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A Review of the Effects of Glucagon-Like Peptide-1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors on Lean Body Mass in Humans

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A Review of the Effects of Glucagon-Like Peptide-1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors on Lean Body Mass in Humans 
     [Jack Alistair Sargeant](/articles/search_result.php?term_type=authors&term=Jack Alistair Sargeant)1,2*[![orcid](/image/orcid.png)](https://orcid.org/0000-0003-0395-7329 "https://orcid.org/0000-0003-0395-7329"), [Joseph Henson](/articles/search_result.php?term_type=authors&term=Joseph Henson)1,2*[![orcid](/image/orcid.png)](https://orcid.org/0000-0002-3898-7053 "https://orcid.org/0000-0002-3898-7053"), [James Adam King](/articles/search_result.php?term_type=authors&term=James Adam King)2,3, [Thomas Yates](/articles/search_result.php?term_type=authors&term=Thomas Yates)1,2, [Kamlesh Khunti](/articles/search_result.php?term_type=authors&term=Kamlesh Khunti)1,4, [Melanie Jane Davies](/articles/search_result.php?term_type=authors&term=Melanie Jane Davies)1,2[![orcid](/image/orcid.png)](https://orcid.org/0000-0002-9987-9371 "https://orcid.org/0000-0002-9987-9371")
     Endocrinology and Metabolism 2019;34(3):247-262.   
DOI: <https://doi.org/10.3803/EnM.2019.34.3.247>  
Published online: September 26, 2019  ![](https://crossmark-cdn.crossref.org/widget/v2.0/logos/CROSSMARK_Color_horizontal.svg)

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1Diabetes Research Centre, University of Leicester, Leicester, UK.

2NIHR Leicester Biomedical Research Centre, University Hospital of Leicester NHS Trust and the University of Leicester, Leicester, UK.

3National Centre for Sport and Exercise Medicine, Loughborough University, Loughborough, UK.

4NIHR Collaborations for Leadership in Applied Health Research and Care (CLAHRC), Leicester, UK.

     Corresponding author: Melanie Jane Davies. Diabetes Research Centre, Leicester General Hospital, Gwendolen Rd, Leicester, LE5 4PW, UK. Tel: +44-116-258-6481, [melanie.davies@uhl-tr.nhs.uk](mailto:melanie.davies@uhl-tr.nhs.uk)
    *These authors contributed equally to this work.

• Received: September 3, 2019   • Revised: September 10, 2019   • Accepted: September 17, 2019 

Copyright © 2019 Korean Endocrine Society

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  * ABSTRACT
  * INTRODUCTION
  * DEFINITIONS AND MEASUREMENT OF BODY COMPOSITION
  * METHODS
  * RESULTS
  * CONCLUSIONS
  * ACKNOWLEDGMENTS
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#### ABSTRACT

  *     Weight loss is an important goal in the management of several chronic conditions, including type 2 diabetes mellitus, and pharmacological therapies that aid weight loss are appealing. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter 2 inhibitors (SGLT2is) are novel glucose-lowering therapies that have been shown to induce clinically significant reductions in body weight. However, this weight loss may not be attributed solely to fat mass (FM). Given the importance of skeletal muscle and lean body mass (LBM) on cardio-metabolic health and physical function, we reviewed the available literature reporting the effects of GLP-1RAs and SGLT2is on body composition. Results demonstrate that, in most circumstances, the weight loss associated with both therapies predominantly comprises a reduction in FM, although significant heterogeneity exists between studies. In over half of the studies identified, the proportion of LBM reduction ranged between 20% and 50% of total weight lost, which is consistent with diet-induced weight loss and bariatric surgery. No clear differences existed between GLP-1RAs and SGLT2is. Consequently, the loss of LBM and skeletal muscle associated with weight loss induced by GLP-1RAs and SGLT2is warrants attention. Strategies to preserve skeletal muscle and improve physical function, for example through structured exercise, are of great importance.
  * **Keywords** : [Glucagon-like peptide-1 receptor agonists](/articles/search_result.php?term_type=keywords&term=Glucagon-like%20peptide-1%20receptor%20agonists); [Sodium-glucose cotransporter 2 inhibitors](/articles/search_result.php?term_type=keywords&term=Sodium-glucose%20cotransporter%202%20inhibitors); [Diabetes mellitus, type 2](/articles/search_result.php?term_type=keywords&term=Diabetes%20mellitus,%20type%202); [Weight loss](/articles/search_result.php?term_type=keywords&term=Weight%20loss); [Body composition](/articles/search_result.php?term_type=keywords&term=Body%20composition); [Lean body mass](/articles/search_result.php?term_type=keywords&term=Lean%20body%20mass)



#### INTRODUCTION

    Glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter 2 inhibitors (SGLT2is) are novel glucose-lowering therapies that are prominent in the most recent guidelines for the management of hyperglycaemia in type 2 diabetes mellitus (T2DM) and recommended as preferred second-line pharmacological therapies after metformin, alongside ongoing lifestyle management (predominantly diet and physical activity) [1]. They are particularly recommended when, in addition to glucose lowering, there is a compelling need to reduce body weight (BW) [1].
    In large cardiovascular outcome trials, several GLP-1RAs (liraglutide, semaglutide, exenatide once-weekly, albiglutide and dulaglutide) have shown compelling cardiovascular protection in the form of reduced risk of major adverse cardiovascular events, along with some renal benefits [23]. A number of SGLT2is (canagliflozin, dapagliflozin, and empagliflozin) have also shown cardiovascular benefits and greater renal protection, in addition to reduced hospitalization for heart failure [456]. Both classes of therapy also elicit clinically relevant weight loss [1], with liraglutide gaining an independent license (at a higher dose of 3 mg) for use in obesity management. Importantly, the glucose-dependent mechanisms of action of both therapies mean that their beneficial effects come with low risk of hypoglycaemia, particularly when not used with sulphonylureas or insulin [1].
    The dual effects of weight loss and improved glycaemic control associated with GLP-1RAs and SGLT2is are appealing. Obesity and T2DM are intrinsically linked, and each are associated with increased risk of multiple comorbidities [7]. Notably, however, previous studies demonstrate that when BW is reduced through dietary energy restriction, not all of the resulting weight loss can be attributed to fat mass (FM), with approximately 25% to 33% estimated to comprise of reductions in lean body mass (LBM) [89]. This is important because LBM (predominantly comprised of skeletal muscle) has several important functions. It acts as a primary site of glucose disposal (with lower skeletal muscle mass contributing to poorer glycaemic control [10]), and is a strong determinant of resting metabolic rate; and thus loss of skeletal muscle with weight loss may predispose individuals to a greater chance of weight regain [11].
    Lower muscle mass and function, associated with impaired muscular strength and endurance, also result in a higher risk of falls, hospitalisation and physical frailty [1213]. The important association between T2DM and frailty is becoming increasingly recognised [14], with frailty up to five times more likely in individuals with T2DM compared to those without [14151617]. T2DM represents a state of accelerated metabolic ageing, and some of this frailty risk may be underpinned by an increased loss of LBM and function [181920].
    Consequently, whilst weight loss is an important goal in the management of several obesity-associated comorbidities, including T2DM, and pharmacological therapies that support such weight loss are appealing, it is important to understand the impact of these therapies on body composition. The aim of this narrative review is to describe the effects of GLP-1RAs and SGLT2is upon body composition, with a particular focus on LBM and skeletal muscle.

#### DEFINITIONS AND MEASUREMENT OF BODY COMPOSITION

    To interpret changes in body composition, it is important to understand the basic theory and appreciate the advantages and disadvantages of different measurement methods.
    Broadly, body composition measurement divides the body into “compartments” on the basis of differing physical properties, which commonly include FM, fat free mass (FFM), LBM, skeletal muscle mass, bone mineral content and total body water (TBW). Definitions of these terms are provided in Table 1.
    Direct methods of body composition include computed tomography (CT) and magnetic resonance imaging (MRI) [21]. Whilst these techniques have the highest accuracy, they are also expensive, and not widely used in clinical practice. More common measures, which estimate body composition indirectly, include bioelectrical impedance analysis (BIA), air displacement plethysmography (ADP) and dual-energy X-ray absorptiometry (DXA).
    BIA uses a small alternating current to measure body impedance. Estimates of body composition are provided through in-built equations based on assumed impedance of different biological tissues [22]. BIA is quick, easy and relatively inexpensive, but less precise than other available methods and influenced by factors such as hydration status.
    ADP provides an estimate of body composition by combining body volume, measured using the displacement of air within a sealed measurement chamber, with BW to calculate body density. Body density is then used to estimate body composition using pre-defined formulae [23]. ADP is relatively quick to perform and non-invasive, but requires operation by trained personnel to avoid undue error.
    DXA remains a prominent and preferred technique in clinical trials, balancing a high level of accuracy with comparatively lower costs than MRI and CT. DXA provides whole-body and regional estimates of FM, FFM, LBM, and bone mineral content [21]; using a small safe dose of radiation. It is relatively quick and provides much greater accuracy than BIA.

#### METHODS

    We searched for published studies reporting data on the effects of GLP-1RAs and SGLT2is on body composition using the online database, PubMed, from inception to August 2019, using search terms related to GLP-1RAs, SGLT2is, weight loss and body composition. We included studies using DXA, BIA, ADP, MRI, or CT to measure body composition in any human study population before and after 2 or more weeks of GLP-1RA and/or SGLT2i therapy. Where appropriate, the following assumptions were used during data extraction:
    (1) Where data were reported for mean total BW and mean body fat percentage, we estimated mean FM as; BW divided by 100, multiplied by body fat percentage.
    (2) Where mean BW and mean FM were available (including when FM was estimated as above) we estimated FFM as; BW minus FM.
    We used FFM to cautiously infer changes in LBM, with recognition of their subtle differences as outlined in Table 1. Due to variation in techniques used and methods of reporting, we did not sum different body compartments to estimate total LBM, FFM or BW change.

#### RESULTS

    Details of all eligible studies identified in our search can be found in Tables 2,3,4 [242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768]. The following paragraphs provide an overview of the findings for each individual therapy within both drug classes. For ease of reading, medication doses are not included in text unless providing specific context or comparison. Figs. 1, 2 present the relative proportion of weight loss that could be attributed to FM and LBM/FFM with GLP-1RA and SGLT2i therapy, respectively. Our literature search yielded no data for lixisenatide, albiglutide, or ertugliflozin.
Glucagon-like peptide-1 receptor agonists
    We identified 17 studies (two within a single manuscript) reporting changes in body composition with liraglutide (_n_ =10), exenatide (_n_ =5), semaglutide (_n_ =1), or dulaglutide (_n_ =1). Thirteen of these studies used DXA to measure body composition, two used BIA alone, one used a combination of BIA and CT, and one used ADP.

####  Liraglutide

    Four manuscripts, containing data from five studies, reported relative contributions of LBM/FFM to total weight loss with liraglutide therapy, ranging from 30% to 47% [26,28,29,32]. This included data from separate sub-studies contained within two phase 3 trials in the Liraglutide Effect and Action in Diabetes (LEAD) programme. In LEAD 2, there was a step-wise increase in the magnitude of both total weight loss (0.9, 2.0, and 3.2 kg) and the relative contribution of LBM (33%, 40%, and 47%) when liraglutide was prescribed at 0.6, 1.2, and 1.8 mg once-daily for 26 weeks [28]. In LEAD 3, 1.2 mg liraglutide elicited 2.4 kg weight loss after 52 weeks, with 46% comprising LBM. Liraglutide 1.8 mg resulted in a similar magnitude of weight loss at 52 weeks (2.3 kg) but the relative contribution of LBM was considerably higher (65.2%). Two further trials using DXA report similar results in individuals with obesity and T2DM (30% of 5.0 kg weight loss over 12 weeks) [29] and polycystic ovarian syndrome (PCOS; 46% of 5.2 kg weight loss over 26 weeks) [26]. Furthermore, a single study, using BIA, reported 5.6 kg weight loss after 24 weeks of liraglutide therapy in individuals with type 1 diabetes mellitus, of which 41% constituted LBM [32].
    Conversely, two studies report a comparatively smaller contribution of LBM/FFM to total weight loss with 24 weeks liraglutide treatment in overweight/obese individuals with T2DM (16% of 2.5 kg weight loss with 3 mg once-daily), and those with T2DM and non-alcoholic fatty liver disease (NAFLD) (4% of 5.6 kg weight loss) [2531]. Furthermore, two studies report no change or marginal increases in LBM after 24 and 8 weeks of treatment, respectively [2730]. The final manuscript identified reductions in LBM in individuals undergoing liraglutide therapy at 1.2, 1.8, 2.4, and 3.0 mg for 20 weeks, but did not report total BW change for the subset of individuals undergoing body composition assessment [24].

####  Exenatide

    We identified five studies examining the impact of exenatide on body composition, reporting similar, although more heterogeneous, findings than those for liraglutide. Two studies used DXA to assess changes in body composition with 14 weeks of exenatide treatment in individuals with obesity with or without schizophrenia. These studies reported a mean weight loss of 2.0 and 2.3 kg, respectively, of which the relative contribution of FFM to total weight loss was 40% and 52% [3335].
    A separate study reported 3.5 kg weight loss after 16 weeks of treatment in individuals with overweight/obesity and T2DM and reported that 11.4% of weight loss comprised of LBM [36]. One further study reported a small increase (0.3 kg) in LBM after 52 weeks of treatment in individuals with T2DM [34]. The final study identified (utilising BIA to assess changes in body composition over 12 weeks in individuals with obesity and T2DM), did not report changes in LBM, but reported a loss of FM that was greater than the total weight loss induced by exenatide, and a 1.3 kg increase in skeletal muscle [37].

####  Semaglutide

    The only study administering semaglutide reported 5.0 kg weight loss after 12 weeks of treatment in individuals with obesity [38]. Of this weight loss, 20% was estimated, using ADP, to consist of LBM.

####  Dulaglutide

    A single study of dulaglutide reported data from a case series of five individuals assessed before and after 12 weeks of treatment, using BIA [39]. This manuscript did not report total weight loss, but outlined 0.1 and 0.2 kg loss of LBM and skeletal muscle, respectively, in comparison to 1.9 kg loss of FM.
Sodium-glucose cotransporter 2 inhibitors
    We identified 27 studies examining changes in body composition with SGLT2i therapy (canagliflozin=4, dapagliflozin=6, empagliflozin=2, ipragliflozin=7, luseogliflozin=2, tofogliflozin=3, and various=3). Compared to the studies identified for GLP-1RAs, there was much greater use of BIA (17 studies), with the remaining 10 using DXA.

####  Canagliflozin

    The most robust evidence of body composition changes with SGLT2i comes from a pair of randomised controlled trials (RCTs) reporting data on the effects of canagliflozin on DXA-derived body composition in individuals with T2DM [4041]. After 26 weeks, canagliflozin resulted in 2.5 and 3.2 kg weight loss at 100 and 300 mg, respectively, with the relative contribution of weight loss attributed to LBM being 36.0% and 37.5% [40]. At 52 weeks, the magnitude of weight loss was numerically greater than at 26 weeks and similar with both doses (−4.4 and −4.2 kg), but the relative contribution attributed to LBM was lower (20.5% and 26.2%, respectively) [41]. A third study using DXA, resulted in 2.4 kg weight loss in individuals with T2DM, of which almost 46% was LBM [42].
    A single study, using BIA to assess body composition changes over a longer period of time (1 year), reported the contribution of LBM to a total 2.9 kg weight loss in individuals with T2DM and NAFLD to be lower than the studies above, at just 6.9% [43].

####  Dapagliflozin

    Two studies report changes in body composition with dapagliflozin therapy using DXA, providing similar findings to those for canagliflozin. In a sub-study contained within a large multi-site RCT, total weight loss after 104 weeks of dapagliflozin therapy in 69 participants with overweight/obesity and T2DM was 4.5 kg, of which 28.9% consisted of LBM [45]. A smaller, shorter study reported a lower magnitude of weight loss over 12 weeks (2.1 kg) but with a similar relative contribution of LBM (23.8%) [45].
    Three further studies, using BIA, show heterogeneous findings. In a non-randomised trial in which 50 individuals with T2DM were prescribed dapagliflozin or non-SGLT2i therapies for 6 months, the dapagliflozin group lost a mean 3.4 kg of BW, of which 15% constituted LBM [48]. In contrast, a 12-week RCT reported that almost all (94%) of the 3.1 kg weight loss elicited with dapagliflozin was LBM [46], whilst a small cohort study in 11 individuals with T2DM and nonalcoholic steatohepatitis, reported a 1.2 kg increase in LBM after 24 weeks of treatment, despite 3.8 kg weight loss [49].

####  Empagliflozin

    Only two studies report the impact of empagliflozin on body composition. One of these provides DXA-derived data from a sub-study nested within the global EMPA-REG H2H-SU trial; a randomised head-to-head trial conducted as part of the empagliflozin phase 3 programme, which compared empagliflozin with the sulphonylurea glimepiride as second line therapy alongside metformin in individuals with T2DM [50]. Using the weight loss observed in the entire population after 104 weeks (2.8 kg), it was estimated that approximately one-third (32.1%) of weight loss elicited in this trial comprised of FFM.
    A separate study using BIA to assess changes with empagliflozin in women with PCOS reported a loss of FFM (1.1 kg) which was greater than the total weight loss elicited (0.8 kg) [51].

####  Ipragliflozin

    Of the seven studies identified for ipragliflozin, six reported that the contribution of LBM/FFM to total weight loss elicited ranged from 22% to 49% [525354555759]. All of these studies were in individuals with T2DM, and included two studies using DXA, reporting 22% and 49% of 2.8 and 3.5 kg of weight loss over 24 weeks, respectively. The remaining four used BIA and in the largest and longest of these studies (_n_ =217; 104 weeks of treatment), the proportion of weight loss attributed to FFM was approximately one-third (34.5%) [54].
    In the final study identified, 24 weeks of treatment with ipragliflozin resulted in a loss of FM that was greater than the total weight loss in individuals with T2DM and NAFLD, inferring a small gain (0.2 kg) in FFM [56].

####  Luseogliflozin

    Two studies examining the effects of luseogliflozin on body composition using DXA report similar findings to those outlined above. Both studies were in individuals with T2DM, reporting that 12 and 52 weeks of luseogliflozin therapy elicited 2.7 and 3.1 kg weight loss, respectively. Of this weight loss, 41% and 32% could be attributed to a loss of FFM/LBM [5960].

####  Tofogliflozin

    Three similar studies, each utilising BIA, report data regarding the impact of tofogliflozin in individuals with T2DM. Two assessed changes over 12 weeks, reporting 3.3 and 2.9 kg weight loss, of which 30% and 52%, respectively, could be attributed to LBM [6263]. The third study reported 2.3 kg weight loss in 17 individuals with T2DM despite an estimated 0.2 kg increase in FFM [61].

####  Various

    Three cohort studies (two retrospective, one prospective) report data from participants prescribed SGLT2i therapy, with variation in the exact agent prescribed [646566]. Each utilised BIA to assess body composition, but only one study reported changes in both total BW and FFM. In this study, almost half (48%) of the 5.2 kg weight loss elicited by dapagliflozin (_n_ =10) or canagliflozin (_n_ =7), in individuals with overweight/obesity, T2DM and NAFLD, could be attributed to FFM [64].
Dual therapy of GLP-1RAs and SGLT2is in combination
    We identified two studies reporting the effect of GLP-1RAs and SGLT2is in combination. In the first, individuals with obesity and pre-diabetes mellitus were randomised to dapagliflozin-plus-exenatide or placebo for 24 weeks, with an open-label extension (during which all participants underwent combined therapy) for a further 28 weeks [67]. Weight loss after 52 weeks was 5.7 kg, with MRI demonstrating reductions in both adipose tissue and lean tissue volumes (5.3 and 1.4 L, respectively). The second study, using BIA, reported 2.9 kg weight loss after 52 weeks of treatment with luseogliflozin-plus-liraglutide, in individuals with T2DM, of which 14% comprised of LBM [68].

#### CONCLUSIONS

    Our review demonstrates that, in most circumstances, the weight loss associated with GLP-1RA and/or SGLT2i therapy comprises predominantly of a reduction in FM. Within 53 groups of individuals prescribed GLP-1RA, SGLT2i or combined therapy, only five groups saw a loss of LBM, FFM, or skeletal muscle mass (depending on reporting) that was greater than FM lost. Thus, in most cases, body composition (i.e., the ratio of FM to FFM/LBM) was more favorable after treatment than before.
    However, out of 43 groups in which it was possible to estimate the proportion of weight loss that could be attributed to LBM/FFM, in 27 groups this proportion of LBM lost ranged from 20% to 50%. Six groups saw a loss of LBM, FFM, or skeletal muscle but with a smaller relative contribution to total weight loss (0% to 19%), whilst another six reported an increase in LBM, FFM, or skeletal muscle mass. These results are in accordance with studies of diet-induced weight loss and bariatric surgery, which have reported similar proportions of LBM/FFM (approximately 25% to 40%) within total weight loss elicited [89697071].
    There were no clear differences between GLP-1RAs and SGLT2is in the magnitude of weight loss that could be attributed to LBM/FFM. However, it should be noted that the number of studies identified for each class of therapy was relatively small, and these were divided further between multiple therapies within each class. There was also heterogeneity of findings within each drug class and within individual therapies. Reasons for this heterogeneity may include (1) the specific therapy used, (2) the dose or duration of treatment, (3) concomitant therapies alongside GLP-1RA or SGLT2i treatment (e.g., if examined as monotherapy or dual-therapy with other glucose-lowering therapies such as metformin), (4) the clinical diagnoses, background medication usage (i.e., use of sulphonylureas or insulin) and other baseline characteristics of the study population, (5) the technique used to assess body composition (including standardisation procedures prior to measurement), and (6) natural inter-individual variation in responses to weight loss. There are no clear patterns within the data collected in this review, including treatment type, dose or duration and the technique used.
    Although the benefits of weight loss in obesity-associated chronic metabolic disease are well established [7273], these favorable outcomes may be limited by losses in LBM, particularly if this constitutes skeletal muscle. Of the studies identified in this review, 12 reported changes in either LBM or FFM, along with changes in skeletal muscle. In seven of these studies, the loss of skeletal muscle comprised between 55% and 100% of the LBM/FFM lost. In one it was twice as great [40], whilst another reported a loss of skeletal muscle despite an increase in FFM [56]. The mechanism of action of SGLT2i, inducing polyuria alongside glycosuria, means that SGLT2i therapy is also associated with fluid loss [1]. Reductions in TBW may impact on body composition measurement, and it could be speculated that reductions in FFM/LBM with weight loss may reflect reductions in water content. Of the 27 studies identified reporting changes in body composition with SGLT2i therapy, 13 reported changes in TBW, 12 of which reported losses; ranging from 0.2 to 2.4 kg (7% to 100% of total weight loss). It is imperative to stress; however, that water is contained within both adipose- and non-adipose tissues [74], and all of the studies reporting TBW in this review utilised BIA. TBW, as provided by BIA, does not allow distinction between water contained in, and thus lost from, different body compartments (i.e., FM or FFM/LBM). No studies reported change in TBW with GLP-1RA or combination therapy.
    Consequently, although yielding a more favorable body composition, the potential LBM and skeletal muscle loss associated with weight loss induced by GLP-1RAs and SGLT2is warrants attention. A more rapid decline in skeletal muscle and consequential increased risk of sarcopenia is concerning, particularly as individuals prescribed these therapies are usually already vulnerable to an increased risk of physical frailty (i.e., those with T2DM and/or obesity) [75]. In turn, strategies to preserve or increase skeletal muscle and physical function in these individuals (e.g., through structured exercise training), are of importance. It is also important to state that the absolute mass of skeletal muscle is not the only factor to consider, and improving muscular function (strength, endurance, flexibility etc.) remains critical to improve physical function and performance in tasks of daily living; to impact positively on an individual's quality of life.
    Previous studies have shown that markers of physical function (including balance, grip strength, and gait speed) improve following diet-induced weight loss [97677], likely by reducing the biomechanical burden of moving around [78]. However, when diet and exercise are combined, improvements in physical function are greater than that elicited by diet alone, which may be underpinned by preserved or improved skeletal muscle mass and function [979]. Whether the same results are observed when exercise is combined with weight loss elicited by GLP-1RAs and SGLT2i therapy is currently unknown, and requires testing in robust experimental research.
    Collectively, the available evidence suggests that the initiation of GLP-1RA or SGLT2i therapy results in weight loss that is primarily derived of FM. However, this is accompanied by reductions in LBM which are not insignificant and should be considered in parallel to changes in FM and overall BW. Differences between therapies are currently unclear. A substantial proportion of LBM loss may be comprised of skeletal muscle, which may be clinically relevant, particularly in the populations to which these therapies are prescribed. Given the heterogeneity, a more consistent approach to measurement and reporting of body composition in future research would be beneficial. As the prevalence of obesity, physical inactivity and associated co-morbidities, including T2DM, continues to rise, it is imperative to explore strategies to preserve LBM and improve physical function, and particularly their interaction with glucose-lowering strategies that positively impact on weight loss.

**Acknowledgements**

#### ACKNOWLEDGMENTS

    This research was supported by the National Institute for Health Research (NIHR) Leicester Biomedical Research Centre (BRC) and the NIHR Collaboration for Leadership in Applied Health Research and Care–East Midlands (CLAHRC–EM). The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the Department of Health.

#### Article information

    

**CONFLICTS OF INTEREST:** Melanie Jane Davies has acted as a consultant, advisory board member and speaker for Novo Nordisk, Sanofi-Aventis, Lilly, Merck Sharp & Dohme, Boehringer Ingelheim, AstraZeneca and Janssen, an advisory board member for Servier and as a speaker for Mitsubishi Tanabe Pharma Corporation and Takeda Pharmaceuticals International Inc. She has received grants in support of investigator and investigator-initiated trials from Novo Nordisk, Sanofi-Aventis, Lilly, Boehringer Ingelheim, and Janssen.

Kamlesh Khunti has received honoraria from Abbot, AstraZeneca, Berlin-Chemie AG/Menarini Group, Boehringer Ingelheim, Eli Lilly, Janssen, Merck Sharp & Dohme, Novartis, Novo Nordisk, Roche, and Sanofi, and research support from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck Sharp & Dohme, Novartis, Novo Nordisk, Roche, and Sanofi.

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**Fig. 1**

### Relative proportions of fat mass and lean body/fat-free mass within total weight loss elicited by glucagon-like peptide-1 receptor agonist therapy [25262829313233353638].

![enm-34-247-g001.jpg](/upload//thumbnails/enm-34-247-g001.jpg)

**Fig. 2**

### Relative proportions of fat mass and lean body/fat-free mass within total weight loss elicited by sodium-glucose cotransporter 2 inhibitor therapy [4041424344454648505152535455575859606263].

![enm-34-247-g002.jpg](/upload//thumbnails/enm-34-247-g002.jpg)

**Table 1**

### Definitions of Body Composition Compartments

Fat mass |  Mass of all adipose tissue  
---|---  
Fat free mass |  Fat free mass is total body mass minus total fat mass  
Lean body mass |  Lean body mass is fat free mass minus total bone mass  
Skeletal muscle mass |  Skeletal muscle mass is lean body mass minus connective tissue, skin, and other organs  
Total body water |  The summation of intra- and extra-cellular water  
  
**Table 2**

### Studies Reporting Changes in Body Composition with GLP-1RA Therapy

Study (location) |  Study design |  No. prescribed GLP-1RA therapy |  Population |  Dose (all subcutaneous injection) |  Duration, wk |  Tech. used |  Total BW change, kg (% change from baseline) |  FM change, kg |  LBM change, kg |  Skeletal muscle mass change, kg |  Total body water change, kg |  Proportion of weight loss from FM, % |  Proportion of weight loss from LBM, %  
---|---|---|---|---|---|---|---|---|---|---|---|---|---  
Liraglutide |  |  |  |  |  |  |  |  |  |  |  |  |   
Astrup et al. (2012) [24] (Europe; multi-site) |  Six-arm RCT (sub-study) |  15 |  Obesity |  1.2 mg once-daily |  20 |  DXA |  NR |  −6.1 |  −0.5 |  NR |  NR |  - |  -  
13 |  1.8 mg once-daily |  NR |  −5.9 |  −1.5 |  NR |  NR |  - |  -  
15 |  2.4 mg once-daily |  NR |  −7.0 |  −1.3 |  NR |  NR |  - |  -  
15 |  3.0 mg once-daily |  NR |  −6.8 |  −1.1 |  NR |  NR |  - |  -  
Feng et al. (2019) [25] (China) |  Three-arm RCT |  29 |  T2DM and NAFLD |  1.8 mg once-daily (titrated from 0.6 mg as tolerated) |  24 |  DXA |  −5.6 (−7.4%) |  −3.6 |  −0.2 |  NR |  NR |  64.3 |  3.6  
Frossing et al. (2018) [26] (Denmark) |  Two-arm RCT |  44 |  PCOS |  1.8 mg once-daily |  26 |  DXA |  −5.2 (−5.5%) |  −2.6 |  −2.4 |  NR |  NR |  50 |  46.1  
Harder et al. (2004) [27] (Denmark) |  Two-arm RCT |  21 |  T2DM |  0.6 mg once-daily |  8 |  DXA |  −2.1 (−2.0%) |  −1.6 |  +0.6 |  NR |  NR |  76.2 |  LBM increased  
Jendle et al., (2009) [28] (Global; multi-site) |  Five-arm RCT Study 1; LEAD 2 (sub-study) |  35 |  T2DM |  0.6 mg once-daily (with 1.5–2.0 g metformin) |  26 |  DXA |  −0.9 (−1.0%) |  −0.7 |  −0.3 |  NR |  NR |  77.8 |  33.3  
31 |  1.2 mg once-daily (with 1.5–2.0 g metformin) |  −2.0 (−2.3%) |  −1.6 |  −0.8 |  NR |  NR |  80 |  40  
37 |  1.8 mg once-daily (with 1.5–2.0 g metformin) |  −3.2 (−3.6%) |  −2.4 |  −1.5 |  NR |  NR |  75.0 |  46.9  
Three-arm RCT Study 2; LEAD 3 (sub-study) |  23 |  T2DM |  1.2 mg once-daily |  52 |  DXA |  −2.4 (−2.6%) |  −2.0 |  −1.1 |  NR |  NR |  83.3 |  45.8  
20 |  1.8 mg once-daily |  −2.3 (−2.5%) |  −1.0 |  −1.5 |  NR |  NR |  43.5 |  65.2  
Li et al. (2014) [29] (China) |  Case series (prospective) |  31 |  Obesity and T2DM |  1.2 mg once-daily (titrated from 0.6 mg as tolerated) |  12 |  DXA |  −5.0 (−5.5%) |  −3.8 |  −1.5 |  NR |  NR |  76.0 |  30.0  
Perna et al. (2016) [30] (Italy) |  Case series (retrospective) |  9 |  Obesity and T2DM (elderly) |  3 mg once-daily (titrated from 1.2 mg as tolerated) |  24 |  DXA |  −2.0 (−2.3%) |  −1.5 |  +0.1a |  NR |  NR |  75.0 |  FFM increased  
Rondanelli et al. (2016) [31] (Italy) |  Case series (prospective) |  28 |  Overweight/ obese and T2DM |  3 mg once-daily (titrated from 1.2 mg as tolerated) |  24 |  DXA |  −2.5 (−2.6%) |  −2.0 |  −0.4a |  NR |  NR |  80.0 |  16.0  
Dube et al. (2018) [32] (Canada) |  Two-arm RCT |  15 |  T1DM |  1.8 mg once-daily (titrated from 0.6 mg as tolerated) |  24 |  BIA/ CT |  −5.6 (−6.3%) |  −3.3 |  −2.3a |  NR |  NR |  58.9 |  41.1  
Exenatide |  |  |  |  |  |  |  |  |  |  |  |  |   
Bradley et al. (2012) [33] (USA) |  Case series (prospective) |  18 |  Obesity |  10 μg twice-daily (titrated from 5 μg as tolerated) |  14 |  DXA |  −2.0 (−2.0%) |  −1.3 |  −0.8a |  NR |  NR |  65 |  40  
Bunck et al. (2010) [34] (Sweden) |  Two-arm RCT |  29 |  T2DM |  20 μg twice-daily (titrated from 5 μg as tolerated) |  52 |  DXA |  −3.9 (−4.3%) |  −2.4 |  +0.3 |  NR |  NR |  61.5 |  LBM increased  
Ishoy et al. (2017) [35] (Denmark) |  Two-arm RCT |  20 |  Obesity and schizophrenia (treated with anti-psychotic medication) |  2 mg weekly |  14 |  DXA |  −2.3 (−1.9%) |  −1.1 |  −1.2a |  −0.7 |  NR |  47.8 |  52.2  
Yin et al. (2018) [36] (China) |  Two-arm RCT |  19 |  Overweight/ obesity and T2DM |  10 μg twice-daily (titrated from 5 μg as tolerated) |  16 |  DXA |  −3.5 (−4.3%) |  −2.1 |  −0.4 |  NR |  NR |  60 |  11.4  
Hong et al. (2016) [37] (Korea) |  Case series (prospective) |  32 |  Obesity and T2DM |  10 μg twice-daily (titrated from 5 μg as tolerated) |  12 |  BIA |  −1.0 (−1.4%) |  −2.1 |  NR |  +1.3 |  NR |  210 |  -  
Semaglutide |  |  |  |  |  |  |  |  |  |  |  |  |   
Blundell et al. (2017) [38] (UK) |  Randomised, placebo-controlled corss-over trial |  30 |  Obesity |  1.0 mg (titrated up from 0.25 mg) |  12 |  ADP |  −5.0 (−4.9%) |  −3.5 |  −1.1 |  NR |  NR |  70.0 |  22.0  
Dulaglutide |  |  |  |  |  |  |  |  |  |  |  |  |   
Seko et al. (2017) [39] (Japan) |  Case series (retrospective) |  5 |  T2DM and NAFLD |  0.75 mg once-daily |  12 |  BIA |  NR |  −1.9 |  −0.1 |  −0.2 |  −0.1 |  - |  -  
  
    

GLP-1RA, glucagon-like peptide-1 receptor agonist; BW, body weight; FM, fat mass; LBM, lean body mass; RCT, randomised controlled trial; DXA, dual-energy X-ray absorptiometry; NR, not reported; T2DM, type 2 diabetes mellitus; NAFLD, nonalcoholic fatty liver disease; PCOS, polycystic ovarian syndrome; LEAD, Liraglutide Effect and Action in Diabetes phase 3 programme for liraglutide; FFM, fat-free mass; T1DM, type 1 diabetes mellitus; BIA, bioelectrical impedance analysis; CT, computed tomography; ADP, air displacement plethysmography.

aFFM.

**Table 3**

### Studies Reporting Changes in Body Composition with SGLT2i Therapy

Study (location) |  Study design |  No. (in group prescribed SGLT2i therapy) |  Population |  Dose (all once-daily, oral tablet unless otherwise specified) |  Duration |  Tech. used |  Total BW change, kg (% change from baseline) |  FM change, kg |  LBM change, kg |  Skeletal muscle mass change, kg |  Total body water change, kg |  Proportion of weight loss from FM, % |  Proportion of weight loss from LBM, %  
---|---|---|---|---|---|---|---|---|---|---|---|---|---  
Various |  |  |  |  |  |  |  |  |  |  |  |  |   
Arase et al. (2019) [64] (Japan) |  Case series (retrospective) |  17 (DAPA=10; CANA=7) |  Overweight, T2DM and NAFLD |  DAPA: 5 mg |  24 wk |  BIA |  −5.2 (−7.1%) |  −2.7 |  −2.5a |  −0.7 |  −1.2 |  51.9 |  48.1  
CANA: 100 mg  
Kinoshita et al. (2018) [65] (Japan) |  Case series (prospective) |  156 (_n_ =108 for body composition) (LUSEO=52%; TOFO=29%; IPRA=15%; DAPA=4%) |  T2DM |  NR |  12 wk |  BIA |  −2.8 (−3.6%) |  NR |  NR |  −0.1 |  NR |  - |  -  
Seko et al. (2017) [66] (Japan) |  Case series (retrospective) |  24 (_n_ =17 for body composition) (CANA=18; IPRA=6) |  T2DM and biopsy-proven NAFLD/NASH |  CANA: 100 mg |  24 wk |  BIA |  NR |  −2.4 |  −1.1 |  −0.6 |  −0.8 |  - |  -  
IPRA: 50 mg  
Canagliflozin |  |  |  |  |  |  |  |  |  |  |  |  |   
Blonde et al. (2016) [40] Study 2 (Global; multi-site) |  Three-arm RCT (sub-study) |  CANA 100=63 |  T2DM |  100 mg |  26 wk |  DXA |  −2.5 (−2.8%) |  −1.9 |  −0.9 |  NR |  NR |  76 |  36.0  
CANA 300=71 |  300 mg |  −3.2 (−3.4%) |  −2.4 |  −1.2 |  NR |  NR |  75 |  37.5  
Cefalu et al. (2013) [41]b (Global; multi-site) |  Three-arm RCT (sub-study) |  CANA 100=111 |  T2DM |  100 mg |  52 wk |  DXA |  −4.4 (−5.2%) |  −2.9 |  −0.9 |  NR |  NR |  65.9 |  20.5  
CANA 300=102 |  300 mg |  −4.2 (−4.9%) |  −2.5 |  −1.1 |  NR |  NR |  59.5 |  26.2  
Koike et al. (2019) [42] (Japan) |  Case series (prospective) |  38 |  T2DM |  100 mg |  24 wk |  DXA |  −2.4 (−3.3%) |  −1.3 |  −1.1 |  NR |  NR |  54.2 |  45.8  
Inoue et al. (2019) [43] (Japan) |  Case series (prospective) |  20 |  T2DM and NAFLD |  100 mg |  12 mo |  BIA |  −2.9 (−3.5%) |  −2.6 |  −0.2 |  −0.2 |  −0.2 |  89.7 |  6.9  
Dapagliflozin |  |  |  |  |  |  |  |  |  |  |  |  |   
Bolinder et al. (2014) [44] (Europe; multi-site) |  Two-arm RCT |  69 |  Overweight/obese with T2DM |  10 mg (added to open-label metformin) |  104 wk |  DXA |  −4.5 (−4.9%) |  −2.8 |  −1.3 |  NR |  NR |  62.2 |  28.9  
Kosugi et al. (2019) [45] (Japan) |  Case series (prospective) |  26 |  T2DM |  5 mg |  12 wk |  DXA |  −2.1 (−2.9%) |  −1.7 |  −0.5 |  NR |  NR |  81 |  23.8  
Fadini et al. (2017) [46] (Italy) |  Two-arm RCT |  16 |  T2DM |  10 mg |  12 wk |  BIA |  −3.1 (baseline NR) |  −0.1 |  −2.9a |  NR |  −2.4 |  3.2 |  93.5  
Shimizu et al. (2019) [47] (Japan) |  Two-arm RCT |  33 |  T2DM and NAFLD |  5 mg |  24 wk |  BIA |  −2.9 (−3.9%) |  NR |  NR |  −0.9 |  −1.1 |  - |  -  
Sugiyama et al. (2018) [48] (Japan) |  Two-arm non-randomised cohort study |  28 |  T2DM |  5 mg |  6 mo |  BIA |  −3.4 (−4.4%) |  −3.1 |  −0.5 |  −0.2 |  NR |  91.2 |  14.7  
Tobita et al. (2017) [49] (Japan) |  Case series (prospective) |  11 |  T2DM and NASH |  5 mg |  24 wk |  BIA |  −3.8 (−4.8%) |  −6.1 |  1.2 |  0.1 |  1.2 |  160.5 |  LBM increased Fluctuation over 24 wk  
Empagliflozin |  |  |  |  |  |  |  |  |  |  |  |  |   
Ridderstrale et al. (2014) [50] (Global; multi-site) |  Two-arm RCT (sub-study) |  46 |  T2DM |  25 mg |  104 wk |  DXA |  NR (−2.8 kg [–3.4%] in entire EMPA group; _n_ =765) |  −1.6 |  −0.9a |  NR |  NR |  57.1 |  32.1c  
Javed et al. (2019) [51] (UK) |  Two-arm RCT |  19 |  PCOS |  25 mg |  12 wk |  BIA |  −0.8 (−0.8%) |  −0.3 |  −1.1a |  NR |  −0.8 |  37.5 |  137.5  
Ipragliflozin |  |  |  |  |  |  |  |  |  |  |  |  |   
Inoue et al. (2019) [52] (Japan) |  Two-arm RCT |  24 |  T2DM (on insulin therapy) |  50 mg (insulin dose reduced by 20%) |  24 wk |  DXA |  −2.8 (−4.4%) |  −2.1 |  −0.6 |  NR |  NR |  75 |  21.6  
Ohta et al. (2017) [53] (Japan) |  Case series (prospective) |  20 |  T2DM |  50 mg |  24 wk |  DXA |  −3.5 (−4.3%) |  −1.8 |  −1.7 |  NR |  NR |  51.4 |  48.6  
Iemitsu et al. (2019) [54] (Japan) |  Case series (prospective) |  217 |  T2DM |  50 mg |  104 wk |  BIA |  −2.9 (−3.7%) |  −1.9 |  −1.0a |  −0.9 |  −0.7 |  31 |  34.0  
Kato et al. (2017) [55] (Japan) |  Case series (prospective) |  20 |  T2DM |  50 mg |  12 wk |  BIA |  −1.9 (−2.3%) |  −1.3 |  −0.6a |  −0.6 |  −0.7 |  68.4 |  31.6  
Miyake et al. (2018) [56] (Japan) |  Case series (prospective) |  12 |  T2DM+NAFLD |  50 mg |  24 wk |  BIA |  −1.4 (−2.1%) |  −1.6 |  +0.2a |  −0.5 |  NR |  114.3 |  FFM increased  
Osonoi et al. (2018) [57] (Japan) |  Case series (prospective) |  98 |  T2DM |  50 mg |  24 wk |  BIA |  −2.1 (−3.2%) |  −1.5 |  −0.6a |  NR |  NR |  71.4 |  28.6  
Yamamoto et al. (2016) [58] (Japan) |  Case series (prospective) |  24 |  T2DM |  50 mg |  16 wk |  BIA |  −2.5 (−3.3%) |  −1.7 |  −0.6 |  NR |  −0.6 |  68 |  24.0  
Luseogliflozin |  |  |  |  |  |  |  |  |  |  |  |  |   
Bouchi et al. (2017) [59] (Japan) |  Case series (prospective) |  19 |  T2DM |  2.5 mg, titrated up to 5 mg where tolerated and safe |  12 wk |  DXA |  −2.7 (−3.3%) |  −1.4 |  −1.1a |  NR |  NR |  51.9 |  40.7  
Sasaki et al. (2019) [60] (Japan) |  Case series (prospective) |  37 |  T2DM |  2.5 mg, titrated up to 5 mg where tolerated and safe |  52 wk |  DXA |  −3.1 (−3.9%) |  −2.0 |  −1.0 |  NR |  NR |  64.5 |  32.3  
Tofogliflozin |  |  |  |  |  |  |  |  |  |  |  |  |   
Iwahashi et al. (2016) [61] (Japan) |  Case series (prospective) |  17 |  T2DM |  20 mg |  48 wk |  BIA |  −2.3 (−3.0%) |  −2.6 |  +0.2a |  NR |  −0.3 |  113 |  FFM increased  
Kamei et al. (2018) [62] (Japan) |  Case series (retrospective) |  37 |  T2DM |  20 mg |  12 wk |  BIA |  −3.3 (−3.7%) |  −1.9 |  −1.0 |  −0.8 |  −0.9 |  57.6 |  30.3  
Matsuba et al. (2018) [63] (Japan) |  Case series (prospective) |  14 |  T2DM |  20 mg |  12 wk |  BIA |  −2.9 (baseline NR) |  −1.3 |  −1.5 |  −1.4 |  −1.1 |  44.8 |  51.7  
  
    

SGLT2i, sodium-glucose cotransporter 2 inhibitor; BW, body weight; FM, fat mass; LBM, lean body mass; DAPA, dapagliflozin; CANA, canagliflozin; T2DM, type 2 diabetes mellitus; NAFLD, nonalcoholic fatty liver disease; BIA, bioelectrical impedance analysis; LUSEO, luseogliflozin; TOFO, tofogliflozin; IPRA, ipragliflozin; NR, not reported; NASH, nonalcoholic steatohepatitis; RCT, randomised controlled trial; DXA, dual-energy X-ray absorptiometry; EMPA, empagliflozin; PCOS, polycystic ovarian syndrome; FFM, fat-free mass.

aFFM; bSame as “Study 1” in Blonde et al. (2016) [41]; cCalculated using mean weight loss in entire cohort.

**Table 4**

### Studies Reporting Changes in Body Composition with GLP-1RA and SGLT2i Combination Therapy

Drug/author (location) |  Study design |  No. (in group prescribed SGLT2i+ GLP-1RA therapy) |  Population |  Dose (all once-daily, unless otherwise specified; SGLT2i taken as oral tablet, GLP-1RA as subcutaneous injection) |  Duration, wk |  Tech. used |  Total BW change, kg (% change from baseline) |  FM change |  LBM change |  Skeletal muscle mass change, kg |  Total body water change, kg |  Proportion of weight loss from FM, % |  Proportion of weight loss from LBM, %  
---|---|---|---|---|---|---|---|---|---|---|---|---|---  
Dapagliflozin+Exenatide |  |  |  |  |  |  |  |  |  |  |  |  |   
Lundkvist et al. (2017) [67] (Sweden) |  Two-arm RCT |  16 |  Obesity and pre-diabetes mellitus |  DAPA: 10 mg |  52 |  MRI |  −5.7 (−5.4%) |  −5.3 La |  −1.4 L |  NR |  NR |  - |  -  
EXEN: 2 mg weekly  
Luseogliflozin+Liraglutide |  |  |  |  |  |  |  |  |  |  |  |  |   
Seino et al. (2018) [68] (Japan) |  Case series (prospective) (sub-study) |  22 |  T2DM |  LUSEO: 2.5 mg, titrated up to 5 mg where tolerated and safe (_n_ =17 reached 5 mg dose) |  52 |  BIA |  −2.9 (−4.3%) |  −2.5 kg |  −0.4 kg |  NR |  NR |  86.2 |  13.8  
LIRA: 0.6 mg (_n_ =1) or 0.9 mg (_n_ =21)  
  
    

GLP-1RA, glucagon-like peptide-1 receptor agonist; SGLT2i, sodium-glucose cotransporter 2 inhibitor; BW, body weight; FM, fat mass; LBM, lean body mass; RCT, randomised controlled trial; DAPA, dapagliflozin; EXEN, exenatide; MRI, magnetic resonance imaging; NR, not reported; T2DM, type 2 diabetes mellitus; LUSEO, luseogliflozin; LIRA, liraglutide; BIA, bioelectrical impedance analysis.

aTotal adipose tissue.

#### Figure & Data

#### References




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A Review of the Effects of Glucagon-Like Peptide-1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors on Lean Body Mass in Humans

![Image](../upload/thumbnails/enm-34-247-g001.jpg) ![Image](../upload/thumbnails/enm-34-247-g002.jpg)

**Fig. 1** Relative proportions of fat mass and lean body/fat-free mass within total weight loss elicited by glucagon-like peptide-1 receptor agonist therapy [25262829313233353638]. 

**Fig. 2** Relative proportions of fat mass and lean body/fat-free mass within total weight loss elicited by sodium-glucose cotransporter 2 inhibitor therapy [4041424344454648505152535455575859606263]. 

### Fig. 1

![](../upload/thumbnails/enm-34-247-g001.jpg)

### Fig. 2

![](../upload/thumbnails/enm-34-247-g002.jpg)

ALL FIGURES

A Review of the Effects of Glucagon-Like Peptide-1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors on Lean Body Mass in Humans

Fat mass| Mass of all adipose tissue  
---|---  
Fat free mass| Fat free mass is total body mass minus total fat mass  
Lean body mass| Lean body mass is fat free mass minus total bone mass  
Skeletal muscle mass| Skeletal muscle mass is lean body mass minus connective tissue, skin, and other organs  
Total body water| The summation of intra- and extra-cellular water  
Study (location)| Study design| No. prescribed GLP-1RA therapy| Population| Dose (all subcutaneous injection)| Duration, wk| Tech. used| Total BW change, kg (% change from baseline)| FM change, kg| LBM change, kg| Skeletal muscle mass change, kg| Total body water change, kg| Proportion of weight loss from FM, %| Proportion of weight loss from LBM, %  
---|---|---|---|---|---|---|---|---|---|---|---|---|---  
Liraglutide| | | | | | | | | | | | |   
Astrup et al. (2012) [24] (Europe; multi-site)| Six-arm RCT (sub-study)| 15| Obesity| 1.2 mg once-daily| 20| DXA| NR| −6.1| −0.5| NR| NR| -| -  
13| 1.8 mg once-daily| NR| −5.9| −1.5| NR| NR| -| -  
15| 2.4 mg once-daily| NR| −7.0| −1.3| NR| NR| -| -  
15| 3.0 mg once-daily| NR| −6.8| −1.1| NR| NR| -| -  
Feng et al. (2019) [25] (China)| Three-arm RCT| 29| T2DM and NAFLD| 1.8 mg once-daily (titrated from 0.6 mg as tolerated)| 24| DXA| −5.6 (−7.4%)| −3.6| −0.2| NR| NR| 64.3| 3.6  
Frossing et al. (2018) [26] (Denmark)| Two-arm RCT| 44| PCOS| 1.8 mg once-daily| 26| DXA| −5.2 (−5.5%)| −2.6| −2.4| NR| NR| 50| 46.1  
Harder et al. (2004) [27] (Denmark)| Two-arm RCT| 21| T2DM| 0.6 mg once-daily| 8| DXA| −2.1 (−2.0%)| −1.6| +0.6| NR| NR| 76.2| LBM increased  
Jendle et al., (2009) [28] (Global; multi-site)| Five-arm RCT Study 1; LEAD 2 (sub-study)| 35| T2DM| 0.6 mg once-daily (with 1.5–2.0 g metformin)| 26| DXA| −0.9 (−1.0%)| −0.7| −0.3| NR| NR| 77.8| 33.3  
31| 1.2 mg once-daily (with 1.5–2.0 g metformin)| −2.0 (−2.3%)| −1.6| −0.8| NR| NR| 80| 40  
37| 1.8 mg once-daily (with 1.5–2.0 g metformin)| −3.2 (−3.6%)| −2.4| −1.5| NR| NR| 75.0| 46.9  
Three-arm RCT Study 2; LEAD 3 (sub-study)| 23| T2DM| 1.2 mg once-daily| 52| DXA| −2.4 (−2.6%)| −2.0| −1.1| NR| NR| 83.3| 45.8  
20| 1.8 mg once-daily| −2.3 (−2.5%)| −1.0| −1.5| NR| NR| 43.5| 65.2  
Li et al. (2014) [29] (China)| Case series (prospective)| 31| Obesity and T2DM| 1.2 mg once-daily (titrated from 0.6 mg as tolerated)| 12| DXA| −5.0 (−5.5%)| −3.8| −1.5| NR| NR| 76.0| 30.0  
Perna et al. (2016) [30] (Italy)| Case series (retrospective)| 9| Obesity and T2DM (elderly)| 3 mg once-daily (titrated from 1.2 mg as tolerated)| 24| DXA| −2.0 (−2.3%)| −1.5| +0.1a| NR| NR| 75.0| FFM increased  
Rondanelli et al. (2016) [31] (Italy)| Case series (prospective)| 28| Overweight/ obese and T2DM| 3 mg once-daily (titrated from 1.2 mg as tolerated)| 24| DXA| −2.5 (−2.6%)| −2.0| −0.4a| NR| NR| 80.0| 16.0  
Dube et al. (2018) [32] (Canada)| Two-arm RCT| 15| T1DM| 1.8 mg once-daily (titrated from 0.6 mg as tolerated)| 24| BIA/ CT| −5.6 (−6.3%)| −3.3| −2.3a| NR| NR| 58.9| 41.1  
Exenatide| | | | | | | | | | | | |   
Bradley et al. (2012) [33] (USA)| Case series (prospective)| 18| Obesity| 10 μg twice-daily (titrated from 5 μg as tolerated)| 14| DXA| −2.0 (−2.0%)| −1.3| −0.8a| NR| NR| 65| 40  
Bunck et al. (2010) [34] (Sweden)| Two-arm RCT| 29| T2DM| 20 μg twice-daily (titrated from 5 μg as tolerated)| 52| DXA| −3.9 (−4.3%)| −2.4| +0.3| NR| NR| 61.5| LBM increased  
Ishoy et al. (2017) [35] (Denmark)| Two-arm RCT| 20| Obesity and schizophrenia (treated with anti-psychotic medication)| 2 mg weekly| 14| DXA| −2.3 (−1.9%)| −1.1| −1.2a| −0.7| NR| 47.8| 52.2  
Yin et al. (2018) [36] (China)| Two-arm RCT| 19| Overweight/ obesity and T2DM| 10 μg twice-daily (titrated from 5 μg as tolerated)| 16| DXA| −3.5 (−4.3%)| −2.1| −0.4| NR| NR| 60| 11.4  
Hong et al. (2016) [37] (Korea)| Case series (prospective)| 32| Obesity and T2DM| 10 μg twice-daily (titrated from 5 μg as tolerated)| 12| BIA| −1.0 (−1.4%)| −2.1| NR| +1.3| NR| 210| -  
Semaglutide| | | | | | | | | | | | |   
Blundell et al. (2017) [38] (UK)| Randomised, placebo-controlled corss-over trial| 30| Obesity| 1.0 mg (titrated up from 0.25 mg)| 12| ADP| −5.0 (−4.9%)| −3.5| −1.1| NR| NR| 70.0| 22.0  
Dulaglutide| | | | | | | | | | | | |   
Seko et al. (2017) [39] (Japan)| Case series (retrospective)| 5| T2DM and NAFLD| 0.75 mg once-daily| 12| BIA| NR| −1.9| −0.1| −0.2| −0.1| -| -  
Study (location)| Study design| No. (in group prescribed SGLT2i therapy)| Population| Dose (all once-daily, oral tablet unless otherwise specified)| Duration| Tech. used| Total BW change, kg (% change from baseline)| FM change, kg| LBM change, kg| Skeletal muscle mass change, kg| Total body water change, kg| Proportion of weight loss from FM, %| Proportion of weight loss from LBM, %  
---|---|---|---|---|---|---|---|---|---|---|---|---|---  
Various| | | | | | | | | | | | |   
Arase et al. (2019) [64] (Japan)| Case series (retrospective)| 17 (DAPA=10; CANA=7)| Overweight, T2DM and NAFLD| DAPA: 5 mg| 24 wk| BIA| −5.2 (−7.1%)| −2.7| −2.5a| −0.7| −1.2| 51.9| 48.1  
CANA: 100 mg  
Kinoshita et al. (2018) [65] (Japan)| Case series (prospective)| 156 (_n_ =108 for body composition) (LUSEO=52%; TOFO=29%; IPRA=15%; DAPA=4%)| T2DM| NR| 12 wk| BIA| −2.8 (−3.6%)| NR| NR| −0.1| NR| -| -  
Seko et al. (2017) [66] (Japan)| Case series (retrospective)| 24 (_n_ =17 for body composition) (CANA=18; IPRA=6)| T2DM and biopsy-proven NAFLD/NASH| CANA: 100 mg| 24 wk| BIA| NR| −2.4| −1.1| −0.6| −0.8| -| -  
IPRA: 50 mg  
Canagliflozin| | | | | | | | | | | | |   
Blonde et al. (2016) [40] Study 2 (Global; multi-site)| Three-arm RCT (sub-study)| CANA 100=63| T2DM| 100 mg| 26 wk| DXA| −2.5 (−2.8%)| −1.9| −0.9| NR| NR| 76| 36.0  
CANA 300=71| 300 mg| −3.2 (−3.4%)| −2.4| −1.2| NR| NR| 75| 37.5  
Cefalu et al. (2013) [41]b (Global; multi-site)| Three-arm RCT (sub-study)| CANA 100=111| T2DM| 100 mg| 52 wk| DXA| −4.4 (−5.2%)| −2.9| −0.9| NR| NR| 65.9| 20.5  
CANA 300=102| 300 mg| −4.2 (−4.9%)| −2.5| −1.1| NR| NR| 59.5| 26.2  
Koike et al. (2019) [42] (Japan)| Case series (prospective)| 38| T2DM| 100 mg| 24 wk| DXA| −2.4 (−3.3%)| −1.3| −1.1| NR| NR| 54.2| 45.8  
Inoue et al. (2019) [43] (Japan)| Case series (prospective)| 20| T2DM and NAFLD| 100 mg| 12 mo| BIA| −2.9 (−3.5%)| −2.6| −0.2| −0.2| −0.2| 89.7| 6.9  
Dapagliflozin| | | | | | | | | | | | |   
Bolinder et al. (2014) [44] (Europe; multi-site)| Two-arm RCT| 69| Overweight/obese with T2DM| 10 mg (added to open-label metformin)| 104 wk| DXA| −4.5 (−4.9%)| −2.8| −1.3| NR| NR| 62.2| 28.9  
Kosugi et al. (2019) [45] (Japan)| Case series (prospective)| 26| T2DM| 5 mg| 12 wk| DXA| −2.1 (−2.9%)| −1.7| −0.5| NR| NR| 81| 23.8  
Fadini et al. (2017) [46] (Italy)| Two-arm RCT| 16| T2DM| 10 mg| 12 wk| BIA| −3.1 (baseline NR)| −0.1| −2.9a| NR| −2.4| 3.2| 93.5  
Shimizu et al. (2019) [47] (Japan)| Two-arm RCT| 33| T2DM and NAFLD| 5 mg| 24 wk| BIA| −2.9 (−3.9%)| NR| NR| −0.9| −1.1| -| -  
Sugiyama et al. (2018) [48] (Japan)| Two-arm non-randomised cohort study| 28| T2DM| 5 mg| 6 mo| BIA| −3.4 (−4.4%)| −3.1| −0.5| −0.2| NR| 91.2| 14.7  
Tobita et al. (2017) [49] (Japan)| Case series (prospective)| 11| T2DM and NASH| 5 mg| 24 wk| BIA| −3.8 (−4.8%)| −6.1| 1.2| 0.1| 1.2| 160.5| LBM increased Fluctuation over 24 wk  
Empagliflozin| | | | | | | | | | | | |   
Ridderstrale et al. (2014) [50] (Global; multi-site)| Two-arm RCT (sub-study)| 46| T2DM| 25 mg| 104 wk| DXA| NR (−2.8 kg [–3.4%] in entire EMPA group; _n_ =765)| −1.6| −0.9a| NR| NR| 57.1| 32.1c  
Javed et al. (2019) [51] (UK)| Two-arm RCT| 19| PCOS| 25 mg| 12 wk| BIA| −0.8 (−0.8%)| −0.3| −1.1a| NR| −0.8| 37.5| 137.5  
Ipragliflozin| | | | | | | | | | | | |   
Inoue et al. (2019) [52] (Japan)| Two-arm RCT| 24| T2DM (on insulin therapy)| 50 mg (insulin dose reduced by 20%)| 24 wk| DXA| −2.8 (−4.4%)| −2.1| −0.6| NR| NR| 75| 21.6  
Ohta et al. (2017) [53] (Japan)| Case series (prospective)| 20| T2DM| 50 mg| 24 wk| DXA| −3.5 (−4.3%)| −1.8| −1.7| NR| NR| 51.4| 48.6  
Iemitsu et al. (2019) [54] (Japan)| Case series (prospective)| 217| T2DM| 50 mg| 104 wk| BIA| −2.9 (−3.7%)| −1.9| −1.0a| −0.9| −0.7| 31| 34.0  
Kato et al. (2017) [55] (Japan)| Case series (prospective)| 20| T2DM| 50 mg| 12 wk| BIA| −1.9 (−2.3%)| −1.3| −0.6a| −0.6| −0.7| 68.4| 31.6  
Miyake et al. (2018) [56] (Japan)| Case series (prospective)| 12| T2DM+NAFLD| 50 mg| 24 wk| BIA| −1.4 (−2.1%)| −1.6| +0.2a| −0.5| NR| 114.3| FFM increased  
Osonoi et al. (2018) [57] (Japan)| Case series (prospective)| 98| T2DM| 50 mg| 24 wk| BIA| −2.1 (−3.2%)| −1.5| −0.6a| NR| NR| 71.4| 28.6  
Yamamoto et al. (2016) [58] (Japan)| Case series (prospective)| 24| T2DM| 50 mg| 16 wk| BIA| −2.5 (−3.3%)| −1.7| −0.6| NR| −0.6| 68| 24.0  
Luseogliflozin| | | | | | | | | | | | |   
Bouchi et al. (2017) [59] (Japan)| Case series (prospective)| 19| T2DM| 2.5 mg, titrated up to 5 mg where tolerated and safe| 12 wk| DXA| −2.7 (−3.3%)| −1.4| −1.1a| NR| NR| 51.9| 40.7  
Sasaki et al. (2019) [60] (Japan)| Case series (prospective)| 37| T2DM| 2.5 mg, titrated up to 5 mg where tolerated and safe| 52 wk| DXA| −3.1 (−3.9%)| −2.0| −1.0| NR| NR| 64.5| 32.3  
Tofogliflozin| | | | | | | | | | | | |   
Iwahashi et al. (2016) [61] (Japan)| Case series (prospective)| 17| T2DM| 20 mg| 48 wk| BIA| −2.3 (−3.0%)| −2.6| +0.2a| NR| −0.3| 113| FFM increased  
Kamei et al. (2018) [62] (Japan)| Case series (retrospective)| 37| T2DM| 20 mg| 12 wk| BIA| −3.3 (−3.7%)| −1.9| −1.0| −0.8| −0.9| 57.6| 30.3  
Matsuba et al. (2018) [63] (Japan)| Case series (prospective)| 14| T2DM| 20 mg| 12 wk| BIA| −2.9 (baseline NR)| −1.3| −1.5| −1.4| −1.1| 44.8| 51.7  
Drug/author (location)| Study design| No. (in group prescribed SGLT2i+ GLP-1RA therapy)| Population| Dose (all once-daily, unless otherwise specified; SGLT2i taken as oral tablet, GLP-1RA as subcutaneous injection)| Duration, wk| Tech. used| Total BW change, kg (% change from baseline)| FM change| LBM change| Skeletal muscle mass change, kg| Total body water change, kg| Proportion of weight loss from FM, %| Proportion of weight loss from LBM, %  
---|---|---|---|---|---|---|---|---|---|---|---|---|---  
Dapagliflozin+Exenatide| | | | | | | | | | | | |   
Lundkvist et al. (2017) [67] (Sweden)| Two-arm RCT| 16| Obesity and pre-diabetes mellitus| DAPA: 10 mg| 52| MRI| −5.7 (−5.4%)| −5.3 La| −1.4 L| NR| NR| -| -  
EXEN: 2 mg weekly  
Luseogliflozin+Liraglutide| | | | | | | | | | | | |   
Seino et al. (2018) [68] (Japan)| Case series (prospective) (sub-study)| 22| T2DM| LUSEO: 2.5 mg, titrated up to 5 mg where tolerated and safe (_n_ =17 reached 5 mg dose)| 52| BIA| −2.9 (−4.3%)| −2.5 kg| −0.4 kg| NR| NR| 86.2| 13.8  
LIRA: 0.6 mg (_n_ =1) or 0.9 mg (_n_ =21)  
  
**Table 1** Definitions of Body Composition Compartments 

**Table 2** Studies Reporting Changes in Body Composition with GLP-1RA Therapy 

GLP-1RA, glucagon-like peptide-1 receptor agonist; BW, body weight; FM, fat mass; LBM, lean body mass; RCT, randomised controlled trial; DXA, dual-energy X-ray absorptiometry; NR, not reported; T2DM, type 2 diabetes mellitus; NAFLD, nonalcoholic fatty liver disease; PCOS, polycystic ovarian syndrome; LEAD, Liraglutide Effect and Action in Diabetes phase 3 programme for liraglutide; FFM, fat-free mass; T1DM, type 1 diabetes mellitus; BIA, bioelectrical impedance analysis; CT, computed tomography; ADP, air displacement plethysmography.

aFFM.

**Table 3** Studies Reporting Changes in Body Composition with SGLT2i Therapy 

SGLT2i, sodium-glucose cotransporter 2 inhibitor; BW, body weight; FM, fat mass; LBM, lean body mass; DAPA, dapagliflozin; CANA, canagliflozin; T2DM, type 2 diabetes mellitus; NAFLD, nonalcoholic fatty liver disease; BIA, bioelectrical impedance analysis; LUSEO, luseogliflozin; TOFO, tofogliflozin; IPRA, ipragliflozin; NR, not reported; NASH, nonalcoholic steatohepatitis; RCT, randomised controlled trial; DXA, dual-energy X-ray absorptiometry; EMPA, empagliflozin; PCOS, polycystic ovarian syndrome; FFM, fat-free mass.

aFFM; bSame as “Study 1” in Blonde et al. (2016) [41]; cCalculated using mean weight loss in entire cohort.

**Table 4** Studies Reporting Changes in Body Composition with GLP-1RA and SGLT2i Combination Therapy 

GLP-1RA, glucagon-like peptide-1 receptor agonist; SGLT2i, sodium-glucose cotransporter 2 inhibitor; BW, body weight; FM, fat mass; LBM, lean body mass; RCT, randomised controlled trial; DAPA, dapagliflozin; EXEN, exenatide; MRI, magnetic resonance imaging; NR, not reported; T2DM, type 2 diabetes mellitus; LUSEO, luseogliflozin; LIRA, liraglutide; BIA, bioelectrical impedance analysis.

aTotal adipose tissue.

### Table 1

![](../upload/thumbnails/T1-enm-34-247.png)

### Table 2

![](../upload/thumbnails/T2-enm-34-247.png)

### Table 3

![](../upload/thumbnails/T3-enm-34-247.png)

### Table 4

![](../upload/thumbnails/T4-enm-34-247.png)

ALL TABLES

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# metadata

key: description
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Abstract page for arXiv paper 1710.01359v2: Multi-Period Coordinated Management of Electric Vehicles in Zonal Power Markets: A Convex Relaxation Approach


key: og_title
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Multi-Period Coordinated Management of Electric Vehicles in Zonal Power Markets: A Convex Relaxation Approach


key: og_description
value:
In recent years, developments in plug in hybrid electric vehicles have provided various environmental and economic advantages. In the future smart grids, electric vehicles are seen as an important means of transportation to reduce greenhouse gas emissions. One of the main issues regarding to this sort of vehicles is managing their charging time to prevent high peak loads over time. Deploying advanced metering and automatic chargers can be a practical way not only for the vehicle owners to manage their energy consumption, but also for the utilities to manage the electricity load during the day by shifting the charging loads to the off-peak periods. Additionally, an efficient charging schedule can reduce the user's electricity bill cost. In this paper we propose a new coordinated multi-period management model for electric vehicles charging scheduling based on multi-objective approach, aiming at optimizing customers charging cost. In the proposed method, a stochastic model is given for starting time of charging, which makes the method a practical tool for simulating the vehicle owners charging behavior effectively. In order to verify the effectiveness of the proposed method, two market policies are used as case studies. The computation results can be used to evaluate the impact of electric vehicles scheduling on economic performance of smart grid.


key: description
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While the principle of lower emissions behind electric vehicles is commendable, the environmental impact of battery production is still up for debate.


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https://u4d2z7k9.rocketcdn.me/wp-content/uploads/2023/01/Untitled-1024-×-683px-2023-01-10T152926.620.jpg


key: description
value:
Abstract page for arXiv paper 2104.14287v1: Electric cars, assessment of green nature vis a vis conventional fuel driven cars


key: og_description
value:
A comprehensive analysis of energy requirements and emissions associated with electric vehicles, ranging from mining and making the rare-earth magnets required in electric motor to assembling the Li-ion battery, including charging and regular running of the electric vehicles has been performed. A simple, analytical procedure is used to determine the embodied energy and emissions. The objective is to assess the potential of electric cars to reduce green house gases emission to limit global warming to < 1.5 degrees C by the Year 2050 as per IPCC recommendations and also to compare them with conventional fuel driven cars. The combined embodied energy for Nd- and Dy-metals production which are required in electric motors and battery assembly for 150 million cars, projected to be on the road in the year 2050 is ~ 1500 TWh and the CO2 emissions is found to be > 600 MT. The emissions includes carbon intensity of electrical energy required to run these electric vehicles. The projected emissions due to fossil fuels, gasoline production as well as burning it in combustion engines however is only 412 MT, far less than that due to electric vehicles. The main contributor to emissions from electric vehicles is the battery assembling process which releases ~ 379 MT of CO2-e gases. The emissions from both electric vehicles as well as combustion engine vehicles scale linearly with the number of vehicles, indicating that a breakeven is not possible with the currently available manufacturing technologies. These results clearly show that significant technological developments have to take place in electric vehicles so that they become environmentally better placed compared to combustion engine based cars.


key: description
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Abstract page for arXiv paper 2410.09330v1: A Framework to Estimate Life Cycle Emissions for Vehicle-Integrated Photovoltaic Systems


key: og_description
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This paper presents a framework to estimate the environmental impact of solar electric vehicles, accounting for the emissions caused by photovoltaic system production as well as vehicle use. We leverage a cradle-to-gate life cycle assessment to estimate the greenhouse gas emissions of the vehicle-integrated photovoltaic system, from the raw material extraction to the final panel assembly, including the effect of the electricity mix both at the factory location and in the country of use. %the vehicle's life cycle, considering both Furthermore, we modify an existing optimization framework for battery electric vehicles to optimally design a solar electric vehicle and estimate its energy consumption. We showcase our framework by analyzing a case study where the mono-crystalline silicon extraction and refinement processes occur in China, while the final assembly of the panel is in The Netherlands, generating 118 kg of CO2 equivalents per square meter of solar panel. The results suggest that it is generally beneficial to operate solar electric vehicles in countries with a high irradiation index. However, when the local electricity mix already displays a low carbon intensity, the additional emissions introduced by the panel are unnecessary, requiring a longer vehicle lifetime to reach an advantageous emission balance.


key: description
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Abstract page for arXiv paper 2301.03779v1: Estimating required flexibility for secure distribution grid operation considering the uncertainties of EV and PV


key: og_title
value:
Estimating required flexibility for secure distribution grid operation considering the uncertainties of EV and PV


key: og_description
value:
Renewable energy productions and electrification of mobility are promising solutions to reduce greenhouse gas emissions. Their effective integration in a power grid encounters several challenges. The uncertain nature of renewable energy productions as well as stochastic consumptions of electric vehicles introduce remarkable intermittency to a distribution grid and results in bi-uncertain characteristics of both supply and demand sides. One way to verify the secure grid operation within acceptable voltage and loading levels is to assess its required flexibility considering possible boundaries of uncertain variables. In this paper, first a comprehensive linear model of distribution grid considering all pertaining constraints is presented. Then, a flexibility estimation technique is proposed based on the feasibility study of the uncertain space of photovoltaic power productions and load containing electric vehicles. The proposed methodology uses grid monitoring data to determine grid state and to model uncertain parameters. The model is applied on a real low voltage (LV) system equipped with grid monitoring devices.


key: description
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Are electric vehicles really better for the environment? EVs produce fewer emissions, but that’s not the whole story. Read more to understand the pros & cons.


key: og_image
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https://cdn.prod.website-files.com/62733f1361e2ad64f8790984/62740196a573d7ed6af7f8af_622a722904b5fd6cad562585_sustain.life-electric-vehicles-environment-thumb.jpeg


key: description
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Abstract page for arXiv paper 1806.06947v1: Forecasting the value of battery electric vehicles compared to internal combustion engine vehicles: the influence of driving range and battery technology


key: og_title
value:
Forecasting the value of battery electric vehicles compared to internal combustion engine vehicles: the influence of driving range and battery technology


key: og_description
value:
Battery electric vehicles (BEVs) are now clearly a promising candidate in addressing the environmental problems associated with conventional internal combustion engine vehicles (ICEVs). However, BEVs, unlike ICEVs, are still not widely accepted in the automobile market but continuing technological change could overcome this barrier. The aim of this study is to assess and forecast whether and when design changes and technological improvements related to major challenges in driving range and battery cost will make the user value of BEVs greater than the user value of ICEVs. Specifically, we estimate the relative user value of BEVs and ICEVs resulting after design modifications to achieve different driving ranges by considering the engineering trade-offs based on a vehicle simulation. Then, we analyze when the relative user value of BEVs is expected to exceed ICEVs as the energy density and cost of batteries improve because of ongoing technological change. Our analysis demonstrates that the relative value of BEVs is lower than that of ICEVs because BEVs have high battery cost and high cost of time spent recharging despite high torque, high fuel efficiency, and low fuel cost. Moreover, we found the relative value differences between BEVs and ICEVs are found to be less in high performance large cars than in low performance compact cars because BEVs can achieve high acceleration performance more easily than ICEVs. In addition, this study predicts that in approximately 2050, high performance large BEVs could have higher relative value than high performance large ICEVs because of technological improvements in batteries; however low performance compact BEVs are still very likely to have significantly lower user value than comparable ICEVs until well beyond 2050.


key: description
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Abstract page for arXiv paper 2410.17282v1: Drivers of Electric Vehicle Adoption in Nigeria: An Extended UTAUT Framework Approach


key: og_description
value:
Electric vehicles (EVs) represent a significant advancement in automotive technology, utilizing electricity as a power source instead of traditional fossil fuels, while incorporating sophisticated navigation and autopilot systems. These vehicles align with multiple Sustainable Development Goals (SDGs) by offering a more environmentally sustainable alternative to internal combustion engine vehicles (ICEVs). Despite their potential, the adoption of EVs in developing nations such as Nigeria remains constrained. This study expands the Unified Theory of Acceptance and Use of Technology (UTAUT) framework by incorporating key enablers, including poor infrastructure, affordability issues, and government support, within the broader category of facilitating conditions. Additionally, it examines factors such as trust, performance expectations, social influences, and network externalities to identify the primary determinants influencing Nigerian consumers' propensity to adopt EVs. Results show that the percentage increase of H6 (facilitating conditions - behavioral intentions) compared to H5 (network externalities - behavioral intentions) is approximately 32.35%, indicating that traditional drivers significantly influence individuals' willingness to purchase EVs and are particularly strong factors in adoption decisions. The paper concludes with a discussion of these findings and proposes strategies for future research to further explore the barriers and drivers of EV adoption in Nigeria.


key: description
value:
Abstract page for arXiv paper 2312.03137v1: The Role of Net-Zero Energy Residential Buildings in Florida's Energy Transition: Economic Analysis and Technical Benefits


key: og_title
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The Role of Net-Zero Energy Residential Buildings in Florida's Energy Transition: Economic Analysis and Technical Benefits


key: og_description
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Net-zero emission policies, coupled with declining costs of renewables, battery storage, and electric vehicles, require both direct and indirect electrification (such as green hydrogen) of the energy infrastructure to address climate change impacts, improve resilience, and lower energy expenses. In this paper, a comprehensive techno-economic analysis was performed that integrated net-zero energy buildings within the context of Florida energy transition by the year 2050. The analysis compares the monthly cost savings of owning photovoltaic (PV) on the roof, battery storage, and Electric Vehicle (EV) to the cost of buying electricity from the grid for both existing and newly built Orlando homes. The impact of income tax credits (ITC) and energy efficiency improvements on monthly savings was taken into account. The levelized cost of electricity for residential PV and PV + battery systems was determined and extended to provide a cost equivalent to gasoline for an average EV. Rooftop solar fueling an EV for 10,000 miles saves \$100 per month over purchasing gasoline. Today, Florida residents can save on their monthly costs of electricity and gasoline if they have both PV on their roof and a battery system (sized for average daily residence load), and benefit from the federal ITC. Florida residents can cost-effectively transition to their own solar, battery storage, and electric vehicle to home systems. Allowing utility-scale solar to be used for on-site EV fast-charging and hydrogen production through electrolysis for fuel cell vehicles or blending with natural gas, minimizing the need for electricity transmission and distribution costs across the power grids.


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Abstract page for arXiv paper 2112.12769v2: Joint Routing of Conventional and Range-Extended Electric Vehicles in a Large Metropolitan Network


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Range-extended electric vehicles combine the higher efficiency and environmental benefits of battery-powered electric motors with the longer mileage and autonomy of conventional internal combustion engines. This combination is particularly advantageous for time-constrained delivery routing in dense urban areas, where battery recharging along routes can be too time-consuming to economically justify the use of all-electric vehicles. However, switching from electric to conventional fossil fuel modes also results in higher costs and emissions and lower efficiency. This paper analyzes this heterogeneous vehicle routing problem and describes two solution methods: an exact branch-price-and-cut algorithm and an iterated tabu search metaheuristic. From a methodological perspective, we find that the exact algorithm consistently obtains tight lower bounds that also serve to certify the metaheuristic solutions as near-optimal. From a policy standpoint, we examine a large-scale real-world case study concerning parcel deliveries in the Chicago metropolitan area and quantify various operational metrics including energy costs and vehicle miles traveled. We find that by deploying roughly 20% of range-extended vehicles with a modest all-electric range of 33 miles, parcel distributors can save energy costs by up to 17% while incurring less than 0.5% increase in vehicle miles traveled. Increasing the range to 60 miles further reduces costs by only 4%, which can alternatively be achieved by decreasing the average service time by 1 minute or increasing driver working time by 1 hour. Our study reveals several key areas of improvement on which vehicle manufacturers, distributors, and policy makers can focus their attention.


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The rapid transition to electric-drive vehicles is taking place globally. Most automakers are adding electric models to their lineups to prepare for the new electric future. From the analysis of the automotive market, it is evident that there is a growing interest in such vehicles. They are expected to account for half the models released after 2030. Electric-drive vehicles include battery-electric vehicles. As indicated in the research literature and emphasized by experts, electric vehicles (EVs) are supposed to be an environmentally friendly alternative to conventional vehicles. The rising number and variety of EVs contribute to a better understanding of their performance. With more EVs on the market, there are problems to be solved and challenges to overcome. This article is the first part of a two-article series reviewing the strengths and weaknesses of EVs. The article analyzes the environmental effects of EVs at each stage of their life cycle, compares large- and small-scale recycling methods, and explores the potential applications of second-life batteries. This article is an attempt to find out how environmentally friendly EVs are.


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Electric Vehicles—An Overview of Current Issues—Part 1—Environmental Impact, Source of Energy, Recycling, and Second Life of Battery


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https://pub.mdpi-res.com/energies/energies-17-00249/article_deploy/html/images/energies-17-00249-g001-550.jpg?1704269271


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Abstract page for arXiv paper 2410.04540v2: Distribution Grids May Be a Barrier To Residential Electrification


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Replacing fossil-fueled appliances and vehicles with electric alternatives can reduce greenhouse gas emissions and air pollution in many settings. However, residential electrification can raise electricity demand beyond the safe limits of electrical infrastructure, increasing the risk of blackouts or requiring grid reinforcement that can be slow and expensive. Here, we estimate the physical and economic impacts on distribution grids of electrifying all housing and personal vehicles in each county of the lower 48 United States. We find that space heating is the main driver of grid impacts, with the coldest regions seeing demand peaks up to five times higher than today's peaks. Accommodating electrification of all housing and personal vehicles could require up to 544 GW of distribution grid reinforcement nationally, at a cost of \$320 to \$720 billion, or \$2,600 to \$5,800 per household (95% confidence intervals). However, demand-side management can mitigate demand peaks, reducing grid reinforcement costs by up to 77%.


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Electric vehicles (EVs) are an important part of meeting global goals on climate change. They feature prominently in mitigation pathways that limit warming to well-below 2C or 1.5C, which would be inline with the Paris Agreement’s targets.


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What are the environmental impacts of electric vehicles, and how do they compare to traditional cars?


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Answer: Electric vehicles (EVs) have lower emissions of greenhouse gases and pollutants than traditional cars, but their environmental impact depends on various factors. Some of the main environmental impacts of EVs are:

Greenhouse gas emissions: EVs emit fewer greenhouse gases than traditional ...


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Abstract page for arXiv paper 2210.14908v6: Powering Up a Slow Charging Market: How Do Government Subsidies Affect Charging Station Supply?


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Electric vehicle adoption is considered to be a promising pathway for addressing climate change. However, the market for charging stations suffers from a market failure: a lack of EV sales disincentives charging station production, which in turn inhibits mass EV adoption. Charging station subsidies are often discussed as policy levers that can stimulate charging station supply and correct this market failure. Nonetheless, there is limited research examining the extent such subsidies are successful in promoting charging station supply. Using annual data on electric vehicle sales, charging station counts, and subsidy amounts from 57 California counties and a staggered difference-in-differences methodology, I find that charging station subsidies are highly effective: counties that adopt subsidies experience a 36% increase in charging station supply 2 years following subsidy adoption. This finding suggests that governmental intervention can help correct the market failure in the charging station market.


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By the year 2035, the federal government is looking to ban the sales of new gasoline-powered cars in an effort to achieve zero emissions by 2050. To further promote e-mobility, the Biden administration, through the Inflation Reduction Act of 2022, is incentivizing converts by giving tax credits to lower the cost of pur


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By the year 2035, the federal government is looking to ban the sales of new gasoline-powered cars in an effort to achieve zero emissions by 2050. To further promote e-mobility, the Biden administration, through the Inflation Reduction Act of 2022, is incentivizing converts by giving tax credits to lower the cost of purchasing a new or used electric vehicle (EV) – both battery-electric and plug-in hybrid models.
But how "clean" really are EVs?

What are EVs, and how do they work?
Electric vehicles are automobiles powered by electric motors rather than internal combustion engines. Unlike gas-powered cars that run by burning fuel, EVs rely on electricity stored in lithium-ion batteries for energy. When driving, the electric motor uses power from the batteries to propel the vehicle forward. EVs can be charged at home, through public charging stations, or high-speed charging stations.
Learn more on How do Electric cars work in our article.
What are the different kinds of EVs?
There are three main types of EVs:


Battery Electric Vehicles (BEVs): Pure electric vehicles that run solely on electricity stored in large batteries.


Plug-in Hybrid Electric Vehicles (PHEVs): Plug-in hybrids combine an internal combustion engine with a battery, allowing for both electric and gasoline-powered driving.


Hybrid Electric Vehicles (HEVs): Uses a combination of an internal combustion engine and an electric motor but cannot be plugged in for recharging.


How does electric car production affect the environment? 
While electric vehicles are lauded for their eco-friendly operation, the EV production process does have environmental consequences. The extraction and processing of raw materials for electric vehicle batteries, such as lithium and cobalt, can result in habitat disruption and resource depletion. Additionally, manufacturing EV batteries and electric motors requires energy, often sourced from fossil fuels.

However, scientists and car manufacturers have been looking into incorporating recycled raw materials, including valuable metals, in the battery production process. On average, EV batteries can last between 8 to 10 years or 100,000 to 200,000 miles, but some can last longer than the entire life cycle of the EV itself. After the batteries have served their intended purpose, recycling facilities will collect, discharge, dismantle, shred, sort, and refine materials for reuse. The refined materials are used in the production of new batteries or other products, completing the recycling loop.
Benefits of electric cars on the environment
Despite the production-related environmental impacts, electric cars offer several benefits:


Slowing Climate Change: One of the primary advantages of EVs is their contribution to reducing the emission of greenhouse gases. By relying on electricity, which can be generated from renewable sources like solar or wind power, EVs leave a smaller carbon footprint compared to traditional gasoline vehicles.


Reducing Local Air Pollution: Electric cars produce zero tailpipe emissions, improving air quality in urban areas. Traditional vehicles emit pollutants like nitrogen oxides and particulate matter, contributing to respiratory problems and smog. The shift to electric vehicles helps combat these issues.


Less Waste Through Recycling: Electric car battery recycling is a key element in minimizing the environmental impact of EVs. As the industry advances, efficient recycling methods for lithium-ion batteries are being developed, reducing the amount of waste generated from the disposal of used batteries.


Four ways that EVs are better for the environment

Reduced Carbon Emissions
The significant reduction in greenhouse gas emissions from electric vehicles stems from their unique mode of propulsion. Unlike a traditional gasoline car, an electric vehicle produces fewer emissions. The absence of exhaust emissions, particularly carbon dioxide (CO2) and other pollutants associated with burning fossil fuels, is a crucial factor in mitigating climate change. By opting for electric power, EVs directly contribute to lowering the concentration of greenhouse gases in the atmosphere, thereby slowing the pace of global warming and its associated adverse effects on ecosystems and weather patterns.
Energy Efficiency
The inherent efficiency of electric motors is a game-changer in the quest for sustainable transportation. Electric motors boast a higher energy conversion efficiency compared to internal combustion engines. When energy is drawn from the vehicle's power source, be it a battery or a plug-in hybrid system, a more significant proportion of that energy is utilized to propel the vehicle. This increased efficiency translates into less energy waste, reducing the overall demand for electricity and, consequently, the environmental impact associated with energy production. This efficiency is a key element in making EVs a greener alternative to traditional vehicles.
Noise Pollution Reduction
Electric vehicles contribute to a quieter and more serene urban environment due to their near-silent operation. A gas-powered car with an internal combustion engine generates noise through an exhaust system, engine combustion, and mechanical components. In contrast, an EV, propelled by an electric motor, produces minimal noise during operation. This reduction in noise pollution has significant implications for the well-being of urban residents. Quieter streets contribute to improved mental health, reduced stress levels, and an overall enhancement of the urban living experience.
Lower Dependence on Fossil Fuels
As electric vehicles become more prevalent, there is a simultaneous push for a transition in the energy grid from fossil fuels to renewable sources. The electricity used to charge EVs can be sourced from renewable energy, such as solar, wind, or hydropower. This shift is crucial in addressing the environmental impact of traditional vehicles, which rely heavily on fossil fuels and other natural gas sources. By decreasing the dependence on non-renewable resources, EVs play a pivotal role in fostering a more sustainable and eco-friendly energy ecosystem. This transition is not only beneficial for reducing carbon dioxide emissions but also contributes to energy security and resilience against the finite nature of fossil fuel resources.
Electric vs. Petrol Car

To truly know which one is better for the environment, let's have a look at the major differences between electric and petrol cars:


Emissions and Environmental Impact:


Electric Cars: EVs emit zero tailpipe emissions during operation, helping reduce carbon emissions, especially when charged using renewable energy sources. However, EV battery production also creates upstream emissions.


Petrol Cars: Conventional vehicles emit pollutants such as carbon dioxide (CO2), nitrogen oxides (NOx), and particulate matter during combustion, contributing to air pollution and climate change.




Operating Costs:


Electric Cars: EVs generally have lower operating costs per mile/kilometer due to the efficiency of electric motors, fewer moving parts, and lower maintenance requirements. Electricity is often cheaper than petrol.


Petrol Cars: An average new gasoline car may have higher fuel and maintenance costs over the vehicle's lifetime, as petrol prices can vary, and internal combustion engines have more components that can wear out.




Performance:


Electric Cars: EVs provide instant torque, resulting in quick acceleration and a smooth driving experience. Electric motors often offer a more responsive and quiet operation.


Petrol Cars: ICE cars offer a wide range of performance options, from fuel-efficient models to high-performance sports cars. However, internal combustion engines may have a brief lag in power delivery.




Range and Charging:


Electric Cars: EV driving range can vary, but advancements in battery technology are increasing it. EV charging infrastructure is expanding, with options for home charging, public charging stations, and fast charging networks.


Petrol Cars: Gas cars generally have a longer driving range per tank of fuel, and refueling at petrol stations is quick. However, the infrastructure is already established, whereas electric charging infrastructure is still developing.




Infrastructure and Convenience:


Electric Cars : EVs require a charging infrastructure, which is expanding but may not be as ubiquitous as petrol stations. Home charging is convenient for many EV owners.


Petrol Cars: ICE vehicles benefit from a well-established network of petrol stations, offering widespread accessibility for refueling.




Government Incentives:


Electric Cars: In many regions, governments offer incentives such as tax credits, rebates, or reduced registration fees to encourage the adoption of electric vehicles. Additionally, the expanded Inflation Reduction Act grants incentives to anyone who installs qualified charging equipment. The Federal Tax Credit gives individuals 30% off Lectron home charging stations plus installation costs (up to $1,000).


Petrol Cars: Typically do not receive specific incentives related to fuel type, as they are the traditional vehicle type.




Vehicle Variety:


Electric Cars: Offer a growing variety of models, including sedans, SUVs, and even trucks. The market is expanding as technology advances.


Petrol Cars: Have a wide range of models, sizes, and types, providing extensive options for consumers.




Resale Value:


Electric Cars: Historically, EVs have faced concerns about resale value due to rapid advancements in battery technology. However, this trend is changing as batteries become more durable and warranties improve.


Petrol Cars: Generally, resale values are influenced by factors such as brand reputation, model popularity, and overall condition.




Are hybrid cars just as good for the environment?
In the context of hybrid vs electric car, although hybrid vehicles are known for their enhanced fuel efficiency, they fall short in environmental benefits when compared to pure electric vehicles. This is due to hybrids' dependence on internal combustion engines, which makes them less efficient in lowering greenhouse gas emissions and carbon pollution than their fully electric counterparts.
FAQs


Can Electric Vehicles Cut Pollution?

Yes, electric vehicles can cut pollution significantly. By relying on electricity, which can be generated from renewable sources, EVs produce fewer emissions compared to traditional vehicles, reducing both air pollution and greenhouse gas emissions.





What are the disadvantages of electric cars on the environment?

The main disadvantages include the environmental impact of battery production, extraction of raw materials, and energy sourcing for manufacturing. However, ongoing advancements in technology and recycling methods aim to address these concerns.





Are electric cars better for the environment?

Overall, electric cars are better for the environment when considering their operational benefits, such as reduced emissions and energy efficiency. However, it's essential to address the environmental impacts associated with their production and disposal. Even after taking battery manufacture into consideration, electric cars are still the more environmentally friendly option.





Does cold weather affect EV cars?

Cold weather can affect the range of electric vehicles due to the impact on battery performance. Batteries may experience reduced efficiency in low temperatures, leading to a temporary decrease in driving range. However, advancements in battery technology are continuously improving EV performance in various weather conditions.


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Abstract page for arXiv paper 2012.04773v2: Effects of Electric Vehicle Adoption for State-Wide Intercity Trips on Emission Saving and Energy Consumption


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Effects of Electric Vehicle Adoption for State-Wide Intercity Trips on Emission Saving and Energy Consumption


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Electric vehicles (EVs) are considered as sustainable alternatives to conventional vehicles, as they reduce emission and fossil fuel dependency. A recent study has proposed a charging infrastructure planning tool to support intercity trips for the estimated EV market share (6 percent) in Michigan for 2030. The main goal of this study is to estimate the emission reduction associated with this electrification rate and infrastructure investment for light duty vehicles. To this end, a state-of-the-art emission estimation framework is proposed to be applied to the state-wide intercity travels. The main contributions of the proposed framework includes: 1) Incorporating a micro emission estimation model for simulated vehicle trajectories of the intercity network of Michigan, 2) Adjusting the micro emission model results considering impacts of monthly travel demand and temperature variations, and heterogeneity of vehicles based on their make, model, and age. The emission estimation framework is then compared with the traditional VMT analysis method as a benchmark. Finally, five different scenarios are explored for EV adoption to assess potential emission savings from the given electrification rate for each scenario. The results suggest an annual CO2 emission savings of 0.58-0.92 million-ton. The CO2 social cost savings may justify the investment on the network electrification. Note that only 3.7 to 8.6 percent of the total EV energy requirements must be provided via the DC fast charger network proposed by the charging infrastructure planning tool. This requires annual energy consumption of 22.15 to 51.76 BWh for the estimated EV market share in Michigan for 2030.


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Abstract page for arXiv paper 2403.13373v1: Charged Momentum: Electric Vehicle Surge in India's 2023 Landscape


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Electric vehicles (EVs) have emerged as a transformative force in India's transportation sector, offering a sustainable solution to the country's growing energy and environmental challenges. Against the backdrop of rapid urbanization, rising pollution levels, and the need for energy security, EVs have gained traction as a viable alternative to traditional internal combustion engine vehicles. This paper provides a comprehensive analysis of the electric vehicle market in India, focusing particularly on the landscape of 2023. It emphasizes key aspects such as the 2023 scenario of EV adoption, the role of indigenous manufacturers, dominant players shaping the market, and the influence of government policies and initiatives, including the FAME I and II schemes. Furthermore, the paper delves into EV sales data for the fiscal year 2023, offering insights into market trends and consumer preferences. By elucidating the current state of EVs in India, this paper aims to contribute to a deeper understanding of the country's transition towards sustainable mobility and its implications for energy, environment, and economy.


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Electric cars are becoming mainstream, but does that mean they’re better for the environment? According to the experts, yes. Read our article to learn more.


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https://images.prismic.io/greenly/4352e912-ce01-4e59-8be4-4fbe7f533dbd_train+v+car+article+image2.jpg?auto=format,compress


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Abstract page for arXiv paper 2404.01358v1: Utilizing AI and Social Media Analytics to Discover Adverse Side Effects of GLP-1 Receptor Agonists


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Adverse side effects (ASEs) of drugs, revealed after FDA approval, pose a threat to patient safety. To promptly detect overlooked ASEs, we developed a digital health methodology capable of analyzing massive public data from social media, published clinical research, manufacturers' reports, and ChatGPT. We uncovered ASEs associated with the glucagon-like peptide 1 receptor agonists (GLP-1 RA), a market expected to grow exponentially to $133.5 billion USD by 2030. Using a Named Entity Recognition (NER) model, our method successfully detected 21 potential ASEs overlooked upon FDA approval, including irritability and numbness. Our data-analytic approach revolutionizes the detection of unreported ASEs associated with newly deployed drugs, leveraging cutting-edge AI-driven social media analytics. It can increase the safety of new drugs in the marketplace by unlocking the power of social media to support regulators and manufacturers in the rapid discovery of hidden ASE risks.


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Weight loss and low BMI due to an underlying illness have been associated with increased mortality, reduced functional capacity, and diminished quality of life. There is a need for safe, long- term approaches to maintain body weight in patients with ...


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Recent studies have shown that pharmacologic weight loss with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and combination therapies is approaching magnitudes achieved with surgery. However, as more weight loss is achieved, there is concern for potential adverse effects on muscle quantity, …


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pmid:39401279, doi:10.1161/CIRCULATIONAHA.124.067676, Review, Jennifer Linge, Andreas L Birkenfeld, Ian J Neeland, Adaptation, Physiological / drug effects, Animals, Glucagon-Like Peptide-1 Receptor Agonists*, Humans, Muscle, Skeletal* / drug effects, Muscle, Skeletal* / pathology, Obesity / drug therapy, Sarcopenia / drug therapy, Weight Loss* / drug effects, PubMed Abstract, NIH, NLM, NCBI, National Institutes of Health, National Center for Biotechnology Information, National Library of Medicine, MEDLINE


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Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of cardiovascular mortality and is increasingly prevalent in our population. Glucagon-like ...


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glucagon-like peptide-1 receptor agonists,Atherosclerosis,Cardiovascular outcome trials,Inflammation,Mitochondrial dysfunction


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Scholar Rock is running a proof-of-concept study to mitigate muscle loss and enhance muscle mass in patients using GLP-1 drugs for weight management.


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Cardiovascular disease (CVD) remains a prominent cause of global mortality, primarily driven by atherosclerosis. Diabetes mellitus, as a modifiable risk factor, significantly contributes to atherogenesis. Monocyte recruitment to the intima is a critical step in atherosclerotic plaque formation, involving chemokines and adhesion molecules such as selectins, ICAM-1, and MCP-1. Glucagon-like peptide 1 receptor agonists (GLP-1RAs) are a promising group of drugs for reducing cardiovascular risk in diabetic patients, prompting investigation into their mechanisms of action. This interventional study enrolled 50 diabetes patients with atherosclerotic plaque, administering GLP-1RA for 180 days. Serum concentrations of MCP-1, ICAM-1, and L-selectin were measured before and after treatment. Anthropometric and biochemical parameters were also assessed. GLP-1RA treatment resulted in significant improvements in anthropometric parameters, glycemic control, blood pressure, and biochemical markers of liver steatosis. Biomarker laboratory analysis revealed higher baseline levels of MCP-1, ICAM-1, and L-selectin in diabetic patients with atherosclerotic plaque compared to healthy controls. Following treatment, MCP-1 and L-selectin levels decreased significantly (p < 0.001), while ICAM-1 levels increased (p < 0.001). GLP-1RA treatment in diabetic patients with atherosclerotic plaque leads to favorable changes in serum molecule levels associated with monocyte recruitment to the endothelium. The observed reduction in MCP-1 and L-selectin suggests a potential mechanism underlying GLP-1RA-mediated cardiovascular risk reduction. Further research is warranted to elucidate the precise mechanisms and clinical implications of these findings in diabetic patients with atherosclerosis.


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https://pub.mdpi-res.com/life/life-14-00690/article_deploy/html/images/life-14-00690-g001-550.jpg?1716881986


