
==== Front
Rheumatology (Oxford)
Rheumatology (Oxford)
brheum
Rheumatology (Oxford, England)
1462-0324
1462-0332
Oxford University Press

38552312
10.1093/rheumatology/keae204
keae204
Systematic Review and Meta Analysis
AcademicSubjects/MED00360
Risk of diabetes mellitus in systemic lupus erythematosus: systematic review and meta-analysis
Etchegaray-Morales Ivet Department of Rheumatology, Medicine School, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico

Mendoza-Pinto Claudia Department of Rheumatology, Medicine School, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico
Systemic Autoimmune Diseases Research Unit, Specialties Hospital UMAE- CIBIOR, Instituto Mexicano del Seguro Social, Puebla, Mexico

Munguía-Realpozo Pamela Department of Rheumatology, Medicine School, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico
Systemic Autoimmune Diseases Research Unit, Specialties Hospital UMAE- CIBIOR, Instituto Mexicano del Seguro Social, Puebla, Mexico

Solis-Poblano Juan Carlos Department of Haematology, Specialties Hospital UMAE, Instituto Mexicano del Seguro Social, Puebla, Mexico

Méndez-Martínez Socorro Coordination of Health Research, Instituto Mexicano del Seguro Social, Puebla, Mexico

Ayón-Aguilar Jorge Coordination of Health Research, Instituto Mexicano del Seguro Social, Puebla, Mexico

Abud-Mendoza Carlos Department of Rheumatology, Hospital Central Dr Ignacio Morones Prieto, San Luis Potosí, Mexico

García-Carrasco Mario Department of Rheumatology, Medicine School, Benemérita Universidad Autónoma de Puebla, Puebla, Mexico

https://orcid.org/0000-0001-6085-492X
Cervera Ricard Department of Autoimmune Diseases, Reference Centre (UEC/CSUR) for Systemic Autoimmune Diseases of the Catalan and Spanish Health Systems, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain

Correspondence to: Claudia Mendoza-Pinto, Systemic Autoimmune Diseases Research Unit, Specialties Hospital UMAE- CIBIOR, Mexican Institute for Social Security, Calle 2 Nte 2004, Centro, Puebla, Mexico. E-mail: cmendozap.26@gmail.com
I.E.-M. and C.M.-P. contributed equally.

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© The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

Objective

To investigate the risk of DM and evaluate the impact of SLE therapies on the risk of developing DM in patients with SLE.

Methods

Electronic database searches of PubMed, Embase, Cochrane Library and Web of Science were performed from inception to February 2023. Cohort and cross-sectional studies that analysed the risk of DM in patients with SLE were included. The associations between diabetes and antirheumatic agents, such as antimalarials and glucocorticoids, were analysed in cohort studies. Data were pooled using fixed- or random-effects meta-analysis to estimate pooled odd ratios (OR), relative risks (RR) and 95% confidence intervals (CIs). This study was registered with PROSPERO (CRD42023402774).

Results

A total of 37 studies (23 cross-sectional and 14 cohort studies) involving 266 537 patients with SLE were included. The pooled analyses from cross-sectional studies and cohort studies did not show an increased risk of DM in SLE patients (OR = 1.05, 95% CI 0.87–1.27; P = 0.63 and RR = 1.32, 95% CI 0.93–1.87; P = 0.12, respectively). However, several cohort studies consistently demonstrated a reduced risk of diabetes with antimalarials, while glucocorticoid use has been associated with an increased risk of developing diabetes. Age, sex, hypertension and immunosuppressants have not been identified as risk factors for DM in SLE patients.

Conclusion

Although there was no increased risk of DM in patients with SLE compared with controls, HCQ users or adherents had a decreased risk, whereas glucocorticoid users had an increased risk.

systemic lupus erythematosus
diabetes mellitus
risk factors
meta-analysis
==== Body
pmcRheumatology key messages Our study showed that patients with SLE did not have an increased risk of diabetes compared to controls.

Antimalarials were associated with a decreased risk of diabetes and glucocorticoids with increased risk in SLE patients.

Introduction

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that increases the risk of severe clinical outcomes and mortality [1]. SLE not only contributes to an increased burden of chronic disease [2] but may also increase the risk of cardiovascular morbidity compared with persons without SLE [3]. Diabetes mellitus (DM) is among the most relevant cardiovascular risk factors; however, the association between SLE and DM morbidity remains uncertain [4]. Given the massive burden of cardiovascular disease in patients with SLE, potentially due to traditional risk factors such as DM, it is thus of particular interest to prevent DM development in patients with SLE. Patients with SLE are found to have altered glucose metabolism and insulin resistance, provoked in part by adipocytokines [5], but also by central obesity, hypertension, use of glucocorticoids, elevated serum levels of C-peptide [6] and hypovitaminosis D [7]. In addition, increased levels of C-reactive protein and pro-inflammatory cytokines predict the development of type 2 DM [8]. On the other hand, decreasing SLE activity and inflammation with immunomodulators could indirectly reduce the risk of DM in those patients. According to some cohort studies, hydroxychloroquine (HCQ) has been shown to reduce the risk of DM in patients with systemic autoimmune rheumatic diseases such as RA [9, 10].

Diverse cross-sectional studies have shown that SLE influences the development of DM [3, 11, 12]. However, other studies have not suggested significant differences in DM frequency between patients with SLE and healthy controls [13–15]. Therefore, in the present study, we performed a systematic review of the literature and a meta-analysis to analyse the relationship between SLE and DM. In addition, we evaluated the relationship between antirheumatic agents and the risk of DM in patients with SLE.

Material and methods

Registration

We followed the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to perform and report our systematic review [16]. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (registration number CRD42023402774).

Search strategy and study selection

A systematic search was carried out, utilizing a strategy that included both MeSH terms and free text words related to ‘Systemic lupus erythematosus’ and ‘diabetes’. We searched the PubMed, Embase, Cochrane Library and Web of Science datasets from their inception to February 13, 2023. Additionally, conference abstracts were also considered. The complete search strategy is provided in Supplementary Table S1, available at Rheumatology online. Finally, the references of relevant studies were reviewed to identify further potential articles. No language restrictions were required.

We selected cross-sectional and cohort studies that assessed the relationship between SLE and the risk of diabetes. We excluded studies that did not report a diagnosis of diabetes and focused only on hyperglycaemia or impaired glucose tolerance. Incident diabetes was categorized as newly diagnosed diabetes reported in studies according to the International Classification of Diseases (ICD) or physician diagnosis, or fasting plasma glucose (FPG) ≥126 mg/dl (7.0 mmol/l) or 2-h plasma glucose ≥200 mg/dl (11.1 mmol/l). Editorials, reviews and letters were not included. When more than one study was from the same cohort, we included those with the largest sample size and a longer follow-up duration.

All retrieved references were uploaded to the open-source Rayyan software. Two authors independently evaluated the eligibility of the studies, including the title and abstract and further evaluated the full texts for possible eligibility. Discrepancies were resolved through discussion or arbitration by a third reviewer.

Data extraction

Two authors, in an independent fashion, extracted the following data according to a predefined data collection form: author, year of publication, country, study design, sample size, duration of follow-up, demographic characteristics (sex, age and race), type of control, clinical features (disease duration, disease activity, body mass index, lupus nephritis, etc), treatments such (antimalarials, glucocorticoids and immunosuppressive drugs), the rate or the risk of diabetes, 95% confidence intervals (IC) and confounders. The pooled effect of diabetes was calculated using the risk for all types of diabetes described in the original studies.

Risk of bias of included studies

Two authors (PMR and IEM) independently evaluated the risk of bias in the included studies. The risk of bias in cohort or cross-sectional studies was appraised independently by two reviewers using the Newcastle-Ottawa Scale (NOS) [17], a tool specifically developed to assess the quality of observational studies. The scoring systems cover three major domains: selection of cohorts or case-controls (maximum four points), comparability of selected groups (maximum two points) and ascertainment of either the exposure or the outcome of interest (maximum three points), with a score ranging from 0 to 9; a higher score represents better methodological quality. While there is no validated cut-off value to distinguish between good or poor quality studies, studies with a score ≥6 were categorized as being of high quality. A discrepancy in the risk assessment was discussed by a third reviewer (CMP). We also used the modified GRADE (Grading of Recommendations, Assessment, Development and Evaluation) framework for prognosis to assess and grade the overall certainty of evidence for each outcome, taking into account the RoB within the studies, directness of evidence, heterogeneity, precision of effect estimates and risk of publication bias [18].

Data synthesis

The primary outcome was the association between DM and SLE. The secondary outcome was the association between SLE treatment and DM. To summarize the results of selected studies evaluating the primary outcome, we used odds ratios (ORs) for cross-sectional studies, relative risks (RRs) for cohort studies and corresponding 95% CI. In cohort studies, the RRs were shown with adjustment for potential confounders to assess the risk of DM in patients with SLE. I2 measured the heterogeneity across the studies. To estimate the effect values, we used the random-effects models (DerSimonian and Laird method) in most of our analyses, considering that most studies showed non-statistical heterogeneity.

The robustness of the result was evaluated using a jackknife sensitivity analysis (i.e. the analysis was repeated multiple times, each time with the removal of a single study from the total group of studies).

Publication bias was visually estimated, assessed using funnel plots and confirmed using Egger’s regression test. Analyses were performed using the Meta-Analysis Statistical Software Review Manager (RevMan), version 5.4.1. RevMan used the generic inverse variance method to perform pools of adjusted RR for cohort studies. Narrative synthesis was an alternative option for reporting findings when meta-analysis was not possible, particularly for the secondary outcome (patient subgroups).

Results

Study selection and characteristics

Our electronic search yielded 3179 records, of which 114 were potentially eligible (Flow chart, Fig. 1). After full-text reviews, we included 37 studies (Supplementary Table S2, available at Rheumatology online reasons for exclusion). Tables 1–3 summarize the main characteristics of the included studies. A total of 23 cross-sectional studies were included to evaluate the risk of diabetes between SLE patients and controls, fourteen longitudinal cohort studies. Fourteen included population from the United States and Canada [3, 14, 19–30], nine from Europe [15, 31–38], eight from Asia [11, 12, 39–44], six from Latin America [13, 45–49]. Patients were recruited from hospitals (21 studies) or population-based registries (16 studies).

Figure 1. PRISMA flow diagram of the screening and selection process according to PRISMA 2020 guidelines

Table 1. Characteristics of cross-sectional studies

Author, year	Country	Participants source	Diabetes criteria	SLE, n	Control, n	Mean age, years SLE/controls	Female %	NOS	
Bruce I, 2003 [3]	Canada	University of Toronto, Lupus Clinic	Medical records, laboratory test	250	250	44.5/44.1	100	7	
De Souza AWS, 2005 [13]	Brazil	Universida de Federal de Sao Paulo	Medical records, laboratory test	82	62	34.0/35.7	100	7	
Sada KE, 2006 [11]	Japan	Okayama University Hospital	Medical records, laboratory test	37	80	44.0/44.0	NR	5	
Estevez del Toro M, 2008 [45]	Cuba	Hospital de Meijer’s	Medical records, laboratory test	51	51	37.9/37.8	90.2	6	
Rho YH, 2008 [14]	US	Vanderbilt University

Hospital

	Medical records, laboratory test	109	78	40.2/40.5	91.7	6	
Santos MJ, 2010 [15]	Portugal	Hospital Garcia de Orta	Medical records, laboratory test	100	102	44.6/49.2	100	8	
Baker JF, 2011 [23]	US	University of Pennsylvania clinics	Medical records, laboratory test	159	70	43.2/40.2	100	6	
Yang L, 2012 [12]	China	Huazhong University of Science and Technology	Medical records, laboratory test	139	139	34.5/34.4	85.0	6	
Romero-Diaz J, 2012 [46]	Mexico	Instituto Nacional de Ciencias Médicas y Nutrición	Medical records, laboratory test	139	139	31.8/32.3	93.0	6	
Hassan AA, 2013 [40]	Saudia Arabia	Al Ahsa

Hospital

	Medical records, laboratory test	120	100	32.0/30.0	91.6	5	
Kaul MS, 2013 [24]	US	Duke University

Medical Center

	Medical records, laboratory test	86	258	49.0/70.0	98.8	8	
Liu SY, 2013 [41]	China	Hospital of Zhengzhou University	Medical records, laboratory test	116	115	34.1/36.2	91.6	6	
Chuang YW, 2015 [39]	Taiwan	National Health Insurance Research Database	ICD-9	10144	10144	41.0/41.0	86.5	8	
Das Chagas-Medeiros MM, 2016 [47]	Brazil	Walter Cantídio University Hospital	Medical records, laboratory test	146	101	41.7/32.7	91.8	8	
Gustafsson JT, 2017 [31]	Sweden	Karolinska University Hospital Solna	Medical records, laboratory test	281	281	49.0/49.0	93.0	8	
Dregan A, 2017 [32]	UK	UK Biobank data	ICD-10	654	483559	42.0/57.0	89.00	8	
Sinicato NA, 2017 [48]	Brazil	Pediatric Rheumatology Outpatient Clinic of State University of Campinas	Medical records, laboratory test	76	54	16.7/16.8	90.8	7	
Mobini M, 2018 [33]	Italy	Mazandaran University of Medical

Sciences

	Medical records, laboratory test	73	73	40.9/41.4	95.8	3	
Romero-Diaz J, 2018 [49]	Mexico	Instituto Nacional de Ciencias Médicas y Nutrición	Medical records, laboratory test	95	100	34.7/34.8	0.0	7	
Levinson DJ, 2020 [25]	US	National Inpatient Sample (NIS) of the Health Care Utilisation Project	ICD-9	167466	167466	50.7/NR	88.8	8	
McVeigh ED, 2021 [26]	US	University of Kentucky Health

System database

	ICD-10	20	60	NR	65.0	7	
Sinha A, 2021 [21]	US	Northwestern Medicine Enterprise Data Warehouse	ICD-9 o ICD-10	371	10289	43.7/50.6	86.3	8	
Chevet B, 2022 [22]	US	Lupus Midwest Network study	ICD-10	440	430	54.0/54.1	81.6	8	
ICD: International Classification of Diseases; NA: not applicable; NOS: Newcastle-Ottawa Scale; NR: not reported; SLE: systemic lupus erythematosus; US: United States; UK: United Kingdom.

Table 2. Characteristics of cohort studies assessing the overall diabetes mellitus risk

Author, year	Country	Participants source	Diabetes criteria	Control group, n	SLE/Control, n	Mean age, years
SLE/controls	Female %	Follow-up, months	NOS	
Hemminki K, 2015 [34]	Sweden	Swedish national registy	ICD-10	General population	12206/NA	53.0/NR	81.6	>72	7	
Kuo CF, 2019 [35]	UK	Clinical Practice

Research Data-link

	ICD-9,	General population	1605/6248	50.8/50.8	81.7	120	7	
Lin YJ, 2022 [42]	Taiwan	Taiwan National Health Insurance Research Database	ICD-9	General population	6159/18477	38.8/38.8	87.9	36	6	
ICD: International Classification of Diseases; NOS: Newcastle-Ottawa Scale; NR: not reported; UK: United Kingdom.

Table 3. Characteristics of cohort studies assessing the diabetes mellitus risk by sociodemographic, clinical and treatment characteristics

Authors, year	Country	Participants source	Diabetes criteria	Subgroup	No. patients	Age	Female	Follow-up, months	NOS	
Petri M, 2013 [27]	US	Hopkins Lupus Cohort	Medical records	Age

HCQ

GCT

	1944	37.5	93.0	79.2	7	
Shah M, 2013 [28]	US	IMS LifeLink Health Plans Claims Database	ICD-9	Sex

GCT

	2717	47.9/49.7a	88.8/85.4a	24	6	
Chen YM, 2015 [43]	Taiwan	National Health Insurance

Research Database

	ICD-9	Age

Hypertension

GCT

HCQ

Immunosuppressive therapy

	8628	37.0	88.2	67.2	7	
Davidson J, 2018 [29]	US	Hopkins Lupus Cohort	Medical records	Sex

Hypertension

Immunosuppressive therapy

	128	37.3	73.7	60	7	
Wu J, 2020 [36]	UK	UK Clinical Practice Research Datalink (CPRD)	ICD-10	GCT	4410	47.9	84.1	63.6	7	
Kallas R, 2020 [30]	US	Hopkins Lupus Cohort	Medical records, laboratory test	Black ethnicity	2629	32.3	92.1	NR	7	
Haugaard JH, 2021 [37]	Denmark	Danish nationwide registers	ICD-8 and ICD-10	HCQ	4587	47.7	84.3	NR	7	
Ye Q, 2021 [44]	China	Children’s Hospital of Fudan University	Medical records, laboratory test	MPA levels	107	14.1	76.6	36	6	
Salmasi S, 2021 [19]	Canada	Population Data British Columbia	ICD-9, ICD-10	Sex

Hypertension

Antimalarials

Immunosuppressive therapy

	1498	44.4	90.8	55.5	7	
Levinson D, 2022 [20]	US	TriNetX database	ICD-10	HCQ	38050	47.0/52.0a	90.9/87.3a	120	7	
Stirnadel-Farrant HA, 2023 [38]	UK	Three linked data sourcesb	ICD-9	GCT	715	NR	88.5	120	7	
a Users and non-users.

b The Clinical Practice Research Datalink (CPRD) GOLD, Hospital Episode Statistics (HES)-linked health care administrative databases and Office for National Statistics (ONS) mortality files.

GCT: glucocorticoid; ICD: International Classification of Diseases; HCQ: hydroxychloroquine; MPA: mycophenolic acid; NA: not applicable; NOS: Newcastle-Ottawa Scale; NR: not reported; US: United States; UK: United Kingdom.

For cross-sectional studies, 181 154 SLE patients and 674 001 controls were included. For the cohort studies, 85 383 participants with SLE and 24 725 controls were evaluated. The population included mostly adult participants, with a mean age of 41.5 years. Patients from the cohort studies were followed up for 24–120 months. Most studies included female participants (65.0–100%). Except for three studies, most of the studies reported the method of case ascertainment, mostly based either on chart review [13, 33, 40, 44, 47], self-reported and/or hypoglycaemic medication use [3, 11, 12, 14, 15, 23, 24, 27–29, 31, 41, 45, 46, 48, 49] or International Classification of Disease (ICD) codes [19–22, 25, 26, 28, 32, 34–37, 39, 42, 43].

Analysis of cross-sectional studies

The meta-analysis of 23 cross-sectional studies did not show a statistical association between SLE and DM prevalence (OR = 1.05, 95% CI 0.87–1.27; P = 0.63) (Supplementary Fig. S1, available at Rheumatology online). We identified a significative heterogeneity among these studies (I2 = 65%, P < 0.001).

Analysis of cohort studies

Only three large cohort studies [34, 35, 42], derived from nationwide registries using the ICD code, were included in the pooled analysis. This analysis also failed to show an independently increased risk of DM in SLE patients (RR = 1.32, 95% CI 0.93–1.87; P = 0.12) (Supplementary Fig. S2, available at Rheumatology online). The heterogeneity among cohort studies was significant (I2 = 95%, P < 0.001). Most adjustments were performed according to age, sex and morbidities.

There were cohort studies that investigated the role of sociodemographic, clinical and treatment factors on the risk of DM. The number of events (incident diabetes) in those studies is shown in Supplementary Table S3, available at Rheumatology online (Supplementary material, available at Rheumatology online). The relationship between antimalarial use and the risk of diabetes has been explored in five studies, of which three found a reduced risk of diabetes [20, 37, 43]. However, one study found that HCQ was not associated with DM risk [27]. Interestingly, one cohort identified a protective effect of adherence to antimalarials in preventing DM among patients with SLE [19]. In addition, another cohort study revealed contrasting protective effects of HCQ on DM based on the daily glucocorticoid dose [43]. Patients with lower cumulative HCQ dose and higher daily glucocorticoid dose (10 mg or more) exhibited an increased risk of DM compared with those taking lower daily glucocorticoid dose. In contrast, the increased risk of DM in patients under higher glucocorticoid dose was reduced in individuals on higher cumulative HCQ dose. Overall, ‘ever use’ of glucocorticoids is associated with an increased risk of developing diabetes, according to two cohort studies [28, 36], while another cohort did not find a significant association [19]. Studies analysing higher prednisone dose (at least 7.5 mg/day) showed a higher risk of diabetes compared with low prednisone dose [28, 36, 38, 43]. Two studies evaluated the role of cumulative glucocorticoid dose [29, 36] and just one identified a significant association with the risk of DM [36].

In addition, older age was also associated with an increased risk of DM, according to two cohort studies [27, 43]. Other variables, such as sex, hypertension and immunosuppressants, were not associated with DM in SLE patients. However, one cohort study found that increased levels of mycophenolic acid (MPA) exposure were associated with a decreased incidence of DM in children [44]. One cohort study observed that African American smokers were at higher risk of diabetes compared with Caucasian smokers (hazard ratio [HR] = 3.15, 95% CI 1.75–5.54; P = 0.0001) [30].

Risk of bias of included studies and sensitivity analyses

In assessing the study quality based on the NOS, only three studies were considered to have a high risk of bias [11, 33, 40] and the remaining studies had a low risk of bias (Supplementary Table S4, available at Rheumatology online). For cross-sectional and cohort studies, the domains with a lower proportion of compliance were nonresponse rate and adequacy of follow-up, respectively.

Study findings were assessed with the modified GRADE checklist and are shown in the Summary of Findings Supplementary Table S5, available at Rheumatology online.

In the sensitivity analysis, no study significantly modified the effect estimator in either cross-sectional or cohort studies (Supplementary Table S6, available at Rheumatology online).

Publication bias

Funnel plots showed asymmetrical distribution and Egger’s tests confirmed publication bias in cross-sectional studies (P < 0.0001) (Supplementary Fig. S3, available at Rheumatology online).

Discussion

Based on cross-sectional and cohort studies involving a large sample size of SLE, mainly from North America, we found that SLE might not be associated with an increased risk of DM. However, in some SLE patients, the risk of incident DM is significant, particularly in those glucocorticoid users, while using antimalarials was associated with a reduced risk of DM. Antimalarial use was associated with a 41% reduction in diabetes risk, while glucocorticoid use and glucocorticoid high dose were associated with a 45% and 133% increase in diabetes risk. All the previous findings were from low to moderate-quality evidence according to the GRADE approach. To the best of our knowledge, this is the first systematic review and meta-analysis of published studies evaluating the association between DM and SLE.

Diabetes mellitus is an important cardiovascular risk factor and it is on the rise. It has been reported that over 240 million people globally have diabetes and that >400 million people will be affected by diabetes by 2030. DM increases the risk of cardiovascular morbidity and mortality [50]. Overall, we did not observe a significantly increased risk of DM in patients in cross-sectional or cohort observational studies. The potential reasons for this finding are unclear; however, most evaluations were performed in cross-sectional studies. Interestingly, two studies of three cohort studies (all large population-based cohorts) individually reported an association between SLE and the risk of DM and only one cohort with a longer follow-up showed no higher risk of DM [35]. Those studies’ administrative data are imperfect, lacking several confounders, missing data and potential misclassification bias for SLE classification and DM diagnosis.

Medications used for SLE management, including glucocorticoids and calcineurin inhibitors, have been associated with an increased risk of diabetes. Glucocorticoids are commonly administered to SLE patients. Glucocorticoids cause hyperglycaemia through several mechanisms, such as inducing or worsening preexisting insulin resistance, increasing hepatic gluconeogenesis, causing islet cell dysfunction and stimulating appetite and weight gain in the long term [51].

Although an increased diabetes risk with glucocorticoids is well recognized, the effect sizes vary greatly. Our meta-analysis of SLE patients from moderate-quality evidence revealed that glucocorticoids are associated with an increased risk of diabetes. Despite its potent anti-inflammatory effects, it could lead to hyperglycaemia and insulin resistance, increase the expression of adipogenic and lipogenic key genes, enhance gluconeogenesis in the liver and antagonize insulin-mediated glucose disposal in a dose-dependent manner [52]. The clinical implications of systemic glucocorticoids have been explored in previous studies, in which glucocorticoid users were approximately twice as likely to develop acute and chronic adverse clinical outcomes than non-users [28, 38]. The current European League Against Rheumatism (EULAR) guidelines recommend minimizing the daily dose or, where possible, discontinuing glucocorticoids owing to associated risks [53]. In addition, glucocorticoid exposure is also related to a high economic burden on patients and the healthcare system [38]. Therefore, effective glucocorticoid-sparing strategies may reduce the burden of glucocorticoid-related complications, including adverse metabolic outcomes.

In contrast, antimalarial use, mainly HCQ, has been suggested to have a beneficial effect on metabolic conditions, including diabetes risk. A previous meta-analysis of observational studies in RA patients demonstrated that the incidence of diabetes was lower in HCQ-ever users than in non-users [54]. According to moderate quality evidence, this effect was also identified in our systematic review of SLE patients. The precise mechanism implied that diabetes risk reduction with antimalarials had not been established. Still, it is presently assumed to be associated with improvements in pancreatic beta cell function, insulin sensitivity and glucose metabolism in addition to its immunomodulatory benefits [55]. HCQ can supportively improve glucose metabolism and lipid profile by blocking toll-like receptors 7, 9 and inhibiting the production of interferon-α [56, 57]. The benefit of HCQ on lipid profiles has been observed in previous meta-analyses of patients with RA and SLE [54].

Although some studies have reported an increased risk of DM with immunosuppressive treatment [9], our findings did not identify this association.

However, one study observed that increased MPA exposure was associated with a decreased incidence of diabetes in children with SLE [44]. The administration of mycophenolate mofetil and adequate exposure to this agent may help reduce the glucocorticoid dose, leading to a reduction in the incidence of diabetes.

Our study has several limitations. First, some evaluations showed high heterogeneity, increasing our uncertainty regarding the results. Second, this systematic review was based on observational studies and confounding or other biases can exist. Third, diabetes in the included cohort studies was primarily identified by ICD. Therefore, we cannot rule out the possibility that some cases of diabetes were missed. Fourth, only a few longitudinal studies evaluated overall diabetes mellitus risk in patients with SLE, whereas there were more cohort studies considering this risk by different characteristics such as sociodemographic, clinical and treatment characteristics. Fifth, we identified publication bias from cross-sectional studies. Although the reasons for this finding are unclear, observational studies usually are at higher risk of publication. Furthermore, some articles could have been missed as our search strategy was performed in only certain electronic databases. Finally, some studies included information obtained from community-based surveys, while others included only ‘known diabetes’; therefore, an underestimation of diabetes prevalence could not be ruled out. In the last few years, the diagnostic criteria for diabetes have changed, which could have influenced the prevalence rates. Future longitudinal studies are needed to assess the risk of DM in patients with SLE, considering the role of immunosuppressive drugs, time-varying exposures and adherence to antimalarials.

In summary, although SLE may not increase the risk of diabetes overall, an increased risk of diabetes was identified in glucocorticoid users, particularly in those using high glucocorticoid doses (7.5 mg/day or more). A reduced risk of diabetes has been observed in patients treated with antimalarial agents. These important findings may aid clinical decision-making in the management of SLE. Large, prospective, well-powered studies are needed to evaluate the impact of these drugs on diabetes development in SLE patients.

Supplementary material

Supplementary material is available at Rheumatology online.

Supplementary Material

keae204_Supplementary_Data

Acknowledgements

No ethical approval was required for this review as all data were already published in peer-reviewed journals. No patients were involved in the design, conduct or interpretation of our study.

Data availability

The datasets used in this study can be found in the full-text articles included in the systematic review and meta-analysis. The data underlying this article will be shared upon reasonable request to the corresponding author.

Contribution statement

I.E.M. and C.M.P. were responsible for conceptualization, project administration, data curation, visualization and writing the original draft. P.M.R. was responsible for conceptualization, project administration, data curation and visualization. J.C.S.P. was responsible for conceptualization, data curation and visualization. S.M.M was responsible for methodology, formal analysis, validation and visualization. J.A.A. was responsible for the conceptualization, supervision and writing the original draft. C.A.M., M.G.C. and R.C. were responsible for conceptualization, supervision and writing and editing. All authors provided critical conceptual input and approved the final version of the article.

Funding

No specific funding was received from any funding bodies in the public, commercial or not-for-profit sectors to carry out the work described in this manuscript.

Disclosure statement: The author has declared no conflicts of interest.
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