insulin resistance and brain glucose uptake (BGU) during a euglycemic hyperinsulinemic clamp in a large sample of study participants across a wide range of age and insulin sensitivity. RESEARCH DESIGN AND METHODS [18F]-fluorodeoxyglucose positron emission tomography (PET) data from 194 participants scanned under clamp conditions were compiled from a single-center cohort. BGU was quantified by the fractional uptake rate. We examined the association of age, sex,Mvalue from the clamp, steady-state insulin and free fatty acid levels, C-reactive protein levels, HbA1c, and presence of type 2 diabetes with BGU using Bayesian hierarchical modeling. RESULTS Insulin sensitivity, indexed by theMvalue, was associated negatively withBGUin all brain regions, confirming that in insulin-resistant participants BGU was enhanced during euglycemic hyperinsulinemia. In addition, the presence of type 2 diabetes was associated with additional increase in BGU.On the contrary, age was negatively related to BGU. Steady-state insulin levels, C-reactive protein and free fatty acid levels, sex, and HbA1c were not associated with BGU. CONCLUSIONS In this large cohort of participants of either sex across a wide range of age and insulin sensitivity, insulin sensitivity was the best predictor of BGU
On automatic algorithm configuration of vehicle routing problem solvers
No full textMany of the algorithms for solving vehicle routing problems expose parameters that strongly influence the quality of obtained solutions and the performance of the algorithm. Finding good values for these parameters is a tedious task that requires experimentation and experience. Therefore, methods that automate the process of algorithm configuration have received growing attention. In this paper, we present a comprehensive study to critically evaluate and compare the capabilities and suitability of seven state-of-the-art methods in configuring vehicle routing metaheuristics. The configuration target is the solution quality of eight metaheuristics solving two vehicle routing problem variants. We show that the automatic algorithm configuration methods find good parameters for the vehicle route optimization metaheuristics and clearly improve the solutions obtained over default parameters. Our comparison shows that despite some observable differences in configured performance there is no single configuration method that always outperforms the others. However, largest gains in performance can be made by carefully selecting the right configurator. The findings of this paper may give insights on how to effectively choose and extend automatic parameter configuration methods and how to use them to improve vehicle routing solver performance.peerReviewe
Caching in fog radio access networks : modeling, analysis and optimization
No full textAbstract
Edge caching in Fog Radio Access Networks (FRAN) has been an area of key research over the past few years. Although a large number of research works have been carried out, the need for designing and scaling latency-constrained cache-enabled cloud-aided wireless networks requires rethinking of learning-based methodologies focusing on ultra-dense networks. In this regard, the motivation of this thesis is to propose different methodologies to jointly solve the problem of content caching and resource allocation in cloud-aided wireless networks under latency constraints.
Within this work, edge caching for wireless networks is mainly investigated under three cases: a theoretical analysis of content caching problem with storage-bandwidth trade off in Small Cell Networks (SCNs), a learning-based caching in cloud-aided wireless networks, and a latency-aware radio resource optimization in learning-based cloud-aided wireless networks. Optimizing caching strategy of edge nodes, especially for a large number of contents and ultra-dense networks, is extremely challenging due to the spatio-temporal variation of user requests, fronthaul limitations, latency constraints and severe interference. Moreover, due to the absence of inter-base station communications, intelligent learning aided techniques are required to choose optimal caching strategies, resource allocation and user scheduling in a distributed manner. To overcome these challenges in this work, numerous techniques are developed by combining the tools from stochastic geometry, machine learning and Lyapunov optimization.
Extensive sets of simulations were carried out to validate the performance of the proposed methods compared to conventional models that fail to account for the limitations due to network scale, dynamics of queue and channel states, fronthaul capacity, latency constraints and the lack of coordination between network elements. The results show that the proposed methods yield significant gains in terms of delay minimization and rate improvements compared to the conventional models studied in the existing literature. Original papers Tamoor-ul-Hassan, S., Samarakoon, S., Bennis, M., & Latva-aho, M. (2024). Latency-aware radio resource optimization in learning-based cloud-aided small cell wireless networks. IEEE Transactions on Green Communications and Networking, 8(1), 542–558. https://doi.org/10.1109/TGCN.2023.3317128 https://doi.org/10.1109/TGCN.2023.3317128 Tamoor-ul-Hassan, S., Samarakoon, S., Bennis, M., Latva-aho, M., & Hong, C. S. (2018). Learning-based caching in cloud-aided wireless networks. IEEE Communications Letters, 22(1), 137–140. https://doi.org/10.1109/LCOMM.2017.2759270 https://doi.org/10.1109/LCOMM.2017.2759270 Self-archived version Tamoor-ul-Hassan, S., Bennis, M., Nardelli, P. H. J., & Latva-aho, M. (2016). Caching in wireless small cell networks: A storage-bandwidth tradeoff. IEEE Communications Letters, 20(6), 1175–1178. https://doi.org/10.1109/LCOMM.2016.2543698 https://doi.org/10.1109/LCOMM.2016.2543698 Tamoor-ul-Hassan, S., Bennis, M., Nardelli, P. H. J., & Latva-Aho, M. (2015). Modeling and analysis of content caching in wireless small cell networks. 2015 International Symposium on Wireless Communication Systems (ISWCS), 765–769. https://doi.org/10.1109/ISWCS.2015.7454454 https://doi.org/10.1109/ISWCS.2015.7454454 Tiivistelmä
Edge-välimuisti Fog Radio Access Networkissa (FRAN) on ollut keskeinen tutkimusalue muutaman viime vuoden aikana. Vaikka tutkimustöitä on tehty paljon, latenssirajoitteisten välimuistiin perustuvien langattomien verkkojen suunnittelun ja skaalauksen tarve edellyttää oppimiseen perustuvien metodologioiden uudelleen miettimistä ultratiheisiin verkkoihin keskittyen. Tässä suhteessa tämän opinnäytetyön motiivina on ehdottaa erilaisia menetelmiä sisällön välimuistin ja resurssien allokoinnin ongelman ratkaisemiseksi yhdessä pilviavusteisissa langattomissa verkoissa latenssirajoitteissa.
Tässä työssä langattomien verkkojen reunavälimuistia tutkitaan pääasiassa kolmessa tapauksessa: sisällön välimuistiongelman teoreettinen analyysi tallennustilan kaistanleveyden kompromissin kanssa Small Cell Networkissa (SCN), oppimiseen perustuva välimuisti pilviavusteisessa välimuistissa langattomissa verkoissa ja latenssitietoinen radioresurssien optimointi oppimiseen perustuvissa pilviavusteisissa langattomissa verkoissa. Reunasolmujen välimuististrategian optimointi, erityisesti suurelle määrälle sisältöjä ja erittäin tiheitä verkkoja, on äärimmäisen haastavaa käyttäjien pyyntöjen tila-ajallisen vaihtelun, fronthaul-rajoitusten, latenssirajoitusten ja vakavien häiriöiden vuoksi. Lisäksi tukiasemien välisen viestinnän puuttumisen vuoksi tarvitaan älykkäitä oppimista tukevia tekniikoita optimaalisten välimuististrategioiden, resurssien allokoinnin ja käyttäjien ajoituksen valitsemiseksi hajautetusti. Näiden haasteiden voittamiseksi tässä työssä kehitetään lukuisia tekniikoita yhdistämällä työkalut stokastisesta geometriasta, koneoppimisesta ja Ljapunov-optimoinnista.
Laajoja simulaatiosarjoja on tehty ehdotettujen menetelmien suorituskyvyn validoimiseksi verrattuna perinteisiin malleihin, jotka eivät ota huomioon verkon mittakaavasta, jonon ja kanavan tilojen dynamiikasta, etuliikenteen kapasiteetista, latenssirajoitteista ja koordinaation puutteesta johtuvia rajoituksia verkon elementtien välillä. Tulokset osoittavat, että ehdotetut menetelmät tuottavat merkittäviä etuja viiveen minimoinnissa ja nopeuden parannuksissa verrattuna olemassa olevassa kirjallisuudessa tutkittuihin perinteisiin malleihin. Osajulkaisut Tamoor-ul-Hassan, S., Samarakoon, S., Bennis, M., & Latva-aho, M. (2024). Latency-aware radio resource optimization in learning-based cloud-aided small cell wireless networks. IEEE Transactions on Green Communications and Networking, 8(1), 542–558. https://doi.org/10.1109/TGCN.2023.3317128 https://doi.org/10.1109/TGCN.2023.3317128 Tamoor-ul-Hassan, S., Samarakoon, S., Bennis, M., Latva-aho, M., & Hong, C. S. (2018). Learning-based caching in cloud-aided wireless networks. IEEE Communications Letters, 22(1), 137–140. https://doi.org/10.1109/LCOMM.2017.2759270 https://doi.org/10.1109/LCOMM.2017.2759270 Rinnakkaistallennettu versio Tamoor-ul-Hassan, S., Bennis, M., Nardelli, P. H. J., & Latva-aho, M. (2016). Caching in wireless small cell networks: A storage-bandwidth tradeoff. IEEE Communications Letters, 20(6), 1175–1178. https://doi.org/10.1109/LCOMM.2016.2543698 https://doi.org/10.1109/LCOMM.2016.2543698 Tamoor-ul-Hassan, S., Bennis, M., Nardelli, P. H. J., & Latva-Aho, M. (2015). Modeling and analysis of content caching in wireless small cell networks. 2015 International Symposium on Wireless Communication Systems (ISWCS), 765–769. https://doi.org/10.1109/ISWCS.2015.7454454 https://doi.org/10.1109/ISWCS.2015.7454454 Academic dissertation to be presented with the assent of the Doctoral Programme Committee of Information Technology and Electrical Engineering of the University of Oulu for public defence via remote access on 9 December 2024, at 11 a.m.Abstract
Edge caching in Fog Radio Access Networks (FRAN) has been an area of key research over the past few years. Although a large number of research works have been carried out, the need for designing and scaling latency-constrained cache-enabled cloud-aided wireless networks requires rethinking of learning-based methodologies focusing on ultra-dense networks. In this regard, the motivation of this thesis is to propose different methodologies to jointly solve the problem of content caching and resource allocation in cloud-aided wireless networks under latency constraints.
Within this work, edge caching for wireless networks is mainly investigated under three cases: a theoretical analysis of content caching problem with storage-bandwidth trade off in Small Cell Networks (SCNs), a learning-based caching in cloud-aided wireless networks, and a latency-aware radio resource optimization in learning-based cloud-aided wireless networks. Optimizing caching strategy of edge nodes, especially for a large number of contents and ultra-dense networks, is extremely challenging due to the spatio-temporal variation of user requests, fronthaul limitations, latency constraints and severe interference. Moreover, due to the absence of inter-base station communications, intelligent learning aided techniques are required to choose optimal caching strategies, resource allocation and user scheduling in a distributed manner. To overcome these challenges in this work, numerous techniques are developed by combining the tools from stochastic geometry, machine learning and Lyapunov optimization.
Extensive sets of simulations were carried out to validate the performance of the proposed methods compared to conventional models that fail to account for the limitations due to network scale, dynamics of queue and channel states, fronthaul capacity, latency constraints and the lack of coordination between network elements. The results show that the proposed methods yield significant gains in terms of delay minimization and rate improvements compared to the conventional models studied in the existing literature.Tiivistelmä
Edge-välimuisti Fog Radio Access Networkissa (FRAN) on ollut keskeinen tutkimusalue muutaman viime vuoden aikana. Vaikka tutkimustöitä on tehty paljon, latenssirajoitteisten välimuistiin perustuvien langattomien verkkojen suunnittelun ja skaalauksen tarve edellyttää oppimiseen perustuvien metodologioiden uudelleen miettimistä ultratiheisiin verkkoihin keskittyen. Tässä suhteessa tämän opinnäytetyön motiivina on ehdottaa erilaisia menetelmiä sisällön välimuistin ja resurssien allokoinnin ongelman ratkaisemiseksi yhdessä pilviavusteisissa langattomissa verkoissa latenssirajoitteissa.
Tässä työssä langattomien verkkojen reunavälimuistia tutkitaan pääasiassa kolmessa tapauksessa: sisällön välimuistiongelman teoreettinen analyysi tallennustilan kaistanleveyden kompromissin kanssa Small Cell Networkissa (SCN), oppimiseen perustuva välimuisti pilviavusteisessa välimuistissa langattomissa verkoissa ja latenssitietoinen radioresurssien optimointi oppimiseen perustuvissa pilviavusteisissa langattomissa verkoissa. Reunasolmujen välimuististrategian optimointi, erityisesti suurelle määrälle sisältöjä ja erittäin tiheitä verkkoja, on äärimmäisen haastavaa käyttäjien pyyntöjen tila-ajallisen vaihtelun, fronthaul-rajoitusten, latenssirajoitusten ja vakavien häiriöiden vuoksi. Lisäksi tukiasemien välisen viestinnän puuttumisen vuoksi tarvitaan älykkäitä oppimista tukevia tekniikoita optimaalisten välimuististrategioiden, resurssien allokoinnin ja käyttäjien ajoituksen valitsemiseksi hajautetusti. Näiden haasteiden voittamiseksi tässä työssä kehitetään lukuisia tekniikoita yhdistämällä työkalut stokastisesta geometriasta, koneoppimisesta ja Ljapunov-optimoinnista.
Laajoja simulaatiosarjoja on tehty ehdotettujen menetelmien suorituskyvyn validoimiseksi verrattuna perinteisiin malleihin, jotka eivät ota huomioon verkon mittakaavasta, jonon ja kanavan tilojen dynamiikasta, etuliikenteen kapasiteetista, latenssirajoitteista ja koordinaation puutteesta johtuvia rajoituksia verkon elementtien välillä. Tulokset osoittavat, että ehdotetut menetelmät tuottavat merkittäviä etuja viiveen minimoinnissa ja nopeuden parannuksissa verrattuna olemassa olevassa kirjallisuudessa tutkittuihin perinteisiin malleihin
Jay Rasku, a senior biology and art student at Bates College, introduced student
No full textJay Rasku, a senior biology and art student at Bates College, introduced students at the James Longley Elementary School in Lewiston to an environmental education program that included experiential trips into the woods. The culmination of the program was a mural on the school that is nearly 100 feet long. A group of 9- to 15-year-olds on probation and ordered to serve community service for their crimes assisted with the mural. Details
Insulin Resistance Is Associated With Enhanced Brain Glucose Uptake During Euglycemic Hyperinsulinemia: A Large-Scale PET Cohort
No full textObjective: Whereas insulin resistance is expressed as reduced glucose uptake in peripheral tissues, the relationship between insulin resistance and brain glucose metabolism remains controversial. Our aim was to examine the association of insulin resistance and brain glucose uptake (BGU) during a euglycemic hyperinsulinemic clamp in a large sample of study participants across a wide range of age and insulin sensitivity.
Research design and methods: [18F]-fluorodeoxyglucose positron emission tomography (PET) data from 194 participants scanned under clamp conditions were compiled from a single-center cohort. BGU was quantified by the fractional uptake rate. We examined the association of age, sex, M value from the clamp, steady-state insulin and free fatty acid levels, C-reactive protein levels, HbA1c, and presence of type 2 diabetes with BGU using Bayesian hierarchical modeling.
Results: Insulin sensitivity, indexed by the M value, was associated negatively with BGU in all brain regions, confirming that in insulin-resistant participants BGU was enhanced during euglycemic hyperinsulinemia. In addition, the presence of type 2 diabetes was associated with additional increase in BGU. On the contrary, age was negatively related to BGU. Steady-state insulin levels, C-reactive protein and free fatty acid levels, sex, and HbA1c were not associated with BGU.
Conclusions: In this large cohort of participants of either sex across a wide range of age and insulin sensitivity, insulin sensitivity was the best predictor of BGU
Renal Cortical Glucose Uptake Is Decreased in Insulin Resistance and Correlates Inversely With Serum Free-fatty Acids
No full textContext: Studies on human renal metabolism are scanty. Nowadays, functional imaging allows the characterization of renal metabolism in a noninvasive manner. We have recently demonstrated that fluorodeoxyglucose F18 (18F FDG) positron emission tomography can be used to analyze renal glucose uptake (GU) rates, and that the renal cortex is an insulin-sensitive tissue. Objective: To confirm that renal GU is decreased in people with obesity and to test whether circulating metabolites are related to renal GU. Design, Setting and Participants: Eighteen people with obesity and 18 nonobese controls were studied with [18F]FDG positron emission tomography during insulin clamp. Renal scans were obtained ∼60 minutes after [18F]FDG injection. Renal GU was measured using fractional uptake rate and after correcting for residual intratubular [18F]FDG. Circulating metabolites were measured using high-throughput proton nuclear magnetic resonance metabolomics. Results: Cortical GU was higher in healthy nonobese controls compared with people with obesity (4.7 [3.4-5.6] vs 3.1 [2.2-4.3], P = .004, respectively), and it associated positively with the degree of insulin sensitivity (M value) (r = 0.42, P = .01). Moreover, cortical GU was inversely associated with circulating β-OH-butyrate (r = -0.58, P = .009), acetoacetate (r = -0.48, P = .008), citrate (r = −0.44, P = .01), and free fatty acids (r = −0.68, P < .0001), even when accounting for the M value. On the contrary, medullary GU was not associated with any clinical parameters. Conclusion: These data confirm differences in renal cortical GU between people with obesity and healthy nonobese controls. Moreover, the negative correlations between renal cortex GU and free fatty acids, ketone bodies, and citrate are suggestive of substrate competition in the renal cortex
Regulators of central and peripheral insulin sensitivity in humans
No full textInsulin, secreted by pancreatic β-cells, has a preeminent role in regulating metabolism in virtually all tissues. Highly conserved through evolution, intracellular insulin signaling pathways are tightly regulated, but have been recently challenged by drastic changes in our lifestyles. The following impairment in insulin response, termed insulin resistance, is an important feature of type 2 diabetes, a condition that has reached the proportions of a worldwide epidemic.
It is therefore important to gain a better understanding of how insulin resistance develops, what the interplay is between different tissues, and whether pharmacologically enhancing insulin sensitivity is possible. This thesis focuses on gathering novel information about how an inherited failure in insulin signaling (Study I), brain insulin signaling (Study II), and antidiabetic medicine dapagliflozin (Study III) can alter whole-body and tissue-specific insulin sensitivity.
The studies were performed by using positron emission tomography (PET) scans with a glucose analogue tracer ([18F]-FDG) together with systemic (Studies I and III) and central hyperinsulinemia (Study II) to assess changes in tissue insulin-stimulated glucose uptake (GU).
The results from Study I revealed that inherited impairment in skeletal muscle insulin sensitivity resulted in insulin resistance in all key metabolic organs even before the onset of T2D, while also establishing the role of [18F]-FDG-PET in determining the phenotype associated with a rare genetic variant. In Study II, it was demonstrated that brain insulin signaling induced by intranasal insulin does not have a significant effect on GU in peripheral tissues, but results in lower brain GU under mild systemic hyperinsulinemia. While dapagliflozin did not improve insulin sensitivity either (Study III), 8 weeks of treatment reduced liver fat content, as well as subcutaneous and visceral adipose tissue mass, in overweight T2D subjects.
In conclusion, these studies elucidate how a genetic variant primarily impairing skeletal muscle insulin sensitivity, central response to hyperinsulinemia, and treatment with dapagliflozin can affect tissue-specific insulin sensitivity.Haiman β-solujen erittämällä insuliinilla on merkittävä rooli aineenvaihdunnan säätelijänä lähes kaikissa kudoksissa. Solunsisäinen insuliinisignalointi on kehittynyt evoluutiossa varhain ja on tiukasti säädeltyä, eikä ole vielä onnistunut vastaamaan erittäin nopeaan muutokseen elämäntyylissämme. Tästä seuraava kudosten heikentynyt kyky reagoida insuliiniin, eli insuliiniresistenssi, voi johtaa tyypin 2 diabeteksen puhkeamiseen.
Sen vuoksi on tärkeää ymmärtää paremmin miten insuliiniresistenssi syntyy, miten eri kudokset vaikuttavat siihen, ja voidaanko tilannetta parantaa lääkkeellisesti. Tämän väitöskirjan tavoitteena on tutkia miten perinnöllinen vika erityisesti lihaksen insuliinisignaloinnissa (osatyö I), keskushermoston säätely (osatyö II) ja diabeteslääke dapagliflotsiini (osatyö III) voivat vaikuttaa kudosten insuliiniherkkyyteen.
Tutkimuksissa käytettiin positroniemissiotomorafia (PET) –kuvauksia yhdessä glukoosimerkkiaineen ([18F]-FDG) kanssa. Insuliiniherkkyyden mittaamiseksi kuvausten aikana tutkittaville annettiin insuliinia verenkiertoon (osatyöt I ja III) tai nenäsumutteena (osatyö II).
Osatyössä I todettiin ensisijaisesti lihaksen insuliiniherkkyyttä alentavan geenivariantin aiheuttavan insuliiniresistenssiä laajasti myös muissa kudoksissa. Lisäksi tutkimuksella vahvistettiin [18F]-FDG-PET:n roolia harvinaisten geenivarianttien vaikutusten tutkimisessa. Osatyössä II todettiin, ettei nenä-sumutteena annettu insuliini saanut aikaan merkittävää muutosta muiden kudosten glukoosiaineenvaihdunnassa, mutta aivojen glukoosinotto väheni merkittävästi sumutteen jälkeen. Myöskään dapagliflotsiini ei vaikuttanut merkitsevästi kudosten insuliiniherkkyyteen (osatyö III), mutta 8 viikon hoito vähensi maksan ja vartalon rasvan määrää ylipainoisilla tyypin 2 diabetesta sairastavilla tutkittavilla.
Yhdessä nämä tutkimukset tuovat uutta tietoa siitä, miten lihaksen insuliini-resistenssiä aiheuttava geenivariantti, aivojen vaste korkeaan insuliiniannokseen, sekä hoito dapagliflotsiinilla voivat vaikuttaa kudosten insuliiniherkkyyteenei tietoa saavutettavuudest
Effects of 6 weeks of treatment with dapagliflozin, a sodium- glucose co-transporter-2 inhibitor, on myocardial function and metabolism in patients with type 2 diabetes: A randomized, placebo-controlled, exploratory study
No full textAim: To explore the early effects of dapagliflozin on myocardial function and metabolism in patients with type 2 diabetes without heart failure.Materials and methods: Patients with type 2 diabetes on metformin treatment were randomized to double-blind, 6-week placebo or dapagliflozin 10 mg daily treatment. Investigations included cardiac function and structure with myocardial resonance imaging; cardiac oxygen consumption, perfusion and efficiency with [11 C]-acetate positron emission tomography (PET); and cardiac and hepatic fatty acid uptake with [18 F]-6-thia-heptadecanoic acid PET, analysed by ANCOVA as least square means with 95% confidence intervals.Results: Evaluable patients (placebo: n = 24, dapagliflozin: n = 25; 53% males) had a mean age of 64.4 years, a body mass index of 30.2 kg/m2 and an HbA1c of 6.7%. Body weight and HbA1c were significantly decreased by dapagliflozin versus placebo. Dapagliflozin had no effect on myocardial efficiency, but external left ventricular (LV) work (-0.095 [-0.145, -0.043] J/g/min) and LV oxygen consumption were significantly reduced (-0.30 [-0.49, -0.12] J/g/min) by dapagliflozin, although the changes were not statistically significant versus changes in the placebo group. Change in left atrial maximal volume with dapagliflozin versus placebo was -3.19 (-6.32, -0.07) mL/m2 (p = .056). Peak global radial strain decreased with dapagliflozin versus placebo (-3.92% [-7.57%, -0.28%]; p = .035), while peak global longitudinal and circumferential strains were unchanged. Hepatic fatty acid uptake was increased by dapagliflozin versus placebo (0.024 [0.004, 0.044] μmol/g/min; p = .018), while cardiac uptake was unchanged.Conclusions: This exploratory study indicates reduced heart work but limited effects on myocardial function, efficiency and cardiac fatty acid uptake, while hepatic fatty acid uptake increased, after 6 weeks of treatment with dapagliflozin.</p
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