Some observational studies [44–46], although not all [47,48], have documented an association between NAFLD and the risk of prevalent distal symmetric polyneuropathy in T2DM patients, independent of multiple cardio-metabolic risk factors and diabetesrelated variables. In a cross-sectional study involving roughly 400 outpatients with T2DM attending five Italian diabetes centers, who underwent liver ultrasonography, vibration controlled transient elastography (by FibroScan®) and evaluation of microvascular diabetic complications, Lombardi et al. documented that significant liver fibrosis (i.e., liver stiffness measurement [LSM] ≥ 7.0 and 6.2 kPa with M and XL probes, respectively) was independently associated with higher prevalence of microvascular diabetic complications (28% in patients with LSM < 7.0/6.2 kPa vs. 50% in patients with LSM ≥ 7.0/6.2 kPa, *p* < 0.001), including distal symmetric polyneuropathy (3% in patients with LSM < 7.0/6.2 kPa vs. 14% in patients with LSM ≥ 7.0/6.2 kPa, *p* < 0.05) [46]. Accumulating evidence also suggests the existence of an association between hepatic steatosis (as detected by imaging techniques) and cardiac autonomic dysfunction in patients with and without T2DM [49,50]. For instance, in a recent cross-sectional study including 173 individuals with T2DM and 183 age- and sex-matched nondiabetic controls from the Cooperative Health Research in South Tyrol (CHRIS) study, Targher et al. reported that individuals with T2DM and NAFLD (on ultrasonography) and individuals with NAFLD alone, but not those with T2DM alone, had an increased risk of cardiac sympathetic/parasympathetic imbalance (as assessed by

low- to high-frequency power ratio and other heart rate variability measures obtained by a 20 min resting electrocardiogram), when compared with those without NAFLD and T2DM [50].

However, although this evidence is interesting, additional research is needed to corroborate these findings in larger populations and, more willingly, in longitudinal studies.

### *3.3. Diabetic Retinopathy*

Some cross-sectional studies have investigated the relationship between NAFLD (as detected by imaging techniques) and the risk of prevalent diabetic retinopathy in patients with T2DM, reporting inconsistent results [44,51]. In this regard, a 2021 metaanalysis of nine cross-sectional studies for a total of 7170 patients with T2DM (57% with NAFLD on ultrasonography) reported no association between NAFLD and risk of prevalent diabetic retinopathy (random-effects odds ratio 0.94, 95% confidence interval 0.51–1.71; I 2 = 96%) [52]. In addition, in that meta-analysis, subgroup analyses suggested that in China, Korea and Iran, T2DM patients with NAFLD had a decreased risk of diabetic retinopathy when compared with those without NAFLD, whereas in Italy and India, T2DM patients with NAFLD had an increased risk [52]. As suggested by the authors of that meta-analysis, the aforementioned results should be interpreted with caution, because of the high heterogeneity observed and the differences in the results seen across various countries. Hence, additional research is needed to better explore this issue [52].

### **4. Risk of Macrovascular Complications**

### *4.1. Sub-Clinical Myocardial Remodelling and Dysfunction, Heart Valve Diseases and Cardiac Arrhythmias*

A large body of evidence now supports the existence of a strong and independent association between NAFLD and sub-clinical myocardial remodelling and dysfunction, heart valve diseases (i.e., aortic-valve sclerosis and mitral annulus calcification) and cardiac arrhythmias (mainly atrial fibrillation) in patients with and without T2DM (Table 1) [13,22,23,53–56]. For instance, in a cross-sectional study involving 222 outpatients with T2DM (~70% with NAFLD on ultrasonography), our research group showed that NAFLD was associated with increased risk of left ventricular diastolic dysfunction (as evaluated by trans-thoracic echocardiography), independent of established CVD risk factors, diabetes-related covariates and other confounders [57]. Some recent observational studies using biopsy or vibration-controlled transient elastography (by FibroScan®) also observed a graded relationship between functional and structural myocardial abnormalities and NAFLD severity in patients with and without T2DM [22]. A 2019 meta-analysis of 16 observational studies further confirmed that NAFLD (as detected by imaging techniques or liver biopsy) was independently associated with many functional and structural myocardial abnormalities, including higher left ventricle mass, higher left ventricular end diastolic diameter, higher left atrium diameter and the ratio between left atrial volume and body surface area, higher posterior wall and septum thickness, lower E/A wave ratio, higher E/E ratio, longer deceleration time and longer relaxation time [58]. Interestingly, recent observational studies also indicated that NAFLD (as detected by ultrasonography) was associated with a reduction in global longitudinal strain, which is a relatively novel echocardiographic parameter strongly associated with adverse cardiovascular outcomes [59–61].

Abbreviations: AF, atrial fibrillation; CKD chronic kidney disease; CVD, cardiovascular disease; NAFLD, non-alcoholic fatty liver disease; T2DM, type 2 diabetes.

**Table 1.** *Cont.*

Relating to heart valve calcifications, some cross-sectional studies have shown an independent association between NAFLD and risk of aortic valve sclerosis (AVS) and mitral annulus calcification (MAC) in patients with and without T2DM [22,62]. For instance, in a study involving nearly 250 consecutive outpatients with T2DM (~70% with NAFLD on ultrasonography), our research group documented that NAFLD was strongly associated with cardiac calcifications in both the aortic and mitral valves, even after adjustment for established CVD risk factors, diabetes-related covariates and other confounders [62]. These findings may be clinically relevant, as functional and structural myocardial abnormalities and AVS/MAC are strongly associated with all-cause and cardiovascular mortality in patients with and without T2DM [63].

Relating to cardiac arrhythmias, several observational studies and some meta-analyses [64–67] have documented that NAFLD (as detected by imaging techniques) is associated with prevalent and incident permanent atrial fibrillation (AF) in patients with and without T2DM (Table 1) [22]. Notably, AF is, at present, the most frequent cardiac arrhythmia observed day by day in clinical practice and, importantly, it is strongly linked to adverse cardiovascular outcomes [22]. In a recent meta-analysis of five observational studies for a total of roughly 240,000 adult individuals with and without T2DM, our research group documented that NAFLD (as detected by imaging techniques) was associated with higher prevalence and incidence of AF [66]. Interestingly, in a recent retrospective longitudinal study including 267 patients (33% with NAFLD as detected by ultrasonography and 17% with T2DM at baseline) undergoing AF ablation, Donnellan et al. reported that NAFLD was associated with increased arrhythmia recurrence rates following AF ablation, during a mean follow-up of nearly 2.5 years [68]. Other observational studies and meta-analyses, also enrolling T2DM patients, have reported that NAFLD (as detected by ultrasonography) was associated with an increased risk of prolonged QTc, ventricular arrhythmias or conduction defects, independent of established cardiovascular risk factors, diabetes-related covariates and other confounders [22,67,69– 72]. Interestingly, in a 2021 meta-analysis of 19 observational studies, Gong et al. confirmed that NAFLD (as detected by indirect markers of steatosis or imaging techniques) was independently associated with higher risks of prolonged QT interval (random-effects odds ratio 2.86, 95% confidence interval 1.64–4.99), premature atrial/ventricular contraction (random-effects odds ratio 2.53, 95% confidence interval 1.70–3.78) and heart block (random-effects odds ratio 2.65, 95% confidence interval 1.88–3.72) [67]. These data are clinically relevant, because NAFLD-related cardiac arrhythmias complications might contribute to explaining, at least in part, the increased risk of fatal and non-fatal CVD events observed in NAFLD patients.

### *4.2. Fatal and Non-Fatal Cardiovascular Events*

Over the last few decades, it has become increasingly evident that the leading cause of death in NAFLD patients is CVD [22,23,73–75]. In this regard, using data from the National Vital Statistics System multiple-cause mortality data (2007–2016), Paik et al. reported that CVD was the main cause of death among US patients with NAFLD, as detected by ICD codes [74]. In a meta-analysis of 45 observational studies for a total of approximately 8 million individuals followed up to 13 years, Younossi et al. also estimated that the pooled CVD-specific mortality rate among NAFLD patients with or without T2DM was nearly 5 per 1000 person-years [3]. Several longitudinal studies and some meta-analyses confirmed that patients with NAFLD (as detected by imaging techniques, ICD codes or liver biopsy) have an increased risk of developing fatal and non-fatal CVD events, even after adjustment for several traditional CVD risk factors, diabetes-related variables, specific medications and other potential confounders (Table 1) [22,23,54–56,76–79]. In a 2021 meta-analysis of 36 longitudinal studies for a total of 5,802,226 adults and 99,668 incident cases of fatal and non-fatal CVD events over a median follow-up of 6.5 years, our research group reported that NAFLD (as detected by imaging techniques, ICD codes or liver biopsy) was associated with a 45% increased risk of fatal or non-fatal CVD events, independent of age, sex, body mass index, waist circumference, presence of T2DM and other cardiovascular risk factors (random-effects hazard ratio 1.45, 95% confidence interval 1.31–1.61; I 2 = 86.2%) [14]. Such risk further increased in patients with severe forms of NAFLD, especially those with advanced fibrosis [14]. Another 2021 meta-analysis confirmed that NAFLD (as detected by imaging techniques, ICD codes or liver biopsy) was independently associated with increased risk of myocardial infarction (random-effects odds ratio 1.66, 95% confidence interval 1.39–1.99), ischemic stroke (random-effects odds ratio 1.41, 95% confidence interval 1.29–1.55) and heart failure (random-effects odds ratio 1.62, 95% confidence interval 1.43–1.84) [26]. In this regard, it is important to underline that the magnitude of cardiovascular risk is strongly related to the severity of NAFLD [25,80–82]. For instance, in a nationwide, matched cohort study of 10,568 Swedish individuals with biopsy-confirmed NAFLD (11% with T2DM at baseline) who were followed for a median period of 14 years, Simon et al. reported that, when compared to 49,925 adults of the general population (3% with established T2DM at baseline), mortality rates from CVD were significantly elevated in patients with simple steatosis (adjusted-hazard ratio 1.25, 95% confidence interval 1.16–1.35), and that these risks progressively increased in patients with NASH without fibrosis (adjusted-hazard ratio 1.66, 95% confidence interval 1.38–2.01), in those with non-cirrhotic fibrosis (adjusted-hazard ratio 1.40, 95% confidence interval 1.17–1.69) and also in those with cirrhosis (adjusted-hazard ratio 2.11, 95% confidence interval 1.63–2.73) [80]. Similar findings were also documented in cohorts involving NAFLD patients with T2DM [22,23,53–56].

To date, data regarding whether the improvement of NAFLD may reduce the incidence of cardiovascular complications are scarce. Although some retrospective studies enrolling Asian adults without pre-existing CVD have reported that the improvement or resolution of NAFLD (on ultrasonography) could be associated with a reduced risk of (carotid) atherosclerotic development in patients with and without T2DM [56,83], we believe that additional information on this issue is needed. In addition, it is important to underline that current evidence also indicates that histologic resolution of NASH could be associated with beneficial changes in risk factors for CVD [56,83], thus suggesting a potential favorable effect on cardiac complications.

Lastly, novel evidence also suggests that MAFLD criteria might identify patients with CVD better than NAFLD criteria [84].

### **5. CVD Risk Assessment in Patients with NAFLD**

Based on the aforementioned evidence, the EASL-EASO-EASD and American Association for the Study of Liver Diseases (AASLD) practice guidelines for diagnosing and managing NAFLD now recommend a CVD risk assessment in all patients with NAFLD [1,2]. In this context, as suggested by several experts in the field [13], a potential comprehensive CVD risk assessment may include (Table 2): (a) evaluation of coexisting risk factors (such as a prior history of CVD, family history of premature CVDs or T2DM, cigarette smoking, presence of T2DM, dyslipidemia, hypertension, obesity, metabolic syndrome, chronic kidney disease and erectile dysfunction), (b) physical examination (such as body weight, height, body mass index, waist circumference, blood pressure, arterial bruits and pulse examination), (c) laboratory tests (such as blood count, lipid profile, fasting plasma glucose, HbA1c, serum creatinine, transaminases, albumin, urinalysis, albuminuria) and (d) cardiovascular examination tests (such as resting electrocardiogram, carotid artery ultrasonography, and exercise stress test if coexisting CVD, CKD, T2DM or >2 CVD risk factors). In addition, the current evidence on this topic also calls attention to a holistic approach in managing and treating NAFLD patients [75,85].

**Table 2.** Essential comprehensive cardiovascular risk assessment in patients with NAFLD.

This table is based on the review published by Byrne and Targher [13]. Abbreviations: CKD, chronic kidney disease; CVD, cardiovascular disease; T2DM type 2 diabetes.

### **6. Conclusions**

The aforementioned data support the concept that NAFLD is a "multisystemic" disease [13]. Indeed, NAFLD is not only associated with serious hepatic complications, but it is also linked with macro- and microvascular complications. Importantly and notably, at present, the main cause of death among NAFLD patients is CVD [14]. For this reason, a comprehensive CVD risk assessment is essential in these patients [1,2,13]. That said, information regarding the impact of histological improvement of NAFLD on CVD risk is still scarce and needs further research [56,83]. In spite of our knowledge about epidemiology, pathogenesis and natural history of NAFLD, no specific pharmacological therapies have until now been approved for such a disease [86]. Lifestyle change promoting weight loss and the correction of modifiable cardio-metabolic risk factors are still the cornerstone of the treatment in NAFLD patients [86]. However, over the last few decades, several potential agents have been tested to treat NAFLD and its advanced forms [86,87]. They encompass some glucose-lowering drugs (especially pioglitazone, glucagon-like peptide-1 [GLP-1] receptor agonists and sodium-glucose co-transporter-2 [SGLT-2] inhibitors) [87], bile and non-bile acid farnesoid X activated receptor (FXR) agonists, anti-oxidants (i.e., vitamin E), statins and others [86,88]. In this regard, for instance, in a 2022 systematic review of randomised controlled trials testing the efficacy of peroxisome proliferator-activated receptor (PPAR) agonists, GLP-1 receptor agonists and SGLT-2 inhibitors for treating NAFLD in adults with or without type 2 diabetes, our research group found that pioglitazone (a PPAR-γ agonist), lanifibranor (a pan-PPAR agonist) and GLP1-R agonists (e.g., liraglutide and semaglutide) are able to obtain the resolution of NASH without worsening of fibrosis, whereas SGLT-2 inhibitors (e.g., empagliflozin and dapagliflozin) are able to reduce liver fat content, as detected by magnetic resonance-based techniques [87]. Given the strong relationship between NAFLD and macro- and microvascular complications, it is possible to speculate that these agents may exert a beneficial effect not only on the hepatic disease, but also in reducing the risk of developing cardiovascular and renal diseases [25,86–88]. However, herein it is important to note that pioglitazone is contraindicated in patients with symptomatic heart failure or in patients with a high risk of heart failure [25]. Seeing

the multiple pathways implicated in the pathogenesis of NAFLD and its complications, as well as the single response from single-agent therapies across RCTs available so far, it is also reasonable to hypothesize that the combination of different therapies (e.g., GLP-1 receptor agonists *plus* SGLT-2 inhibitors) will be more appropriate for treating NAFLD patients [86,87,89]. In this context, as suggested by several experts in the field, a holistic approach in managing and treating NAFLD patients seems to be fundamental [75,85].

**Author Contributions:** Conceptualization, A.M. and F.G.-P.; methodology, A.M.; data curation, A.M.; writing—original draft preparation, A.M. and F.G.-P.; writing—review and editing, A.M., A.D., G.B., D.C. and F.G.-P.; supervision, A.M. and F.G.-P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

### *Communication*

## **Low Screening Rates Despite a High Prevalence of Significant Liver Fibrosis in People with Diabetes from Primary and Secondary Care**

**Laurence J. Dobbie 1, Mohamed Kassab 2, Andrew S. Davison 3,4, Pete Grace 4, Daniel J. Cuthbertson 1 and Theresa J. Hydes 1,2,\***

**Abstract:** Diabetes is a driver of non-alcoholic fatty liver disease (NAFLD) and fibrosis. We determine current practices in examining liver fibrosis in people with diabetes and record prevalence levels in primary and secondary care. We extracted HbA1c results ≥48 mmol/mol to identify people with diabetes, then examined the proportion who had AST, ALT, and platelets results, facilitating calculation of non-invasive fibrosis tests (NIT), or an enhanced liver fibrosis score. Fibrosis markers were requested in only 1.49% (390/26,090), of which 29.7% (*n* = 106) had evidence of significant fibrosis via NIT. All patients at risk of fibrosis had undergone transient elastography (TE), biopsy or imaging. TE and biopsy data showed that 80.6% of people with raised fibrosis markers had confirmed significant fibrosis. We also show that fibrosis levels as detected by NIT are marginally lower in patients treated with newer glucose lowering agents (sodium-glucose transporter protein 2 inhibitors, dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists). In conclusion by utilising a large consecutively recruited dataset we demonstrate that liver fibrosis is infrequently screened for in patients with diabetes despite high prevalence rates of advanced fibrosis. This highlights the need for cost-effectiveness analyses to support the incorporation of widespread screening into national guidelines and the requirement for healthcare practitioners to incorporate NAFLD screening into routine diabetes care.

**Keywords:** fibrosis; NAFLD; diabetes; screening; primary care; secondary care

### **1. Introduction**

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of liver disease in the UK and Europe [1], soon to become the most common indication for liver transplantation in the next decade [2], as a result of the obesity and associated type 2 diabetes (T2D) epidemics. Expert consensus has suggested NAFLD be re-named metabolic-associated fatty liver disease (MAFLD) to reflect its strong association with insulin resistance and the metabolic syndrome [3]. Type 2 diabetes is a condition characterised by peripheral insulin resistance with inadequate compensatory pancreatic beta-cell insulin secretion. Insulin resistance and systemic inflammation lead to accumulation of free fatty acids and consequentially hepatocyte triglyceride accumulation characterising NAFLD [4,5]. NAFLD is generally benign in the majority of individuals, however in up to 40% of people it can progress to liver fibrosis [6,7]. Liver fibrosis describes the development of fibrous tissue

**Citation:** Dobbie, L.J.; Kassab, M.; Davison, A.S.; Grace, P.; Cuthbertson, D.J.; Hydes, T.J. Low Screening Rates Despite a High Prevalence of Significant Liver Fibrosis in People with Diabetes from Primary and Secondary Care. *J. Clin. Med.* **2021**, *10*, 5755. https://doi.org/10.3390/ jcm10245755

Academic Editor: Fernando Gómez-Peralta

Received: 16 November 2021 Accepted: 7 December 2021 Published: 9 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

due to the replacement of healthy tissue by extracellular matrix proteins, in NAFLD this is the result of hepatotoxic injury and initially leads to non-alcoholic steatohepatitis (NASH) and chronically to liver fibrosis [8]. Liver fibrosis, rather than simple steatosis or NASH, is associated with an increased risk of liver-related morbidity and mortality [6,9], overall mortality [10], and cardiovascular disease [11,12].

One of the most significant predictors of fibrosis progression and the development of advanced fibrosis is diabetes, particularly T2D [13–18]. NAFLD is reported to be present in 40–70% of individuals with T2D [19–21]. Furthermore, UK diabetes prevalence according to Quality Outcome Framework data is now 7.1% (2020/21), with an additional large number of undiagnosed cases [22]. While the European Association for the Study of the Liver (EASL) guidelines [23] and American Diabetes Association guidelines [24] suggest surveillance for NAFLD in people with T2D, the American [25], Asian [26], and UK [27,28] guidelines acknowledge that individuals with T2D are at greater risk of NAFLD, yet do not advocate widespread screening.

We aimed to perform a cross-sectional analysis of the burden of significant liver fibrosis in individuals with diabetes from both primary and secondary care to understand the prevalence of potentially clinically significant liver disease in these settings; and to provide a snapshot into current practice of examining fibrosis markers and ongoing risk stratification in people with diabetes.

### **2. Materials and Methods**

Screening with HbA1c We extracted glycated haemoglobin (HbA1c) results over a 21-month period (31 December 2019 to 14 September 21) from the Liverpool (University Hospital Foundation Trust) Clinical Laboratories and identified a cohort of individuals with an HbA1c ≥ 48 mmol/mol indicative of a diagnosis of diabetes (Figure 1). Individuals under 35 years old were excluded as fibrosis scores are inaccurate in this age group. Results from blood requests from inpatient stays, the emergency department, cancer services, and dialysis units were excluded, leaving those taken from primary care and other outpatient departments.

**Figure 1.** Study flow chart and summary of results. HbA1c, glycated haemoglobin; FIB-4, fibrosis-4; APRI, aspartate transaminase to platelet ratio index; AST aspartate transaminase; ALT, alanine transaminase; ELF, enhanced liver fibrosis score.

### *2.1. Determination of Liver Biochemistry Results and Fibrosis Scores*

We examined what proportion of these people had an aspartate transaminase (AST), alanine transaminase (ALT), and platelet levels taken within this time frame. From these results, we calculated three validated non-invasive scores of liver fibrosis, the fibrosis-4 (FIB-4) score [29], the AST to platelet ration index (APRI) [30], and the AST:ALT ratio (Supplementary Table S1) [31]. Significant fibrosis was defined as either a FIB-4 score > 2.67, APRI score ≥ 1.0, or AST:ALT ratio ≥ 1.0, where either the AST or ALT level was also >40 IU/L. We also included patients with an enhanced liver fibrosis score (ELF), based on tissue inhibitor metalloproteinases 1, amino-terminal pro-peptide of type III procollagen and hyaluronic acid [32]. Significant fibrosis was defined as an ELF score of >9.8. We then excluded results taken over 6 months prior to the HbA1c to ensure that individuals were likely to have diabetes at the time the fibrosis tests were taken. We additionally compared prevalence rates of liver fibrosis detected by primary and secondary care.

### *2.2. Confirmation of Fibrosis Identified with Non-Invasive Testing Using Transient Elastography (TE) and/or Liver Biopsy*

We further examined what proportion of individuals identified as being at risk of significant liver fibrosis according to non-invasive tests (NITs), had gone on to have confirmatory testing with either TE or liver biopsy. TE suggestive of fibrosis was defined according to a liver stiffness measurement > 8 kPa (Fibroscan, Echosens, Paris, France). Histological evidence of significant fibrosis or cirrhosis was confirmed by percutaneous liver biopsy and verified by an experienced liver histopathologist.

### *2.3. Association between Advanced Fibrosis According to FIB-4 Score and Glucose Lowering Agents*

We examined prescription data for glucose lowering agents for patients who had data available to calculate a FIB-4 score. We additionally examined the proportion of people with a raised FIB-4 score > 2.67 according the number and classes of glucose lowering medications prescribed.

### *2.4. Statistical Analysis*

Results are presented as the median and interquartile range. Data validity was ensured by examining ten random NHS numbers of both included and excluded patients and crosschecking them across databases. Data was analysed using R version 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria) and Excel Kutools.

### *2.5. Ethics*

As all patient data was anonymised this project did not require national ethical approval; clinical audit approval was obtained locally (number 10864).

### **3. Results**

### *3.1. Description of Study Cohort*

We identified 26,090 individuals who had an HbA1c result ≥48 mmol/mol requested from primary care or secondary care (outpatients department). Data was available to calculate the APRI score, AST:ALT ratio and FIB-4 score in 385 (1.47%) of these individuals and a further 5 (0.02%) had an ELF score requested, meaning that overall 390 (1.49%) people with diabetes had undergone a non-invasive test for fibrosis. Following the exclusion of results taken >6 months prior to the HbA1c result, the final study cohort consisted of 357 individuals with diabetes (Figure 1). In total 134 (37.5%) results were ordered from primary care and 223 from outpatients (62.5%). Baseline demographic data and laboratory results from this cohort are presented in Table 1.

**Table 1.** Baseline data from the cohort (*n* = 357).

M, male; F, Female; IQR, interquartile range; HbA1c, glycated haemoglobin; AST aspartate transaminase; ALT, alanine transaminase; ELF, enhanced liver fibrosis.

### *3.2. Prevalence of Significant Fibrosis in Individuals with Diabetes According to Serum Fibrosis Scores*

Between 13.7–19% individuals with diabetes were identified as having evidence of significant fibrosis using simple NITs (Table 2, Figure 2) and 80% (4/5) of people who had an ELF score requested had evidence of significant fibrosis. Using the previously described definitions of significant fibrosis (one or more of FIB-4 score > 2.67, APRI ≥ 1.0, AST:ALT ≥ 1.0, or ELF > 9.8), 106 (29.7%) people with diabetes were identified as being at risk. Of the 106 people at risk of significant fibrosis, 30 (28.3%) had fibrosis markers requested from primary care. Of the 76 outpatient blood requests, 66 (86.8%) came from the liver clinic. Overall fibrosis scores derived from blood requests sent from secondary care (34.1%) showed higher levels of significant fibrosis than primary care (22.4%) (Table 1, Figure 2). There was no positive correlation between HbA1c and fibrosis scores when examined on a continuous scale (Supplementary Figure S1).

**Table 2.** Percentage of people with diabetes found to have evidence of significant fibrosis determined by non-invasive markers.

FIB-4, fibrosis-4; APRI, aspartate transaminase to platelet ratio index; AST aspartate transaminase; ALT, alanine transaminase; ELF, enhanced liver fibrosis test; kPa kilopascal.

### *3.3. Prevalence of People with Diabetes and At-Risk Serum Fibrosis Scores with Confirmed Significant Fibrosis/Cirrhosis*

Of the 106 individuals with diabetes identified to be at risk of significant fibrosis using non-invasive serum markers, 67/106 (63.2%) went on to have transient elastography (TE/Fibroscan) (*n* = 50, 47.2%), liver biopsy (*n* = 24, 22.6%), or both (*n* = 7, 6.6%). In total 54/67 (80.6%) of these individuals had a liver stiffness measurement >8 kPa or evidence of significant fibrosis or cirrhosis at biopsy. All 39 people with raised fibrosis markers who did not receive a fibroscan or liver biopsy, had prior liver imaging via ultrasound (*n* = 30, 76.9%) or CT (*n* = 9, 23.1%), and 21/39 (53.8%) had evidence of cirrhosis.

**Figure 2.** Summary Graphs of FIB-4, APRI, AST:ALT Ratio by Test Location. Red dotted line = cut-off for high-risk fibrosis via APRI and AST:ALT Ration. Blue dotted line = cut-off for high risk liver fibrosis via FIB-4, FIB-4 = Fibrosis 4, APRI = AST to Platelet Ratio Index, *n* = Number, GP = General Practice, OP = Outpatient.

### *3.4. Prevalence of a Raised FIB-4 Score According to the Number and Class of Glucose Lowering Agent*

Medication data was available for 91.6% (327/357) patients. A further 4 patients were excluded who did not have data to calculate a FIB-4 score (final sample *n* = 323). A breakdown of the number of drugs and subclasses of glucose lowering agents prescribed are shown in Table 3. Patients who were not prescribed any glucose lowering therapies had lower levels of fibrosis according to the FIB-4 score (12.5%), compared to those on treatment (19.5%), however glycaemic control was also improved (Table 3, Supplementary Figure S2).  Patients treated with metformin (18.6%) and sulphonylureas (18.4%) had similar levels of fibrosis to the overall cohort. Patients treated with insulin trended towards having non-significantly higher levels of fibrosis (23.8%) and higher HbA1c levels (median 73 mmol/mol) (Table 3, Supplementary Figure S3).

**Table 3.** Results of Non-Invasive Serum Fibrosis Tests for People with Diabetes according to Number and Sub-class of Glucose-Lowering Agents Prescribed.

FIB-4, Fibrosis 4; SGLT-2 inhibitor, sodium-glucose cotransporter 2 inhibitor; GLP-1 receptor agonist, Glucagon-Like Peptide 1 Receptor Agonist; DDP-4 inhibitor, Dipeptidyl peptidase-4 inhibitor.

### **4. Discussion**

### *4.1. Summary of Findings*

In this brief report, we utilise real world UK regional data from local populations of people with diabetes and highlight two alarming findings. First, we demonstrate that <2% of people with diabetes are being screened for liver fibrosis, and that use of patented serum fibrosis biomarkers is minimal despite been advocated by the National Institute of Health and Clinical Excellence (NICE) as first line assessment for people with NAFLD [27]. Secondly, up to 29.7% of people with diabetes, in whom serum fibrosis markers were requested, were at risk of having significant liver fibrosis; subsequent confirmation of fibrosis was provided by second line tests, TE or liver biopsy, in a high proportion (80.6%) of cases. Thirdly, we report limited data showing a non-significant trend towards lower fibrosis scores in patients treated with DDP-4 inhibitors, SGLT-2 inhibitors, and GLP-1 receptor agonists. These findings reinforce the need for large prospective studies in this clinical population to develop cost-effective and easily implementable approaches to widespread screening for liver fibrosis in individuals with diabetes.

### *4.2. Comparison to the Existing Literature*

While our estimates of fibrosis prevalence in people with diabetes are higher than comparable studies, there is consensus in the literature that clinically relevant liver fibrosis is highly prevalent in this group. Global meta-analysis data in 439 biopsied patient with NAFLD and T2D identified that 17% had advanced fibrosis [21]. Data from over 120,000 people with T2D from the Cleveland clinic suggests that 8.4% have a FIB-4 score >2.67; however, prevalence estimates varied widely depending on the non-invasive score used [33]. Among individuals with T2D and a reliable TE result in the NHANES study (*n* = 825), 15.4% had a liver stiffness measurement ≥9.7 kPa. In a recent cross-sectional study from the US, 561 individuals with T2D attending primary care or endocrinology clinics underwent non-invasive screening using serum markers and TE; liver biopsy was performed where there was a suggestion of fibrosis [34]. In total 9% of people with diabetes had advanced fibrosis (F3/F4) according to TE. Fibrosis prevalence levels with TE were similar to that estimated using the FIB-4 and APRI panels, and both modalities correlated well with biopsy findings. A similar analysis from the UK identified that 18.5% of people with T2D attending primary care clinics (*n* = 467) had a FIB-4 >1.3 for ≤65 years and >2.0 for >65 years, of which nearly two thirds had a TE >8 kPa [35].

### *4.3. Molecular Mechanisms Linking T2D and NAFLD*

Pathogenic mechanisms linking T2D to NAFLD are complex; however, insulin resistance and inflammation are central [36]. High levels of circulating glucose and insulin increase rates of hepatic de novo lipogenesis leading to high levels of free fatty acids (FFA) in the liver; excess FFAs are stored as intrahepatic triglycerides [37]. Adiposity and the presence of insulin resistant adipose tissue leads to lipolysis; FFAs released from adipose tissue are taken up by peripheral tissues including the liver and muscle. NAFLD itself in turn leads to impairments in insulin signalling [38] and increased secretion of hepatokines. Adipokines are lipotoxic agents arising from chronically inflamed adipose tissue characterising T2D. These travel to the liver contributing to inflammation and NAFLD development [39]. Lipotoxicity, along with oxidative stress and a pro-inflammatory environment, result in steatohepatitis and eventually activation of hepatic stellate cells and extracellular matrix deposition. Clinical studies support this mechanism: stable isotope analyses show patients with increased hepatic adiposity have higher plasma FFA levels and ~3x greater de novo FFA synthesis [40].

### *4.4. Implications for Practice*

We therefore propose that there is an urgent need for greater adoption of national and international guidelines to implement widespread screening for fibrosis in individuals with diabetes and undertake comprehensive cost-effectiveness analyses. Despite updated recommendations from the EASL advocating the use of NITs and that ALT, AST, and platelets should be part of the routine investigations in primary care in patients with suspected liver disease [41], a huge shift in practice towards more widespread screening is unlikely to be implemented in the UK without guidance from the NICE. Detection of liver cirrhosis, which develops insidiously and without abnormalities in liver biochemistry, allows entry of individuals into variceal and hepatocellular carcinoma surveillance programmes, the latter being particularly relevant for people with diabetes [42,43]. Liver fibrosis is a partially reversible state, achieved with weight loss (~7%) [44], while fibrosis progression may be retarded with optimisation of glycaemic control, so multi-component metabolic intervention programmes are likely to be highly effective. Detection of NAFLD, and associated fibrosis, will facilitate enrolment in relevant clinical trials, and may encourage prescription of glucose-lowering therapies that target steatosis, steatohepatitis, or even fibrosis (including DDP-4 inhibitors, GLP receptor agonists and SGLT2 inhibitors) [45]. The burden of NAFLD and liver fibrosis expands beyond the liver, with well-established associations with cardiovascular morbidity and mortality [11,12] and extrahepatic cancer [46], so the wider benefits of detection are considerable.

We additionally show that fewer patients treated with either GLP-1 receptor agonists, SGLT 2 inhibitors and DDP-4 inhibitors have elevated FIB-4 scores. Glucose lowering therapies are a potential therapy in NAFLD given the fact they reduce insulin resistance and thus potentially reduce liver fat. DDP-4 inhibitors have not shown therapeutic effect in NAFLD; however, data is limited so larger trials are required [45,47,48]. GLP-1 receptor agonists have shown more promising findings.  Similarly, in a larger randomised controlled trial (RCT) (*n* = 320), semaglutide therapy led to higher rate of NASH resolution than control. However, no clear dose–response relationship was reported between dosing regimens (0.1 mg vs 0.2 mg vs 0.4 mg) [50]. A meta-analysis (*n* = 4442) of patients treated with liraglutide demonstrated ALT reduction [51]. For SGLT-2 inhibitors, a large RCT, EMPA-REG OUTCOME, reported that empagliflozin reduced ALT with these findings independent of glycaemic control (HbA1c) [52]. Similarly, in a moderately sized Swedish trial dapagliflozin reduced liver fat and ALT but did not improve glycaemic control. The conflicting findings between these two trials may or may delineate that SGLT-2 inhibitors have beneficial effects on NAFLD independent of glycaemic control [53]. Altogether, these trials show that GLP-1 agonists and SGLT 2 inhibitors have beneficial effects on liver biochemistry and liver fat levels in NAFLD. However, future trials need to assess the effects of these glucose lowering therapies on liver fibrosis. This could be via measuring non-invasive fibrosis scores (i.e., FIB-4, APRI, AST:ALT ratio), conducting fibroscans, liver multi-scan MRI testing, and liver biopsies.

### *4.5. Strengths and Limitations*

This dataset benefits from a systematic approach to screening individuals with diabetes in both primary and secondary care. However, there are several limitations. The dataset is biased by the fact that we were only able to examine fibrosis markers in people in whom clinicians requested an AST level, i.e., influenced by clinical suspicion of liver disease. Most outpatient requests were made from hepatology clinics, with an inevitable bias towards higher rates of fibrosis or cirrhosis. These factors would lead to an over-estimation of fibrosis prevalence compared to the overall population with diabetes. The positive predictive values of NITs are only moderate, so the true prevalence of fibrosis confirmed by biopsy would also have been lower. Furthermore the performance of NITs is less well validated and less reliable in the diabetes population [54,55]. Individuals with exemplary glycaemic control, with HbA1c < 48 mmol/mol would also have been overlooked, leading to a selection bias towards a sub-population of lesser metabolic health at higher risk of diabetes-related end-organ damage. This study was reliant on electronic medical records and therefore we were unable to reliably determine the aetiology of diabetes (type 1 or type 2 diabetes), or liver disease (including alcohol excess or viral hepatitis). Approximately 95%

of people with diabetes in the UK are estimated to have T2D; however, and all individuals that we have assessed would have had either MAFLD or dual aetiology liver disease, given the fact that they had diabetes. We examined the current practice of examining fibrosis markers in individuals with diabetes over a 1 year window of an HbA1c. Current guidelines advise screening every 1–3 years in people with confirmed NAFLD [41], so some individuals may have had bloods taken which could have been used to calculate a fibrosis score outside this time period. This study was limited by a significant proportion of the data being extracted over the COVID-19 pandemic. This may have negatively affected screening rates for fibrosis markers in both primary and secondary care and therefore may have affected the results. In addition, this study was limited by omission of the body mass index (BMI) data, which was not widely available from patient records. While we were able to access prescription records, unfortunately data on duration of diabetes, duration a glucose lowering agent had been prescribed and historic prescription data was no available to allow a comprehensive assessment of the role of newer glucose lowering therapies on fibrosis levels.

### **5. Conclusions**

In summary, we found very limited evidence of systematic screening for liver fibrosis: only 1.5% of individuals with diabetes had a NIT for assessment of fibrosis, despite evidence of a high prevalence of significant fibrosis (29.8%) in those assessed. We also show that fibrosis levels as detected by NIT is lower in patients treated with SGLT2 inhibitors, DDP-4 inhibitors, and GLP-1 receptor agonists. There is an urgent and unmet need to assess, develop, and implement cost-effective methods to provide widespread screening of individuals with diabetes for liver fibrosis and for healthcare practitioners to incorporate NAFLD screening into routine diabetes care. This will undoubtedly reap longer-term clinical benefits in reducing the hepatic and extra-hepatic burden of NAFLD in patients with diabetes.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/jcm10245755/s1, Table S1: Algorithms used to calculate Hepatic Steatosis Index and Fibrosis scores, Figure S1: Correlation between HbA1c and Fibrosis Marker scores; Figure S2: Percentage of people with a raised FIB-4 score according to Number of glucose lowering agents prescribed; Figure S3: Percentage of people with a raised FIB-4 score according to subclasses of glucose lowering agents prescribed.

**Author Contributions:** Conceptualization, all authors; methodology, all authors; formal analysis, L.J.D., M.K. and T.J.H.; data curation, A.S.D. and P.G.; writing—original draft preparation, T.J.H.; writing—review and editing, all authors; visualization, L.J.D.; supervision, D.J.C.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** This project did not require ethical approval however was approved by Aintree University Hospital Local Audit Committee (number 10864).

**Informed Consent Statement:** Patient consent was not required for this study, however we obtained local approval from Aintree University Hospital Audit Committee (Number: 10864).

**Data Availability Statement:** Data is available upon reasonable request to the authors.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

## *Article* **Once-Weekly Semaglutide Use in Patients with Type 2 Diabetes: Results from the SURE Spain Multicentre, Prospective, Observational Study**

**Virginia Bellido 1,2,\*, Cristina Abreu Padín 3, Andrei-Mircea Catarig 4, Alice Clark 4, Sofía Barreto Pittol 5 and Elias Delgado 6,7,8,9**

**Abstract:** Type 2 diabetes (T2D) is a complex disease for which an individualised treatment approach is recommended. Once-weekly (OW) semaglutide is a glucagon-like peptide-1 receptor agonist approved for the treatment of insufficiently controlled T2D. The aim of this study was to investigate the use of OW semaglutide in adults with T2D in a real-world context. SURE Spain, from the 10-country SURE programme, was a prospective, multicentre, open-label, observational study, approximately 30 weeks in duration. Adults with T2D and ≥1 documented HbA1c value ≤12 weeks before semaglutide initiation were enrolled. Change in HbA1c from baseline to end of study (EOS) was the primary endpoint, with change in body weight (BW), waist circumference, and patient-reported outcomes as secondary endpoints. Of the 227 patients initiating semaglutide, 196 (86.3%) completed the study on-treatment with semaglutide. The estimated mean changes in HbA1c and body weight between baseline and EOS were −1.3%-points (95% confidence interval (CI) −1.51;−1.18%-points) and −5.7 kg (95% CI −6.36;−4.98 kg). No new safety concerns were identified. Therefore, in routine clinical practice in Spain, OW semaglutide was shown to be associated with statistically significant and clinically relevant reductions in HbA1c and BW in adults with T2D.

**Keywords:** body weight; glucagon-like peptide-1 receptor agonist; HbA1c; real-world evidence; semaglutide; SURE study; type 2 diabetes

### **1. Introduction**

Type 2 diabetes (T2D) places a heavy burden on individuals and healthcare systems across the world. In Spain, an estimated 13.8% of people have T2D, and this is expected to increase in the future [1,2].

The management of T2D is complex. The American Diabetes Association (ADA) Standards of Medical Care in Diabetes 2022 [3] and the 2020 joint consensus statement of the ADA and the European Association for the Study of Diabetes (EASD) [4] recommend that physicians should take an individualised treatment approach when prescribing medications for T2D, and that they consider drug efficacy, risk of hypoglycaemia, cardiorenal benefits, effect on body weight (BW), adverse effects, pricing, and convenience for the patient [4].

**Citation:** Bellido, V.; Abreu Padín, C.; Catarig, A.-M.; Clark, A.; Barreto Pittol, S.; Delgado, E. Once-Weekly Semaglutide Use in Patients with Type 2 Diabetes: Results from the SURE Spain Multicentre, Prospective, Observational Study. *J. Clin. Med.* **2022**, *11*, 4938. https://doi.org/ 10.3390/jcm11174938

Academic Editors: Fernando Gómez-Peralta and Gianluca Aimaretti

Received: 22 June 2022 Accepted: 11 August 2022 Published: 23 August 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are an established class of antihyperglycaemic drugs used for the treatment of T2D, which have demonstrated improvements in glycaemic control and reductions in BW in patients with T2D [5,6]. In addition to their glucose-dependent function resulting in a low risk for hypoglycaemia [7], some GLP-1RAs (dulaglutide, liraglutide, and semaglutide) have demonstrated cardiovascular (CV) benefits in patients with T2D at high risk of CV disease [8–10]. Despite these benefits, access to GLP-1RAs is limited in Spain, and GLP-1RAs are only reimbursed for patients with obesity (body mass index [BMI] ≥ 30 kg/m2) and insufficient glycaemic control as a second-line therapy after metformin [11].

Semaglutide is a human GLP-1 analogue, approved as an add-on to diet and exercise for the treatment of adults with insufficiently controlled T2D, by the European Medicines Agency in February 2018 [12]. It has a long half-life, which makes it suitable for once-weekly (OW) dosing, [13] and is the only GLP-1RA that is available both in a OW subcutaneous (s.c.) injectable formulation and as an oral formulation administered once-daily [14].

The extensive SUSTAIN randomised clinical trial (RCT) programme, which investigated the efficacy and safety of OW s.c. semaglutide, demonstrated that 0.5 mg and 1.0 mg doses were associated with superior, clinically relevant improvements in glycaemic control and weight loss, compared with placebo or active comparators [8,15–23]. A safety profile similar to other GLP-1RAs was also observed.

SURE Spain is part of the SURE real-world study programme, which aimed to explore the use of OW semaglutide in a diverse population of adults with T2D in routine, real-world clinical practice across 10 countries (Canada, Denmark/Sweden, France, Germany, Italy, the Netherlands, Spain, Switzerland, and the United Kingdom) and to complement the results of the SUSTAIN RCTs. Unlike RCTs, the SURE studies are non-interventional and observational, allowing the assessment of patient outcomes, as well as product use and performance, in diverse patient populations in routine clinical practice [24].

The aim of this study was to evaluate the real-world use of OW semaglutide in a diverse T2D patient population in Spain.

### **2. Materials and Methods**

### *2.1. Study Design*

SURE Spain was a multicentre, prospective open-label, single-arm, non-interventional study assessing the use of OW semaglutide in adult patients with T2D in routine clinical practice in Spain. Informed consent and treatment initiation took place on the first visit (week 0), followed by an anticipated exposure period of ~30 weeks (range: 28–38 weeks). Intermediate visits scheduled according to local practice and data collection were performed throughout the entire study.

The decision to initiate semaglutide treatment was at the discretion of the treating physician, following requirements stated in the Summary of Product Characteristics (SmPC), therapeutic positioning report and local/regional guidelines, and clearly separated from the decision to include the patient in the SURE Spain study. All parameters collected in the study (except the patient-reported outcomes) were part of routine clinical practice. Patients were treated OW with commercially available s.c. semaglutide (Ozempic®; Novo Nordisk A/S, Bagsværd, Denmark), available in a pre-filled, multidose, pen injector. The treating physician determined the maintenance dose and any subsequent changes to it. Diet and physical activity counselling could be offered in line with routine clinical practice, with modifications to prescribed antihyperglycaemic treatment at the physician's discretion.

This study was conducted in accordance with the Declaration of Helsinki [25], the Guidelines for Pharmacovigilance Practices Module VI [26], and Good Pharmacoepidemiology Practices [27]. Prior to study initiation, the protocol, protocol amendment, patient information/informed consent form, together with any other written information to be provided to the patient and patient enrolment procedures, were reviewed and approved by the independent ethics committee/institutional review board at each study site (first approved in 2019 by the Ethics Committee of CEIm de EUSkadi, project identifier: NN9535-4368). Written informed consent was obtained from all patients prior to any study-related activities. This study is registered on ClinicalTrials.gov (NCT04067999).

### *2.2. Study Population*

Adult patients (age ≥ 18 years) diagnosed with T2D were included from 34 sites in Spain, with the first participant's first visit on 5 August 2019, and the last participant's last visit on 19 July 2021. Inclusion criteria included diagnosis of T2D and availability of one or more documented values of HbA1c within 12 weeks prior to semaglutide treatment initiation. Exclusion criteria included previous participation in a SURE study, mental incapacity, unwillingness, or language barriers precluding adequate understanding or cooperation, prior treatment with any investigational drug (90 days before enrolment), and hypersensitivity to semaglutide or any of the excipients. The study duration of 30 weeks was considered sufficient to initiate and optimise the study treatment regimen and to obtain real-world data for the evaluation of the primary endpoint.

### *2.3. Endpoints*

The primary endpoint was a change from baseline to end of study (EOS) in HbA1c (%-point and mmol/mol). Supportive secondary endpoints included: change from baseline to EOS in BW (kg and %) and waist circumference (cm); proportion of patients achieving HbA1c < 8.0% (64 mmol/mol), <7.5% (59 mmol/mol) and <7.0% (53 mmol/mol) [28]; reduction in HbA1c from baseline to EOS of ≥1.0%-point; weight reduction from baseline to EOS of ≥3.0% [29] and ≥5.0%; HbA1c reduction from baseline to EOS of ≥1.0% and weight reduction from baseline to EOS of ≥3.0% [29]; patient-reported severe or documented hypoglycaemia between baseline and EOS; and change from baseline to EOS in scores for patient-reported outcomes of: the Diabetes Treatment Satisfaction Questionnaire–status (DTSQs; absolute treatment satisfaction) comprising eight questions, of which six questions are combined into a total Treatment Satisfaction score (scale: 0 to 36); the Diabetes Treatment Satisfaction Questionnaire–change (DTSQc; relative treatment satisfaction), total treatment satisfaction (scale: −18.0 to 18.0); and the 36-item Short-Form Health Survey version 2 (SF-36®v2), physical and mental summary component. The proportion of patients who completed the study under treatment with semaglutide was also investigated.

Exploratory assessments included: weekly dose of semaglutide at EOS; proportion of patients who had not added new antihyperglycaemic drug(s) to semaglutide treatment at any time during the study, evaluated at EOS; proportion of patients who had achieved clinical success, in relation to the reason to initiate semaglutide treatment, as assessed by the physician at EOS; patient-reported 8-Item Morisky Medication Adherence Scale (MMAS-8) score at EOS (low, medium, high) [30–32]; and the number of severe or documented hypoglycaemic episodes. Post hoc assessments included change from baseline to EOS in BMI (kg/m2). Permission for use of the MMAS-8 was granted prior to the study.

### *2.4. Safety*

Only information on serious adverse drug reactions (SADRs), fatal events, pregnancies in female patients, and adverse events (AEs) in foetuses or newborns were systematically collected during the study. Voluntary reporting of other safety information by the physician followed the same process as for the systematic safety reporting. All episodes of patientreported documented and/or severe hypoglycaemia were to be recorded.

### *2.5. Statistical Analyses*

Power calculations showed that a sample size of 130 patients was required, based on the criterion of 90% probability of obtaining a 95% confidence interval (CI) for mean change from baseline in HbA1c whose half-width was at most 0.30. The half-width of 0.30 was chosen as a reasonable uncertainty allowing for a robust evaluation of glycaemic efficacy, in line with diabetes guidelines [33]. To ensure sufficient statistical power to evaluate the efficacy of semaglutide on glycaemic control (on the basis of evidence from previous observational studies with GLP-1RA treatment), it was necessary to include at least 217 enrolled patients initiating semaglutide, to ensure that 130 patients completed the study on-treatment [34,35].

The Full Analysis Set (FAS), which included all patients in the study who initiated semaglutide treatment, was used for characterising baseline demographics, analysis of the secondary endpoint related to study completion on-treatment, the selected exploratory assessments, description of AEs, and the sensitivity analyses of the primary and secondary endpoints.

The Effectiveness Analysis Set (EAS) included all patients in the FAS who completed the study and were receiving semaglutide treatment at EOS. The EAS was used for characterising baseline demographics at EOS, the description of antihyperglycaemic medications at baseline and EOS, and the primary, secondary and exploratory endpoint analyses.

Baseline demographic data are summarised using descriptive statistics (mean ± standard deviation [SD] or median and interquartile range for continuous variables and number and proportion for categorical variables). Change in the continuous variables of the primary and secondary endpoints from baseline to EOS were analysed using the Analysis of Covariance (ANCOVA) model. Categorical endpoints were analysed using descriptive statistics.

Sensitivity analyses investigated the robustness of the conclusions from the main analyses and explored the impact of missing data in the primary analysis, for which patients were excluded if they did not complete the study or discontinued treatment, or if HbA1c data were missing at EOS. The prespecified in-study sensitivity analysis of the primary endpoint included all patients in the FAS with at least one post-baseline HbA1c measurement in the in-study period. For this analysis, the primary endpoint was analysed using a Mixed Model for Repeated Measures (MMRM) including all HbA1c assessments in the in-study period. The on-treatment sensitivity analysis included patients in the FAS with at least one post-baseline HbA1c assessment, but it only included HbA1c assessments in the on-treatment period and used the same statistical approach as the in-study sensitivity analysis.

Because of the COVID-19 pandemic, the EOS visit (V6) window was extended beyond 38 weeks to allow participants to complete their EOS assessments. Consequently, an additional post hoc sensitivity analysis was performed to explore the impact of extending the EOS visit (V6) window on the primary endpoint. The sensitivity analysis of the primary endpoint was the same as the primary analysis of the primary endpoint but included only those patients who had an EOS visit between weeks 28 and 38 (the original visit window). An additional post hoc sensitivity analysis was performed to explore the impact of extending the EOS visit on the secondary endpoint of change from baseline to EOS in BW. This sensitivity analysis was the same as the main analysis of this endpoint but included only those patients who had an EOS visit between weeks 28 and 38.

### **3. Results**

### *3.1. Patient Population and Baseline Characteristics*

Of the 228 patients who signed the consent form, one did not meet the eligibility criteria. Therefore, the FAS comprised the 227 patients who were enrolled in the study and who had initiated semaglutide treatment (Figure 1). A total of 210 patients (92.5%) completed the study, and the mean treatment duration was 33.7 weeks. The reasons for non-completion were: death (*n* = 1; 0.4%), lost to follow-up (*n* = 3; 1.3%), withdrawal by patient (*n* = 3; 1.3%), and missed EOS visit within the visit window (*n* = 10; 4.4%) (Figure 1). The EAS comprised 196 patients (86.3%) who had completed the study on semaglutide treatment (Figure 1). Twelve patients (5.2% of the FAS) had an unknown treatment status at EOS. With regard to discontinuations, 16 patients (7.0%) discontinued treatment due to unacceptable gastrointestinal (GI) intolerability, and a further three patients (1.3%) had 'other' recorded as the reason (Figure 1).

**Figure 1.** Patient disposition. \* Patients who initiated semaglutide treatment and attended the end of study visit. GI, gastrointestinal.

Baseline characteristics of patients are summarised in Table 1. Hypertension and dyslipidaemia were the most frequent CV comorbidities at baseline, affecting 75.8% and 76.2% of patients, respectively.

Most patients initiated semaglutide at a dose of 0.25 mg (83.3%); 13.7% initiated at 0.5 mg and 3.1% at 1.0 mg. The most common reasons for initiating semaglutide as part of T2D treatment were weight reduction (94.3%) and to improve glycaemic control (88.5%).

The most frequent antihyperglycaemic drugs used by patients in the EAS at baseline were metformin (75.5% of patients), sodium–glucose cotransporter-2 inhibitors (SGLT-2is) (42.3%), basal insulin (32.7%), and dipeptidyl peptidase-4 inhibitors (DPP-4is) (20.4%) (Supplementary Table S1).

### *3.2. HbA1c, BW, BMI, and Waist Circumference Outcomes*

For patients in the EAS receiving semaglutide, statistically significant reductions were observed at EOS for mean HbA1c, BW, waist circumference and BMI (Table 2). The mean HbA1c at EOS was 7.1%, and the estimated mean change from baseline was −1.3%-points [95% CI −1.51;−1.18%-points; *p* < 0.0001] (Table 2, Supplementary Figure S1); mean BW at EOS was 93.2 kg, and the estimated mean change from baseline was −5.7 kg [95% CI −6.36; −4.98 kg; *p* < 0.0001] (Table 2, Supplementary Figure S1); mean BMI at EOS was 34.4 kg/m2 , and the estimated mean change from baseline was −2.1 kg/m2 [95% CI −2.37; −1.86 kg/m2; *p* < 0.0001]; and mean waist circumference at EOS was 113.4 cm, and the estimated mean change from baseline to EOS was −5.3 cm [95% CI −6.29; −4.41 cm; *p* < 0.0001] (Table 2, Supplementary Figure S1).

**Table 1.** Baseline characteristics of patients (FAS).

Values based on FAS (*n* = 227). Data for continuous variables are mean (SD) unless otherwise specified. BMI, body mass index; eGFR, estimated glomerular filtration rate; FAS, Full Analysis Set; FPG, fasting plasma glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SD, standard deviation.

At EOS, 81.0%, 67.7% and 54.0% of patients in the EAS had an HbA1c of < 8.0%, <7.5% and <7.0%, respectively (Figure 2). The proportion of patients achieving an HbA1c reduction ≥1%-point was 56.6% and the proportions achieving weight reduction of ≥ 3.0% and ≥5.0% were, respectively, 69.2% and 49.7% (Figure 2). The proportion of patients in the EAS achieving the composite endpoint of an HbA1c reduction of ≥ 1.0% and weight reduction ≥3.0% at EOS was 44.3% (Figure 2). In the FAS, 86.3% of patients completed the study on-treatment with semaglutide (Figure 1).

### *3.3. Sensitivity Analyses*

Prespecified sensitivity analyses were used to explore the impact of missing data in the main analysis. The on-treatment sensitivity analysis of the FAS showed that the mean HbA1c decreased over time from initiation of semaglutide to week 30, with an estimated change of −1.4%-points [95% CI −1.59; −1.27%-points] (Supplementary Figure S2). The estimated mean changes from baseline to EOS and associated 95% CIs were similar across sensitivity analyses and showed that the mean changes in HbA1c were statistically significantly different from having no mean change in HbA1c (Supplementary Table S2, Supplementary Figure S2). Moreover, the estimated mean HbA1c and estimated change in HbA1c were similar over the course of the study for both the in-study and on-treatment period.

**Table 2.** Change from baseline to EOS in HbA1c, body weight, waist circumference, and BMI (EAS).

Data are based on the EAS, which included patients who attended the EOS visit and were still receiving semaglutide. Change in response from baseline to EOS is analysed using baseline, T2D duration, age, BMI, pre-initiation use of GLP-1RA, pre-initiation use of DPP-4i, pre-initiation use of insulin, number of OADs used pre-initiation (0–1/2+) and sex as covariates. *p*-value is reported for no average change in response from baseline to EOS. The assessment of BMI was performed as a post hoc analysis. BMI, body mass index; CI, confidence interval; DPP-4i, dipeptidyl peptidase-4 inhibitor; EAS, Effectiveness Analysis Set; EOS, end of study; GLP-1RA, glucagon-like peptide-1 receptor agonist; N, total number of patients in EAS; *n*, total number of patients included in analyses; OAD, oral antihyperglycaemic drug; T2D, type 2 diabetes.

**Figure 2.** Proportion of patients achieving HbA1c targets and weight-loss goals (EAS). EAS, Effectiveness Analysis Set; EOS, end of study.

Additional post hoc sensitivity analyses were performed in patients who had their EOS visit within the original visit window (week 28–38). The post hoc analyses of the mean changes from baseline to EOS for HbA1c and for BW showed similar results to those seen in the primary analysis (Supplementary Table S3).

Collectively, the sensitivity analyses supported the conclusions from the primary analysis, which included assessments for patients who were on-treatment at the EOS visit (V6), also including those completing the study after week 38.

### *3.4. Semaglutide Dose*

The mean ± SD weekly dose of semaglutide at EOS was 0.85 ± 0.24 mg. At EOS, five (2.6%) patients were receiving 0.25 mg OW semaglutide, 50 (25.5%) were receiving 0.5 mg, two (1.0%) were receiving between >0.5 mg and <1.0 mg, and 139 (70.9%) were receiving 1.0 mg.

### *3.5. Patient-Reported Outcomes*

In patients receiving semaglutide at EOS, DTSQs score increased by 4.4 [95% CI 3.66; 5.07; *p* < 0.0001] from baseline to EOS, representing a significant increase in absolute treatment satisfaction (Figure 3). Patients receiving semaglutide also reported a DTSQc score at EOS of 13.1 (95% CI 12.36; 13.85) out of a maximum score of 18, indicating a significant relative improvement in treatment satisfaction (*p* < 0.0001) (Figure 3).

**Figure 3.** Treatment satisfaction and HRQoL (EAS). \* *p* = 0.0013; \*\* *p* < 0.0001. Data are based on EAS. DTSQ status version (DTSQs) was measured at the informed consent and initiation visit, and the EOS visit; with responses ranging from 0 (very dissatisfied) to 6 (very satisfied) for each item of the questionnaire. The maximum total score is 36. The SF-36®v2 questionnaire has 36 questions grouped into eight domains, which can be combined into two summary component scores (overall mental and physical health); a higher SF-36®v2 score indicates lower disability. DTSQ, Diabetes Treatment Satisfaction Questionnaire; DTSQc, DTSQ change version; DTSQs, DTSQ status version; EAS, Effectiveness Analysis Set; EOS, end of study; HRQoL, health-related quality of life; MCS, mental component summary; PCS, physical component summary; SF-36®v2, 36-Item Short-Form Health Survey version 2.

At EOS, significant increases were observed in both the SF-36®v2 health-related qualityof life (HRQoL) questionnaire physical component score (*p* < 0.0001) and the mental component score (*p* = 0.0013), indicating an improvement in quality of life from baseline to EOS (Figure 3).

Mean MMAS-8 score was 7.0 at baseline and 7.4 at EOS, indicating a medium level of treatment adherence. The proportion of participants with medium and high adherence was, respectively, 49.5% and 36.6% at baseline and 34.1% and 57.3% at EOS.

### *3.6. Adverse Events and Hypoglycaemia*

AEs and severe or documented hypoglycaemic episodes in patients receiving semaglutide are summarised in Table 3. In the FAS, 15 patients (6.6%) reported 26 treatmentemergent AEs: 88.5% of AEs were non-serious (13 [5.7%] patients; 23 events) and 46.2% were moderate in intensity (7 [3.1%] patients; 12 events). A total of 13 patients reported 22 GI AEs, which accounted for the highest number of AEs by system organ class. Three serious AEs (SAEs) were reported (Medical Dictionary for Regulatory Activities preferred terms: atrial fibrillation, left ventricular failure, and myocardial infarction) by two patients (0.9%), which were all judged as unlikely to be related to semaglutide treatment by the investigators. One severe SAE (preferred term: myocardial infarction) was reported, which had a fatal outcome.

All other events were reported on a voluntary basis (FAS). AE, adverse event; E, event; FAS, Full Analysis Set; GI, gastrointestinal; N, total number of patients in FAS; SADR, serious adverse drug reaction.

Eight patients (4.1%) in the EAS reported severe or documented hypoglycaemia episodes between baseline and EOS, with similar results in the FAS (12 patients; 5.3%). At EOS, 20 severe or documented hypoglycaemic episodes were reported in the EAS, and 26 were reported in the FAS. Of these 26 events, 22 were reported by patients while using insulin and 3 occurred while using sulphonylureas. The date of one hypoglycaemic episode was unrecorded, which prevented an assessment of concurrent medication use. No severe hypoglycaemic episodes were reported during the study.

### **4. Discussion**

The SURE Spain study is part of the SURE study programme, which consists of nine observational studies in ten countries and was conducted to assess the real-world use of OW semaglutide.

The data reported indicate that when OW s.c. semaglutide was taken according to local clinical practice by adult patients with T2D in Spain, a clinically relevant and statistically significant reduction, compared with baseline, was observed for HbA1c at EOS (*p* < 0.0001) [36]. This was observed despite 14.5% of the study population switching from another GLP-1RA to semaglutide at baseline. While previous treatment may be expected to influence outcomes, improvements have been reported in patients treated with semaglutide who were not naïve to GLP-1RAs [36].

At EOS, patients also experienced statistically significant decreases from baseline in BW and waist circumference. A total of 97 (49.7%) patients achieved a weight reduction from baseline of ≥5%. This weight reduction is a key consideration in terms of reducing CV risk, in view of the beneficial reductions in triglycerides, total cholesterol, and low-density lipoprotein cholesterol that are associated with a weight loss of 5–10% [37].

Furthermore, patients reported substantial improvements in treatment satisfaction and HRQoL, as measured by the DTSQ and the SF-36®v2, respectively. In addition, patients' adherence to OW semaglutide treatment was good, with 91.4% of patients reporting either high (57.3%) or medium (34.1%) adherence at EOS. Adherence to OW semaglutide in the SURE Spain study compares favourably to the adherence rates of 39.1–64.5% at 1 year reported for GLP-1RAs (including semaglutide) in retrospective, real-world cohort studies [37,38].

Additionally, the patient population in this study had advanced T2D, as indicated by the mean disease duration of 11.8 years from diagnosis and the complex pharmacological treatment at baseline, with 41.9% of patients taking more than two antihyperglycaemic medications and 47.5% taking insulin. These factors are associated with poorer treatment outcomes and make it more difficult to achieve treatment goals.

Drawing comparisons between GLP-1RA RCTs and real-world evidence studies from different countries can be challenging. Local T2D clinical guidelines vary and can restrict clinical access to GLP-1RAs, while local reimbursement policies may impose further barriers to patient access. In Spain, outside of private practice, GLP-1RAs are only reimbursed for patients with a BMI ≥ 30 kg/m2. This is in contrast with Denmark, where the recommendation is independent of BMI, and the UK, where use is recommended in those with a BMI ≥ 35 kg/m2 who show an adequate metabolic response.

Overall, the results of this study support previously reported data on the real-world use of OW semaglutide in Spain [39–41]. The reduction in HbA1c and BW observed in Spain align with those observed in the countries that have published results from the SURE programme to date—Canada, Denmark/Sweden, Switzerland, and the UK—for which the mean change in HbA1c from baseline to EOS was between −0.8 and −1.5%-points and the mean change in BW from baseline to EOS was between −4.3 and −5.8 kg [42–45]. The results are also aligned with real-world evidence from other countries, for example, a study by Marzullo et al., that showed reductions in HbA1c and body weight after 6 and 12 months of OW semaglutide treatment in people with T2D in Italy [46].

Metabolic control in patients with T2D is assessed using multiple factors (e.g., BW, waist circumference), and not only HbA1c. In SURE Spain, the majority (70.9%) of patients were receiving the recommended dose of 1.0 mg OW of semaglutide by EOS, and the significant improvements in primary and secondary endpoints in the study may indicate that this dose is appropriate for the goal of achieving global metabolic control. The safety findings of the real-world T2D population in Spain were also consistent with the safety profile of semaglutide established in the SUSTAIN programme and with that of the GLP-1RA class, with no unexpected safety issues reported.

### *Study Limitations*

The SURE Spain study was non-interventional and single-armed, so the potential impact of other predictive factors cannot be excluded. The fundamental limitation of such a study design is the absence of a randomised comparator, which would otherwise have enabled differentiation of the changes caused by treatment, and the impact of other factors. Data in this study were collected during routine clinical practice, rather than through mandated examinations at predetermined time points, which may have impacted the robustness and completeness of the dataset.

The primary analysis was based on data from patients who completed the study on semaglutide treatment and with their HbA1c levels recorded at EOS. This could have resulted in selection bias, because patients who benefit from the study treatment are more likely to continue than those who do not. To account for this, sensitivity analyses of the primary endpoint included all post-baseline HbA1c assessments as well as evaluations from intermediate visits also including patients who did not complete the study or discontinued semaglutide during the study. In addition, secondary supportive analyses assessed the percentage of patients who had started semaglutide treatment and were receiving it at the EOS.

The inclusion criteria were purposely designed to be broad and reflect a real-world T2D population, which is rarely the case in a standard RCT. However, it is likely that physicians who were highly motivated would have been overrepresented among the participating centres, and that the centres included either highly motivated patients or patients who were difficult to treat with the other therapies available. As a result, the enrolled group may only represent subsets of individuals who are eligible for semaglutide therapy. Nevertheless, study participants were profiled in terms of demographics and clinical data, which allowed for the assessment of the representativeness of the recruited population. Details of medical history (including T2D diagnosis) and concurrent diseases were obtained without further confirmation as provided by the investigators.

A potential limitation of SURE Spain is the study's geographical location and time of initiation. The study was conducted soon after the launch of OW semaglutide, in a real-world setting, in a diverse T2D population recruited by investigators at 34 sites in Spain. However, the 34 sites that enrolled patients account for approximately half of the communities/regions within Spain, so may not be representative of the entire population. Furthermore, in Spain, GLP-1RAs are only reimbursed for patients who have a BMI ≥ 30 kg/m2, and only approximately 8% of Spanish patients with T2D are prescribed GLP-1RAs [11,47]. Therefore, none of the patients enrolled in the study had a 'normal' BMI (≥18.5–<25 kg/m2). These country-specific factors may have influenced the study results; in the future, however, semaglutide will likely be prescribed to a broader range of patients with T2D, including those with less severe disease progression.

The COVID-19 pandemic impacted intermediate and EOS visits in SURE Spain. Because of accessibility issues, several of these visits were instead conducted by telephone, rather than in-person. To further mitigate the challenges raised by the pandemic, changes were made to the study design that allowed patients to postpone their last visit (after the 38-week timepoint). An additional post hoc sensitivity analysis was performed to assess how the primary and secondary endpoints were affected by extending the EOS visit window. Extending the EOS visit window had no impact on the study outcomes.

Evidence has been reported that patients with T2D in Spain may have gained weight during the COVID-19 lockdown, due to their substantial lifestyle changes [48]. Sánchez et al. noted that if another lockdown were to be imposed, there should be greater emphasis on avoiding weight gain, in which case GLP-1RAs might be an effective therapy for these patients. Despite the influence of COVID-19, the data from this study are regarded as robust, and are suitable for further interpretation.

### **5. Conclusions**

In SURE Spain, patients treated with OW semaglutide experienced statistically significant and clinically relevant reductions from baseline to EOS in HbA1c, BW, and waist circumference, and improvements in other clinical parameters such as treatment satisfaction and HRQoL in a real-world setting. These findings were significant, despite the nature of the population (advanced T2D) included in the SURE Spain study and the local limitations on prescribing GLP-1RAs. The reported AEs were consistent with the known safety profile of semaglutide, with no new safety concerns reported. These results support the use of OW semaglutide in routine clinical practice in adults with T2D in Spain.

**Supplementary Materials:** The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm11174938/s1.

**Author Contributions:** Conceptualisation, V.B., C.A.P., A.-M.C., A.C., S.B.P. and E.D.; investigation, V.B., C.A.P., A.-M.C., A.C., S.B.P. and E.D.; writing—review and editing, V.B., C.A.P., A.-M.C., A.C., S.B.P. and E.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by Novo Nordisk A/S.

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and first approved in 2019 by the Ethics Committee of CEIm de EUSkadi, project identifier: NN9535-4368.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The datasets analysed during the current study are available from the corresponding author on reasonable request.

**Acknowledgments:** This study was funded by Novo Nordisk A/S. We thank all the participants, investigators, and study-site staff, as well as Kamal Kant Mangla and Sanskruti Jayesh Patel from Novo Nordisk for their review and input to the manuscript, and Julia Peics (AXON Communications) for medical writing and editorial assistance (funded by Novo Nordisk A/S). Use of the ©MMAS is protected by US Copyright laws. Permission for use is required. A license agreement is available from Donald E. Morisky, MMAS Research LLC 14725 NE 20th St. Bellevue WA 98007 or from<EMAIL_ADDRESS>
**Conflicts of Interest:** V.B. has received unrestricted research support from Abbot, Novo Nordisk, and Sanofi, and has received speaker/advisory honoraria from Abbott, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Esteve, Janssen, Merck, Mundipharma, Novartis, Novo Nordisk, Roche, and Sanofi. E.D. has received unrestricted research support from AstraZeneca, Novo Nordisk, Pfizer, Roche, and Sanofi, and has received consulting fees and/or honoraria for membership on advisory boards and speaker's bureau from Abbott Laboratories, Almirall, AstraZeneca, Esteve, GlaxoSmithKline, Lilly, Merck Sharp & Dohme, Novartis, Novo Nordisk, Pfizer, and Sanofi.

### **References**

## **Efficacy of Liraglutide in Non-Diabetic Obese Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials**

**Joshuan J. Barboza 1,2,\*, Mariella R. Huamán 3, Beatriz Melgar 1,4, Carlos Diaz-Arocutipa 1, German Valenzuela-Rodriguez 1 and Adrian V. Hernandez 1,5**

**Abstract:** Objective: We systematically assessed the efficacy of liraglutide in non-diabetic obese adults. Methods: Six databases were searched up to July 2021 for randomized controlled trials (RCTs) assessing liraglutide versus placebo in obese adults. Primary outcomes were body weight and body mass index (BMI). Secondary outcomes were treatment-emergent adverse events (TEAEs), hypoglycemic episodes, HbA1c, and blood pressure. Effect measures were risk ratio (RR) or mean difference (MD) with their confidence interval (95%CI). Random-effects models and inverse variance meta-analyses were used. Quality of evidence was assessed using GRADE. Results: Twelve RCTs (*n* = 8249) were included. In comparison to placebo, liraglutide reduced body weight (MD −3.35 kg; 95%CI −4.65 to −2.05; *p* < 0.0001), and BMI (MD −1.45 kg/m2; 95%CI −1.98 to −0.91; *p* < 0.0001). Liraglutide did not reduce TEAEs (RR 1.08; 95%CI 0.92 to 1.27; *p* = 0.25), and Hb1Ac (MD −0.76%; 95%CI −2.24 to 0.72; *p* = 0.31). Furthermore, it did not increase hypoglycemic episodes (RR 2.01; 95%CI 0.37 to 11.02; *p* = 0.28). Finally, liraglutide reduced systolic blood pressure (MD −3.07 mmHg; 95%CI −3.66 to −2.48; *p* < 0.0001) and diastolic blood pressure (MD −1.01 mmHg; 95%CI −1.55 to −0.47; *p* = 0.0003). Seven RCTs had a high risk of bias. Subgroup analyses by length of treatment and doses had effects similar to the overall analyses. Quality of evidence was low or very low for most outcomes. Conclusions: In non-diabetic obese adults, liraglutide reduced body weight, BMI and blood pressure in comparison to placebo. Adverse events, Hb1Ac levels and hypoglycemic episodes were not different than placebo.

**Keywords:** liraglutide; body weight; obesity; hypoglycemia; meta-analysis

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

### **1. Introduction**

Obesity is a major public health problem, affecting more than 603 million adults across the globe [1]. It may also increase the risk of several diseases, including hypertension, dyslipidemia, type 2 diabetes (T2D), and coronary artery disease. Initial management of obese patients includes a combination of dietary changes, exercise, and behavior modification. Nevertheless, in some cases, this strategy is insufficient and pharmacological treatment is required to achieve and maintain therapeutic goals in terms of weight loss.

Liraglutide is a glucagon-like peptide-1 (GLP-1) agonist and potential weight loss drug [2]. It increases insulin concentrations after eating, prior to the elevation

Melgar, B.; Diaz-Arocutipa, C.; Valenzuela-Rodriguez, G.; Hernandez, A.V. Efficacy of Liraglutide in Non-Diabetic Obese Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. *J. Clin. Med.* **2022**, *11*, 2998. https://doi.org/10.3390/ jcm11112998

**Citation:** Barboza, J.J.; Huamán, M.R.;

Academic Editors: Fernando Gómez-Peralta and Gianluca Aimaretti

Received: 24 March 2022 Accepted: 27 April 2022 Published: 25 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

of blood glucose levels [3,4]. Liraglutide is a drug used in obese diabetic patients, which justified the investigation of liraglutide as a treatment for non-diabetic obese people. A study evaluated the efficacy at 12 weeks of low-dose liraglutide on the weight of Taiwanese patients without T2D. Compared to baseline, 5.6% of patients in the liraglutide 1.2 mg group reached weight reduction (*p* < 0.001), whereas in the 0.6 mg group 6.4% reached weight reduction (*p* < 0.001) [5]. However, there was no difference in weight reduction between liraglutide doses (absolute difference 1.2 mg vs. 0.6 mg −0.8%, 95%CI −0.12 to 0.11).

We conducted a systematic review and meta-analysis to evaluate the efficacy and safety of liraglutide in non-diabetic obese adults.

### **2. Materials and Methods**

We report the systematic review considering the guidelines of the PRISMA-2020 statement [6]. The protocol of this systematic review has been previously published in PROSPERO (CRD42020172654).

### *2.1. Search of Studies*

We searched in different search engines such as Web of Science, Pubmed, Embase, Cochrane Central and Scopus, from inception to 7 October 2021. We performed Mesh terms, Emtree terms and TIAB terms, and we designed different strategies for the selected databases (Search strategy, Supplement). We did not limit our searches by language or year of publication.

### *2.2. Eligibility Criteria*

We included studies based on: (i) randomized controlled trials (RCTs), (ii) assessed adults with obesity without diabetes type 1 or 2, (iii) evaluated liraglutide compared with placebo or other drugs. Observational studies (case-control studies or cohort), systematic reviews, case series/reports, abstract of conferences and editorials were excluded.

### *2.3. Selection of Studies*

One author (JJB) downloaded all registers, and these were added to Rayyan (https: //rayyan.qcri.org/, accessed on 23 March 2022), and duplicate records were removed. Two authors (JBM, MHR) independently reviewed the title and abstract regarding eligibility criteria. Following this step, the full-texts were screened for further evaluation. Differences in selections were addressed with a third author (AVH). Endnote 20 software (Philadelphia, PA, USA) was used for saved registers.

### *2.4. Outcomes*

Primary outcomes a were decrease in body mass index (BMI) and body weight loss. Secondary outcomes were treatment-emergent adverse events (TEAEs), hypoglycemic episodes, decrease of HbA1c, and blood pressure. The concepts and definitions of outcomes described by the authors in each of the eligible studies were applied. TEAEs are defined as undesirable or unexpected events, which are not present before medical treatment. It can also be considered as an already present event that worsens in intensity or frequency after the treatment provided [7]. TEAEs included gastro-intestinal disorders (nausea, abdominal pain, vomiting, or diarrhea), nervous system disorders, infections and infestations, and vascular disorders. Types of hypoglycemic events in non-diabetic child and adult were: (a) reactive hypoglycemia (glycemia level <70 mg/dL at the time of symptoms and relief after eating); and (b) fasting hypoglycemia (glycemia <50 mg/dL after an overnight fast, between meals, or after physical activity) [8] Specific types of hypoglycemic events for any hypoglycemia were extracted. Also, author-reported definitions were used.

### *2.5. Data Extraction and Management*

Two authors (JBM, MRH) independently extracted the data using a pre-developed standard data extraction form. Disagreements were resolved by consensus, and a third author (AVH) was consulted if needed. Data extracted per study were: name of author, year, type of research, country, number of participants, mean age, initial and maximum dosage of liraglutide, duration of treatment, and primary and secondary outcomes per trial arm with baseline values of continuous outcomes.

### *2.6. Risk of Bias Assessment*

The RoB 2.0 tool (Bristol, UK) of the Cochrane Collaboration was used for risk of bias assessment [9]. The risk of bias judged the results as low risk, some concerns, or high risk. RoB 2.0 assessment was performed independently by two authors (JBM and MRH), and discrepancies resolved by discussion or with consultation with a third author (AVH).

### *2.7. Statistical Analyses*

For meta-analysis, we performed random effects models and followed the inverse variance method. The Paule-Mandel estimator was used for the assessment of the between-study variance [10]. For continuous outcomes, effects of liraglutide on outcomes were expressed as mean difference (MD) with 95% confidence intervals (95% CIs). For dichotomous outcomes, relative risk (RR) with 95% CIs were assessed. Baseline values of continuous outcomes were adjusted for per trial arm. Statistical heterogeneity of effects among RCTs were evaluated using the I2 statistic, with values corresponding to low (<30%), medium (30–60%), and high (>60%) levels of heterogeneity. Subgroup analyses by length of treatment (≤16 versus >16 weeks) and maximum dosage (1.8 versus 3.0 mg/day) for all outcomes were performed. For sensitivity analysis, we changed the model and method of meta-analysis. With regard to the model, we applied fixed-effects, and regarding the methods, the Mantel-Haenzel method for sensitivity analyses for the primary outcomes were performed. We used the *metabin* and *metacont* functions of the meta library of R 3.5.1 (www.r-project.org, 23 March 2022). For publication bias analysis, a funnel plot was used to assess asymmetry that may indicate publication bias.

A summary of findings by GRADE methodology was used to rate the quality of evidence (QoE) per outcome [11]. Risk of bias, indirectness, imprecision, inconsistency, and publication bias were assessed, and QoE were rated as high, moderate, low, and very low. QoE was described in the summary of findings (SoF) tables; GRADEpro GDT was used to create SoF tables (GRADEpro).

### **3. Results**

### *3.1. Selection of Studies*

After the search, 2171 registers were found in all databases (Figure 1); 702 duplicate registers were deleted. Of 1469 registers, 1447 were excluded by title and abstract. Thus, 22 full-text studies were assessed for eligibility and 10 studies were excluded. Finally, 12 RCTs were included for qualitative and quantitative analyses [4,12–22].

**Figure 1.** PRISMA flow chart of the study selection process.

### *3.2. Characteristics of Included Studies*

The main characteristics of the included RCTs are summarized in Table 1. A total of 8249 adults treated with liraglutide were evaluated. The mean age was 45.9 ± 5.5 years and 24% of patients were men. Liraglutide was started at 0.6 mg/day with a progressive increase of 0.6 weekly up to a maximum of 1.8 mg/day [13,19,21] and 3.0 mg/day [4,12,14–18,20,22]. The mean duration of treatment was 35.1 ± 19.1 weeks. All studies included body weight loss as primary outcome, and other studies added inflammatory markers [13], glucose tolerance [19], proportion of individuals with T2D [4], and adverse events only [15]. At baseline, the mean Hb1Ac was 5.6% ± 0.09% in the liraglutide arm and 5.6% ± 0.07% in the control arm. Also, the mean BMI was 36.6 ± 2.6 kg/m2 in the liraglutide arm and 36.8 ± 2.9 in the control arm.

**Table 1.** Baseline

characteristics

 of included

randomized

 controlled trials.

**Table 1.** *Cont.*

### *3.3. Risk of Bias*

Overall, seven RCTs were scored as high risk of bias [12–15,20–22]. One RCT showed high risk in the randomization process [13]. Three RCTs showed high risk of deviations from intended interventions [13,15,16], and five RCTs showed high risk of missing outcome data [12–14,20,22]. The other RCTs showed low or unclear risk of bias (Supplementary Figure S1).

### *3.4. Effect on Primary Outcomes*

In comparison to placebo, liraglutide significantly reduced body weight (MD −3.35 kg; 95% CI −4.65 to −2.05; *p* < 0.0001; I2 = 100%; Figure 2A), and reduced BMI (MD −1.45 kg/m2; 95% CI −1.98 to −0.91; *p* < 0.0001; I2 = 99.5%; Figure 2B).

**Figure 2.** Forest plot of primary outcomes. (**A**): body weight, (**B**): BMI.

### *3.5. Effect on Secondary Outcomes*

Liraglutide did not significantly reduce TEAEs (RR 1.08; 95% CI 0.92 to 1.27; *p* = 0.25; I 2 = 90.2%; Figure 3a), and did not significantly increase hypoglycemic episodes (RR 2.01; 95% CI 0.37 to 11.02; *p* = 0.28; I2 = 54%; Figure 3b) in comparison to placebo. Liraglutide did not reduce Hb1Ac in comparison to placebo (MD −0.76%; 95% CI −2.24 to 0.72; *p* = 0.31; I 2 = 99.7%; Figure 3c). Finally, liraglutide significantly reduced systolic blood pressure (MD −3.07 mmHg; 95% CI −3.66 to −2.48; *p* = <0.0001; I2 = 71%; Figure 3d), and diastolic blood pressure (MD −1.01 mmHg; 95% CI −1.55 to −0.47; *p* = 0.0003; I2 = 92.2%; Figure 3e).

**Figure 3.** Forest plot of secondary outcomes. (**a**): TEAEs, (**b**): hypoglycemic episodes, (**c**): Hb1Ac, (**d**): Systolic blood pressure, (**e**): Diastolic blood pressure.

### *3.6. Subgroup Analyses*

Subgroup analyses by length of treatment and maximum dosage were like the overall analyses for all outcomes (Supplementary Figures S2–S15).

### *3.7. Sensitivity Analyses*

Effects on primary outcomes were the same, except for the effects of liraglutide vs. placebo on TEAEs, where liraglutide was associated with higher TEAEs compared to placebo (RR 1.15; 95% CI 1.12 to 1.18; *p* < 0.01) (Supplementary Figures S16 and S17).

### *3.8. Quality of Evidence*

QoE was low or very low for most of the primary and secondary outcomes (Supplementary Table S1). In body weight, body mass index, TEAEs, hypoglycemic episodes, Hb1Ac, systolic blood pressure, and diastolic blood pressure, the QoE was very low due to high risk of bias; the heterogeneity among the studies and the imprecision of the effect. In systolic blood pressure, the QoE was low with regard to moderate heterogeneity among the studies.

### *3.9. Publication Bias*

In the graphical test for publication bias, no significant asymmetry indicating high publication bias was observed (Supplementary Figure S18).

### **4. Discussion**

### *Main Findings*

In our systematic review in non-diabetic obese adults, liraglutide reduced body weight, BMI and blood pressure. However, it did not reduce TEAEs episodes or HbA1c, or the risk of hypoglycemic episodes compared with placebo. We also found that liraglutide reduced body weight, BMI, systolic blood pressure, and diastolic blood pressure in comparison to placebo. Subgroup analyses by duration of treatment and maximum dosage were like the main analyses. The risk of bias was high in 30% of the trials. The QoE was low or very low for most of the outcomes.

Liraglutide is a GLP-1 receptor agonist [23]. GLP-1 is known to be a hormone secreted in the intestine, which is activated after food ingestion by enteroendocrine L cells located in the distal jejunum and ileum [24]. It has been found that GLP-1 receptor agonists reduce cardiovascular events in people with T2D and are also a recommended treatment for weight reduction in these patients [25].

GLP-1 receptors are associated with weight loss by attenuating the fall in the anorexigenic hormone leptin that conditions this decrease [3,26]. Based on this, it has been reported that although GLP-1 can increase energy expenditure, its influence on weight is related to decreased energy intake through factors involved with the appetite reward centers of the brain and through local gastrointestinal effects [27].

Some studies have evaluated the efficacy of liraglutide for weight reduction in nondiabetic obese people. For example, a retrospective cohort study [5] evaluated the efficacy of low-dose liraglutide (0.6 vs. 1.2 mg/day) for 12 weeks on body weight among Taiwanese non-diabetic patients. The authors found that among patients in the liraglutide 1.2 mg group, 5.6% reached weight reduction compared to baseline (*p* < 0.001), whereas in the 0.6 mg group 6.4% reached weight reduction (*p* < 0.001); however, no significant differences in weight reduction were found between the two dose groups (absolute difference 1.2 mg vs. 0.6 mg −0.8%, 95%CI −0.12 to 0.11).

In a similar population, a prospective cohort study [28] evaluated the effect of liraglutide on body weight and microvascular function in non-diabetic overweight women with coronary microvascular dysfunction. The authors evaluated the intervention with Liraglutide 3 mg daily for 11 to 13 weeks of treatment, compared to a previous control stage, without treatment, for four to six weeks, and the baseline features. The authors

found that a period of 12 weeks of liraglutide 3 mg daily led to a significant weight loss vs. baseline (absolute difference −6.03 kg; 95%CI: −5.22 to −6.84; *p* < 0.001).

A systematic review and Bayesian meta-analysis of RCTs by Khera et al. [29] assessed the effects of different drugs on weight loss and adverse effects in 29,018 patients. The authors included studies that assessed obese (BMI ≥ 30) or overweight (BMI ≥ 27) adults (aged ≥18 years), with or without weight-associated comorbidities. The authors found higher odds of >5% weight loss with the liraglutide group compared to placebo (three studies, 3301 patients, OR 5.09, 95%CI 4.07 to 6.37). A network meta-analysis suggested that phentermine-topiramate, 15 mg/92 mg once daily, was associated with the highest probability of achieving at least 5% weight loss (surface under the cumulative ranking [SUCRA], 0.95), followed by liraglutide (SUCRA, 0.83) and other drugs.

In the 2016 systematic review by Khera et al. [29], the authors did not evaluate the adverse effects or hypoglycemic events. For the liraglutide versus placebo comparison, Khera et al. included 4424 patients, whereas our study included 7236 patients. The Khera et al. study included studies published before 2016. The primary and secondary outcomes were also different, as we included TEAEs, hypoglycemic episodes, body weight, BMI, systolic and diastolic blood pressure and Hb1Ac levels; and they included proportion of patients achieving at least 5% weight loss from baseline, weight loss and adverse events. We used the Cochrane Collaboration RoB 2.0 tool, whereas the study by Khera et al. did not specify the tool used. The study by Khera et al. did not perform subgroup analyses due to a small number of included studies. The inclusion and exclusion criteria between Khera et al. and our study were similar and searched the same databases, but with a different search strategy. In addition, the search and selection of abstracts and full texts was performed independently by two people in the same way as our selection has been carried out. Something in common with the Khera et al. study was the use of the GRADE methodology to evaluate QoE per outcome.

Another systematic review published by Zhang et al. [30], assessed the efficacy and safety of liraglutide in obese, non-diabetic individuals. The authors reported five RCTs involving a total of 4754 patients, and found that mean weight loss (MD = −5.52, 95% CI = −5.93 to −5.11, *p* < 0.00001); loss of more than 5% of body weight (OR = 5.46, 95% CI = 3.57 to 8.34, *p* < 0.00001), and key secondary efficacy end points: SBP decreased (the MD = −2.56, 95% CI = −3.28 to −1.84, *p* < 0.00001). These results are similar to those of our study. However, it is noteworthy that the authors reported a low risk of bias in the trials included in the meta-analysis, whereas our study reported a comprehensive risk of bias analysis, where the majority of trials were found to be at high risk of bias. Another observation is that the authors refer to having used two different models for the meta-analysis, and did not consider the implicit heterogeneity among the studies, and there is no exact distinction about the model applied. Our study, on the other hand, used the random effects model for all meta-analyses under the assumption of heterogeneity and differences between studies.

### **5. Limitations**

We have identified several limitations. First, there were differences in the starting and maintenance dose of liraglutide. However, we did not find differences in the weight loss effects between lower or higher liraglutide doses. Second, there was a difference in follow-up time among studies. Most of the included studies had a follow-up time longer than 17 weeks, and our subgroup analyses showed no difference between shorter and longer follow up times. Third, the risk of bias in most studies was high, which may compromise the true effect of most of the outcomes described, as in other studies that applied meta-analysis with included studies and high risk of bias [31–34]. Finally, in the evaluation of the QoE using GRADE methodology, we found low and very low quality of evidence for most outcomes, which should be considered when interpreting the significant effects that may favor the treatment.

### **6. Conclusions**

In non-diabetic obese adults, liraglutide reduced body weight, BMI, and blood pressure in comparison to placebo. TEAEs rates, Hb1Ac and hypoglycemic episodes were not different than placebo. However, the effects in the outcomes may have been compromised due to the true effect related to the high risk of bias in the most studies, and the low or very low level of recommendation in GRADE.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/jcm11112998/s1. Figure S1: Risk of bias assessment of included trials. Figure S2: Subgroup analyses by length of treatment of the effects of Liraglutide vs placebo on TEAEs. Figure S3: Subgroup analyses by doses of the effects of Liraglutide vs placebo on hypoglycemia TEAES. Figure S4: Subgroup analyses by length of treatment of the effects of Liraglutide vs placebo on hypoglycemia episodes. Figure S5: Subgroup analyses by doses of the effects of Liraglutide vs placebo on hypoglycemia episodes. Figure S6: Subgroup analyses by length of treatment of the effects of Liraglutide vs placebo on body weight loss. Figure S7: Subgroup analyses by doses of the effects of Liraglutide vs placebo on body weight loss. Figure S8: Subgroup analyses by length of treatment of the effects of Liraglutide vs placebo on BMI. Figure S9: Subgroup analyses by doses of the effects of Liraglutide vs placebo on BMI. Figure S10: Subgroup analyses by length of treatment of the effects of Liraglutide vs placebo on SBP. Figure S11: Subgroup analyses by doses of the effects of Liraglutide vs placebo on SBP. Figure S12: Subgroup analyses by length of treatment of the effects of Liraglutide vs placebo on DBP. Figure S13: Subgroup analyses by doses of the effects of Liraglutide vs placebo on DBP. Figure S14: Subgroup analyses by length of treatment of the effects of Liraglutide vs placebo on Hb1Ac. Figure S15: Subgroup analyses by doses of the effects of Liraglutide vs placebo on Hb1Ac. Figure S16: Sensitivity analyses of the effects of Liraglutide vs placebo on body weight loss. Figure S17: Sensitivity analyses of the effects of Liraglutide vs placebo on BMI. Figure S18: Publication bias. Table S1: GRADE summary of findings table.

**Author Contributions:** J.J.B., M.R.H., B.M., C.D.-A., G.V.-R. and A.V.H.: conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, writing—review and editing, visualization, supervision. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare that they have no conflict of interest.

### **References**

## *Article* **Association between Add-On Dipeptidyl Peptidase-4 Inhibitor Therapy and Diabetic Retinopathy Progression**

**Eugene Yu-Chuan Kang 1,2, Chunya Kang 3, Wei-Chi Wu 1,2, Chi-Chin Sun 2,4,5, Kuan-Jen Chen 1,2, Chi-Chun Lai 1,2,4, Tien-Hsing Chen 2,5,6,\* and Yih-Shiou Hwang 1,2,\***

**Abstract:** This study aimed to investigate the association of add-on dipeptidyl peptidase-4 inhibitor (DPP4i) therapy and the progression of diabetic retinopathy (DR). In this retrospective populationbased cohort study, we examined Taiwanese patients with type 2 diabetes, preexisting DR, and aged ≥40 years from 2009 to 2013. Prescription of DPP4i was defined as a medication possession ratio of ≥80% during the first 6 months. The outcomes included vitreous hemorrhage (VH), tractional retinal detachment, macular edema, and interventions including retinal laser therapy, intravitreal injection (IVI), and vitrectomy. Of 1,767,640 patients, 62,824 were eligible for analysis. After matching, the DPP4i and non-DPP4i groups each contained 20,444 patients. The risks of VH (*p* = 0.013) and macular edema (*p* = 0.035) were higher in the DPP4i group. The DPP4i group also had higher risks of receiving surgical interventions (retinal laser therapy (*p* < 0.001), IVI (*p* = 0.049), vitrectomy (*p* < 0.001), and any surgical intervention (*p* < 0.001)). More patients in the DPP4i group received retinal laser therapy (*p* < 0.001) and IVI (*p* = 0.001) than in the non-DPP4i group. No between-group differences in cardiovascular outcomes were noted. In the real-world database study, add-on DPP4i therapy may be associated with the progression of DR in patients with type 2 diabetes. No additional cardiovascular risks were found. The early progression of DR in rapid glycemic control was inconclusive in our study. The possible effect of add-on DPP4i therapy in the progression of DR in patients with type 2 diabetes requires further research.

**Keywords:** dipeptidyl peptidase-4 inhibitor; diabetes mellitus; diabetic retinopathy; progression

### **1. Introduction**

Diabetic retinopathy (DR), a common microvascular complication in patients with diabetes, is also a major cause of blindness in working-age adults [1]. The global number of patients with diabetes is estimated to reach 600 million by 2040, one-third of whom are expected to have DR [2]. Severe DR can lead to complications such as vitreous hemorrhage (VH), tractional retinal detachment (RD), and macular edema [3,4]. DR and its complications may require surgical intervention such as retinal laser therapy, intravitreal injection (IVI) of anti-vascular endothelial growth factor, and in some cases vitrectomy [3,4]. This imposes a substantial economic burden on patients with such conditions and their families [5].

Numerous studies have been conducted on preventing or slowing the progression of diabetic complications. A randomized controlled trial reported that appropriate glucose-

**Citation:** Kang, E.Y.-C.; Kang, C.; Wu, W.-C.; Sun, C.-C.; Chen, K.-J.; Lai, C.-C.; Chen, T.-H.; Hwang, Y.-S. Association between Add-On Dipeptidyl Peptidase-4 Inhibitor Therapy and Diabetic Retinopathy Progression. *J. Clin. Med.* **2021**, *10*, 2871. https://doi.org/10.3390/ jcm10132871

Academic Editor: Fernando Gómez-Peralta

Received: 3 June 2021 Accepted: 28 June 2021 Published: 28 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

lowering reduced the risk of cardiovascular diseases, microvascular complications, and all-cause mortality in patients with diabetes [6]. Another randomized controlled trial indicated that intensive glucose control effectively slowed DR progression in patients with type 2 diabetes [7]. Treatment for systemic conditions, such as hypertension and dyslipidemia, has been demonstrated to be associated with a low risk of DR development or progression [7,8].

Dipeptidyl peptidase-4 (DPP4) inhibitors (DPP4i) are a class of oral hypoglycemics, of which the first agent sitagliptin was approved in 2006 by the US Food and Drug Administration [9]. DPP4i suppress the function of DPP4 and indirectly prolong the serum level of glucagon-like peptide-1 (GLP-1), increasing insulin secretion and reducing glucagon secretion from the pancreas [10]. Although a meta-analysis reported that DPP4i exerted a better hypoglycemic effect than α-glucosidase inhibitors [11], other studies have observed associations between its use and an increased risk of heart failure [12,13]. Moreover, another meta-analysis indicated no beneficial association between DPP4i use and all-cause mortality [14]. Regarding DPP4i use in DR, sitagliptin prevented the effect of diabetes on the blood-retinal barrier in male Zucker diabetic fatty rats. Specifically, it improved endothelial function and prevented inflammation, nitrative stress, and apoptosis in animals [15]. However, the association between DPP4i and DR has not been fully characterized [16,17]. The first clinical study of the possible protective effects of DPP4i on DR progression, published in 2016, included 28 patients with type 2 diabetes [18]. A 2018 population-based study by Kim et al. that used data from the South Korean National Health Insurance Service reported a possible association of DPP4i use with an increased risk of DR events early in the treatment phase [19]. Using the same database, Chung et al. found a neutral association between DPP4i use and sulfonylurea added to metformin therapy and the risk of DR progression. The aggravation of DR by DPP4i remains a concern and requires more clinical investigation [20]. In this study, we investigated the association between add-on DPP4i therapy and DR progression in patients with type 2 diabetes and preexisting DR in a real-world setting.

### **2. Materials and Methods**

### *2.1. Data Source*

This retrospective population-based cohort study was conducted using the Taiwan National Health Insurance (NHI) Research Database (NHIRD) (Center for Biomedical Resources of National Health Research Institutes, Miaoli, Taiwan). More than 99.8% of the population in Taiwan (approximately 23.7 million people as of 2020) is covered by the NHI program, a single-payer system established in March 1995. The NHIRD contains de-identified information including medical claims data. Information on the NHI program and its databases has been described in detail in previous publications [21,22]. The present study was approved by the Chang Gung Memorial Hospital Ethics Institutional Review Board (IRB No. 201800199B1) and adheres to the principles of the Declaration of Helsinki.

### *2.2. Inclusion and Exclusion Criteria*

From 2009 to 2013, we identified patients with diabetes in the NHIRD by using the diagnostic codes of the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). These codes were validated in a study on the accuracy of diabetes diagnosis in NHI claims data. Specifically, at least four outpatient visits for diabetes corresponded to a 95.7% accuracy [23]. Another study observed that a prescription of any oral hypoglycemic agent corresponded to an accuracy of 99% [24]. Therefore, in the present study, we included patients with at least five outpatient diagnoses of type 2 diabetes who were also taking any oral hypoglycemics. Patients with type 2 diabetes and preexisting DR were included in the analysis. We excluded patients who were aged under 40 years as well as those with missing demographic data, type 1 diabetes, retinal disorders (including retinal vascular occlusion, separation of retinal layers, retina degeneration, and chorioretinal inflammation), a history of receiving vitreoretinal interventions (including IVI, retinal laser therapy, scleral buckling, and vitrectomy), or were followed up for less than 6 months (Figure 1).

**Figure 1.** Flowchart of the inclusion and exclusion criteria of the patients. DR, diabetes retinopathy; DPP4i, dipeptidyl peptidase 4 inhibitors.

### *2.3. Group Definition*

The index date of the DPP4i group was defined as the date of the first DPP4i prescription between 2009 and 2013. To prevent the immortal time bias, the index date of the non-DPP4i group was assigned as the index date of the DPP4i group through an approach known as prescription time-distribution matching [25]. To ascertain the compliance of DPP4i use, patients in the DPP4i group with a medication possession ratio (MPR) of less than 80% during the first 6 months of follow-up [26], specifically 144 days (180 days × 0.8), were excluded from further analysis (Figure 1).

### *2.4. Outcomes*

In this study, the primary ocular outcome was the composite DR outcome, which consisted of any one of the following: VH, tractional RD, and macular edema. The secondary ocular outcome was the composite outcome of any surgical intervention, namely retinal laser therapy, IVI, and vitrectomy. The cardiovascular outcomes, including myocardial infarction, hospitalization for heart failure, ischemic stroke, and hemorrhagic stroke, were defined as safety outcomes. The primary DR outcome and its components were defined as diagnosis after at least three outpatient diagnoses or one inpatient diagnosis. The surgical interventions and other ocular outcomes were examined using the Taiwan NHI reimbursement codes from the claims data for outpatient and inpatient visits. The occurrence of safety outcomes was determined using the principal discharge diagnosis. Mortality and cardiovascular events selected for analysis have been validated previously [27,28].

### *2.5. Covariates*

Covariates were sex, age, proxy variables for compliance (i.e., the number of outpatient visits for diabetes management), proxy variables for DR severity (previous proliferative DR and previous DR duration), comorbidities as well as scores on the Charlson Comorbidity Index, indicators for diabetic severity (diabetes duration, diabetic neuropathy, and diabetic foot ulcer), and concomitant medications. Comorbidities, namely dyslipidemia, hypertension, ischemic heart disease, chronic kidney disease, peripheral arterial disease, ischemic stroke, heart failure, and atrial fibrillation, were confirmed after at least three outpatient diagnoses or one inpatient diagnosis in the previous year. Medications during the first 6 months of follow-up were classified into three categories: antidiabetics, antihypertensives, and other medications. Details of the ICD-9-CM diagnostic codes used in this study are provided in Supplementary Materials (Table S1). The Charlson Comorbidity Index scores were calculated as described previously [29].

### *2.6. Statistics*

To reduce confounding effects, the analysis of differences in outcomes between the DPP4i and non-DPP4i groups was performed after propensity score matching (PSM). The propensity score was the predicted probability given the value of the covariates, which was calculated using a multivariable logistic regression model in which the study groups (1: DPP4i and 0: non-DPP4i) were regressed on the selected covariates. The matching was processed using a greedy nearest-neighbor algorithm with a caliper of 0.2 times the standard deviation of the logit of the propensity score. The matching order was random, and replacement was not allowed. Each patient in the DPP4i group was matched with a non-DPP4i control. The matching quality was assessed after PSM by using the absolute value of the standardized difference between the groups, where a value of less than 0.1 was considered negligible.

The Fine–Gray subdistribution hazard model, which considers all-cause mortality a competing risk, was used to compare the occurrence of time-to-event outcomes between the groups. The average number of surgical interventions per decade was also analyzed and compared using the Poisson model, in which the natural logarithm of the follow-up duration was an offset variable. The study groups (DPP4i vs. non-DPP4i) were the only explanatory variable in the regression analysis. The within-pair clustering of outcomes after PSM was accounted for by using robust standard errors through the generalized estimating equation approach [30]. Further subgroup analyses were conducted to evaluate the consistency of the observed treatment effect on the specified outcomes across different levels of subgroup variables. The outcomes of interest comprised the primary and secondary endpoints, namely the composite DR outcome and the composite outcome of any surgical intervention, respectively. The selected subgroups were sex, age (dichotomized at 65 years), previous proliferative DR, hypertension, dyslipidemia, ischemic heart disease, ischemic stroke, chronic kidney disease, peripheral arterial disease, diabetes duration (dichotomized at 10 years), diabetic neuropathy, diabetic foot ulcer, and the use of concomitant antidiabetics (e.g., metformin, sulfonylurea, thiazolidinediones, alpha-glucosidase inhibitors, meglitinides, and insulin). A two-sided *p*-value of <0.05 was considered to be significant. All analyses were performed using SAS software, Version 9.4 of the SAS System (SAS Institute Inc., Cary, NC, USA), including the % cif macro for generating cumulative incidence functions under the Fine–Gray sub-distribution hazard method.

### **3. Results**

### *3.1. Participants*

Between 2009 and 2013, a total of 1,767,640 patients with diabetes were identified. After the exclusion of patients aged under 40 years as well as those with type 1 diabetes, missing demographic data, and no DR diagnosis, 213,765 patients remained. We further excluded patients who were followed up for less than 6 months or developed any of the primary or secondary ocular outcomes within 6 months after the index date, as well as those with retinal disorders, a history of receiving vitreoretinal interventions or who had an MPR of less than 80%. After these procedures, 62,824 patients remained. After 1:1 PSM, the non-DPP4i and DPP4i groups comprised 20,444 patients each (Figure 1).

### *3.2. Demographic Characteristics*

Table 1 presents the demographic characteristics of the study groups before and after matching. Before matching, the patients in the DPP4i group were younger; had more outpatient visits for diabetes management in the previous year; were more likely to have undergone a dilated fundus examination in the previous year; had a higher prevalence of dyslipidemia; had a longer diabetes duration; had more prescriptions of sulfonylurea, alpha-glucosidase inhibitors, meglitinides, beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers, antiplatelets, statins, and fenofibrates, and fewer prescriptions of insulin. After matching, the two groups were well balanced in

terms of sex, age, comorbidities, indicators for diabetic severity, underlying ocular diseases, medications, and follow-up duration.

**Table 1.** Characteristics of patients with type 2 diabetes and diabetic retinopathy before and after matching. Balance achieved between the DPP4i and non-DPP4i groups after matching.

DDP4i, dipeptidyl peptidase 4 inhibitor; STD, standardized difference; DR, diabetic retinopathy. Data are presented as frequency (percentage) or mean ± standard deviation.

### *3.3. Primary Ocular Outcomes*

Table 2 presents the primary ocular outcomes of the patients, including any surgical intervention taken. Over a mean follow-up duration of 2.5 years, 366 and 294 patients (1.8% and 1.4%, respectively) in the DPP4i and non-DPP4i groups developed the primary ocular outcome, namely the composite DR outcome. The risk of developing the composite DR outcome was significantly higher in the DPP4i group (sub-distribution hazard ratio [SHR] 1.23, 95% confidence interval [CI] 1.06–1.44; Figure 2A). Among the individual components of the composite DR outcome, the risks of VH (SHR 1.24, 95% CI 1.05–1.48) and macular edema (SHR 1.48, 95% CI 1.03–2.13) were significantly higher in the DPP4i group.

**Table 2.** Primary ocular outcomes, including any surgical intervention taken, of patients with type 2 diabetes and diabetic retinopathy demonstrating significantly higher risks of composite diabetic retinopathy and surgical interventions in the DPP4i group.

DDP4i, dipeptidyl peptidase 4 inhibitor; SHR, sub-distribution hazard ratio; CI, confidence interval; RD, retinal detachment; DR, diabetic retinopathy; RR, rate ratio; VH, vitreous hemorrhage; IVI, intravitreal injection. \* Estimated using a Poisson model in which the logarithm of follow-up duration was treated as an offset variable.

**Figure 2.** Cumulative incidence function of (**A**) composite diabetic retinopathy outcome and (**B**) composite outcome of any surgical intervention between the DPP4i and non-DPP4i group after propensity score matching. DPP4i, dipeptidyl peptidase 4 inhibitor; CI, confidence interval.

The DPP4i group also had a higher risk of receiving surgical intervention for severe DR or its complications (retinal laser therapy: SHR 1.75, 95% CI 1.33–2.30; IVI: SHR 1.32, 95% CI 1.001–1.74; vitrectomy: SHR 1.32, 95% CI 1.24–1.40; any surgical intervention: SHR 1.40, 95% CI 1.26–1.55; Figure 2B). As for the number of interventions, more patients in the DPP4i group received retinal laser therapy (rate ratio (RR) 1.39, 95% CI 1.23–1.58) and IVI (RR 1.84, 95% CI 1.28–2.63) than in the non-DPP4i group.

### *3.4. Safety Outcomes*

The results of the safety outcomes are shown in Table 3. No between-group differences were observed in any of the safety outcomes, namely myocardial infarction, hospitalization for heart failure, ischemic stroke, hemorrhagic stroke, and the composite outcome of major adverse cardiovascular events.

**Table 3.** Safety outcomes of patients with type 2 diabetes and diabetic retinopathy showing no significant risk in both groups.

DDP4i, dipeptidyl peptidase 4 inhibitor; SHR, sub-distribution hazard ratio; CI, confidence interval. \* Any one of myocardial infarction, heart failure, or stroke.

### *3.5. Subgroup Analysis*

We further conducted subgroup analysis on the primary composite DR outcome and the composite outcome of any surgical interventions. The results showed that the observed hazardous effect of DPP4i on the risk of primary composite DR outcome was particularly obvious in the following population: females, younger patients, patients with relatively shorter diabetes duration, and those without taking insulin (All *p*-values for interaction <0.05; Figure 3A). Similarly, the observed increased risk of the composite outcome of any surgical interventions due to DPP4i was more apparent in patients with relatively shorter diabetes duration, and those who took sulfonylurea, and those without insulin therapy (All *p*-values for interaction <0.05; Figure 3B).

**Figure 3.** *Cont*.

**Figure 3.** Subgroup analysis of (**A**) composite diabetic retinopathy outcomes and (**B**) composite outcome of any surgical interventions of the diabetic patients with DR between the DPP4i users and non-DPP4i controls in the propensity score-matched cohort. The red color indicates a statistical significance.

### **4. Discussion**

The use of DPP4i in glucose-lowering for diabetes has increased considerably over the past decade after being introduced in 2006 [31]. To reduce mortality and morbidity, measuring drugs' protective effects and related diabetes complications are essential. As mentioned, DR, a major microvascular complication in diabetes, can cause severe visual impairment. Thus, in this population-based study, we evaluated the association between the add-on DPP4i therapy and the progression of preexisting DR in patients with type 2 diabetes aged ≥ 40 years. During the 2.5-year follow-up, the add-on DPP4i therapy was associated with increased risks of composite DR outcome and needs of surgical interventions. However, it did not increase the risk of cardiovascular events.

The association between DPP4i and DR remains a matter of contention in the literature. A study including 82 patients with type 2 diabetes reported that DPP4i use had protective effects on DR progression [18]. A study using a cohort representative of individuals in the US population aged ≥ 65 years observed that DPP4i use had a neutral effect on DR [32]. Some other studies have found that DPP4i cause adverse retinal outcomes. In the Trial Evaluating Cardiovascular Outcomes With Sitagliptin (TECOS), DR occurred more frequently in patients under add-on sitagliptin therapy than in those who were not (2.8% vs. 2.2%) [33]. Another study, using a sample representative of the South Korean population, also indicated an increased risk of DR in early DPP4i treatment (<12 months) [19]. These findings indicate that the pharmacodynamic or effects of DPP4i may vary with population or patient characteristics.

The non-DPP4i and DPP4i groups in the present study comprised 20,444 patients (after matching) with type 2 diabetes (mean duration of 11 years since onset) and preexisting DR, respectively. VH and macular edema occurred significantly more frequently in the DPP4i group than in the non-DPP4i group. Furthermore, patients in the DPP4i group under add-on DPP4i therapy for diabetes control were more likely to receive surgical intervention for advanced DR. In short, add-on DPP4i therapy increased the risk of DR progression. However, no significant between-group differences in safety outcomes were noted. In addition, DPP4i was not associated with an increased risk of cardiovascular events.

Although the exact mechanism remains uncertain, biochemical changes in retinal cells after DPP4i administration in experimental studies have been inconsistent. Numerous laboratory studies have reported the protective effects of DPP4i on retinal health. For example, Gonçalves et al. found that sitagliptin had an antioxidative effect on rat retinas [34]. In another of their studies, sitagliptin ameliorated bovine retinal endothelial dysfunction caused by inflammation [15]. Another study noted that linagliptin had anti-angiogenic effects on mice with oxygen-induced retinopathy [35]. However, Lee et al. indicated that DPP4i caused disruptions in endothelial cell-to-cell junctions by accumulating stromal cell-derived factor 1α and phosphorylating vascular endothelial cadherin, as well as further increasing retinal vascular permeability [36]. In a 2020 experimental study, the results revealed that prolonged DPP4 inhibition destabilized the blood-retina barrier, potentially inducing retinal edema [37]. Early deterioration of DR was also reported in a GLP-1 analog, semaglutide, although the pharmacodynamic may be different with the DPP4i [38]. Retinal changes under DPP4i therapy may depend on the duration of DPP4i treatment and the severity of diabetes and its complications. Long-term administration of DPP4i in patients with preexisting DR might induce the development of excess vasculature as well as vascular permeability, potentially contributing to exudate production and further exacerbating DR. Thus, more awareness of DR progression may be necessary for patients under long-term DPP4i treatment.

Cardiovascular complications of DPP4i remain the topic of an ongoing debate. Some studies have reported a decreased risk of cardiovascular events after DPP4i therapy [39,40]. By contrast, other studies have indicated that DPP4i use increased the risk of cardiovascular disorders [17,41]. In our study, the safety outcomes (including myocardial infarction, heart failure, ischemic stroke, hemorrhagic stroke, and composite cardiovascular outcomes) did not differ significantly between the groups. This is consistent with the assessment from the TECOS [33], the Examination of Cardiovascular Outcomes with Alogliptin versus Standard of Care, and the Saxagliptin Assessment of Vascular Outcomes Recorded in Patients with Diabetes Mellitus (SAVOR)—Thrombolysis in Myocardial Infarction (TIMI) [12]. Thus, our additional finding also supported a neutral association between DPP4i use and the occurrence of major adverse cardiovascular events.