The vast majority of epidemiological studies suggested a link between systemic lupus erythematosus (SLE) and major depressive disorder (MDD). However, the causality for SLE on the risk of MDD remained unknown due to confounding factors or reverse causality. Herein, we investigated the causality between SLE and MDD in those of European ancestry by a Mendelian randomization (MR) approach. Summary genetic data of cases with SLE/MDD were derived from independent largest public genome-wide association study. Forty-six single nucleotide polymorphisms associated with SLE were used as instrumental variables. The main causal inference was carried out using the MRE-IVW method. Additional, reverse-direction MR and multivariable MR analyses were further performed. Result indicated that SLE was causally associated with a lower risk of MDD (using the MRE-IVW method, odds ratio [OR] = 0.983, 95% confidence interval [CI] = 0.974–0.991, p = 1.18 × 10−4). Complementary analysis found no heterogeneity or horizontal pleiotropy. Multivariate MR analysis yielded consistent results (OR = 0.981; 95% CI = 0.969–0.993; p = 2.75 × 10−3). Reverse-direction MR analysis suggested non-causal relationship of MDD on the risk of SLE (using the IVW method, OR = 0.846, 95% CI = 0.345–2.072; p = 0.714). Thus, this is the first study providing evidence of potential causal links between SLE and MDD and further related research is needed.
Major depressive disorder (MDD) is one of the most severe and common disorders in psychiatry globally and has long been a major societal concern . MDD affects more than 300 million people of all ages worldwide, and is currently a major contributor to the global disease burden in the general population [2, 3]. However, the pathogenesis of MDD is unclear. Twin studies have shown that 30–40% of the variation in MDD can be attributed to genetic factors . Notably, MDD has long been regarded as a “comorbidity” of several chronic diseases, such as angina, systemic lupus erythematosus (SLE), arthritis and diabetes, which worsens the quality of health substantially compared with when these diseases occur alone .
Psychological disorders in SLE have been investigated in recent decades. The reported prevalence varied widely across several published SLE cross-sectional studies, from 2.1% to 78.6% depending on factors such as study design and diagnostic criteria [6,7,8,9]. In the vast majority of epidemiological reports, the prevalence of depression in SLE patients was approximately twice that in the general healthy population in clinical and community samples . SLE accompanied by depression is associated with markedly worse prognosis in physical, mental, and social domains. Given this very close relationship between SLE and MDD, diagnosing and treating MDD may help improve health-related quality of life in individuals with SLE . However, research and evaluation from observational studies are insufficient to draw conclusions on the cause–effect relationships due to spurious correlations caused by confounders and reverse causality.
Well-designed randomized controlled trials (RCTs)—the gold standard to imply causality—can tackle the potential confounders effectively. However, RCTs take considerable time and might be impractical to initiate due to ethical concerns and financial limitations. As an important complementary causal research approach, Mendelian randomization (MR) uses genetic variants that associate with the exposure as instrumental variables to robustly assess the causality between exposure and outcome, given that certain assumptions including the absence of pleiotropy are met . Against this background, the purpose of this study is to investigate lifetime prevalence rates of MDD in patients with SLE, which extends previous work by simultaneously assessing the largest GWAS data of MDD in a large sample of SLE patients, using a reliable and validated structured MR approach.
GWAS data sources
This two-sample MR study using publicly available summary statistics of GWAS data on SLE  and MDD . SLE-related instrumental variables were derived from independent genome-wide relationship studies (GWAS), including 7219 cases and 15,991 controls with European ancestry. Genetic relationships with MDD were obtained from the GWAS data among individuals of European ancestry from the Psychiatric Genomics Consortium database, which comprises 135,458 major depression cases and 344,901 controls. Among them, 59,851 patients with MDD and 113,154 controls were included in the present MR analysis, because genome-wide summary statistics of 23andMe data were not public available (75,607 cases and 231,747 controls). Further details concerning the above studies have been published previously [13, 14].
Genetic instrument selection process
SNPs are considered to meet the following three key assumptions . (1) Genetic variants should be strongly associated with the exposure. The selection of instrumental variables should satisfy the association between SNPs and the corresponding phenotype (systemic lupus erythematosus) (p < 5 × 10−8). (2) Genetic variants extracted for exposure should be independent of any confounder. (3) The genetic variants only affect the outcome only through the exposure. In order to meet the following assumptions, SNPs are then filtered through the following steps. Candidate genetic instrumental variables (IVs) that surpassed a conventional genome-wide significance threshold (p < 5 × 10−8) were obtained from a recent GWAS of SLE comprising data on participants with European ancestry. Proxy SNPs were identified at a cut-off of R2 > 0.8 to replace missing SNPs in the outcome GWAS dataset. If no suitable proxy was available, SNPs were discarded. Linkage disequilibrium (LD) clumping with a clumping window of 10 MB was applied to ensure that these SNPs were individually, and cumulatively, considered as valid instruments for MR analysis (LD R2 > 0.01) . F-statistic was used to confirm the strength of IVs, with weak IVs (F-statistic <10) being discarded. In the harmonization process, ambiguous and palindromic SNPs (minor allele frequency >0.42) were excluded. Outlier SNPs with potential pleiotropy was detected by the MR-pleiotropy residual sum and outlier (MR-PRESSO) test and then discard.
Two-sample Mendelian randomization
To perform a robust and reliable inference of the causal relationship between SLE and MDD, in the main analysis, we performed multiplicative random-effect inverse variance weighted (MRE-IVW) analysis . MR-Egger regression and weighted median constitute statistical tests for the presence of pleiotropic effects of SNPs under analysis and provide a complementary causal estimate [18, 19]. The Cochran Q test for the IVW method was implemented to detect heterogeneity . In detail, no heterogeneity was detected if the p value of the Cochran Q was >0.05 and I2 was <25%. The leave-one-out test was then performed to assess whether the IVW estimate was biased by the influence of particular single SNPs. Additionally, reverse-direction MR analysis was conducted to examine whether there existed reverse-direction causal relationship. Statistical analyses were performed using R software version 4.0.2 (https://www.r-project.org/) with the two-sample MR package (version 0.5.5).
In addition, each SNP was looked up in the genetic instrument in Phenoscanner (http://www.phenoscanner.medschl.cam.ac.uk/) to determine whether the estimate was violated by potential risk factors verified by other MR studies, including periodontitis , plasma cathepsin B level , gut microbiome , selenium , circulating GDF-15 level , and high serum iron status .
An online publicly available power calculator (mRnd, http://cnsgenomics.com/shiny/mRnd/) was utilized to evaluate the power of our study . For binary outcomes (MDD), after we inputted the required parameters in mRnd (α = 0.05, R2 = 0.983 in this study), the power of our study was roughly estimated.
Multivariable Mendelian randomization
Taking critical impact of several confounding factors linking SLE to the MDD onset into account, a multivariable MR analysis was applied to estimate the effect of multiple exposure variables on an outcome (MDD in this study). For MVMR analyses, we constructed instruments using SNPs in each of the GWASs meeting our single-variable MR selection criteria, described previously. We combined the SNPs from the relevant GWASs (Body mass index , smoking , drinking  and physical activity ) and removed those SNPs which was missed in one or more datasets, then extracted the SNP effects and corresponding standard errors from the exposures and outcome GWASs. Notably, SNPs with robust information related to both causal SLE and four several confounders (see Supplement table S4) were utilized as IVs for multivariable MR analysis. Inverse-variance weighted method was further used to estimate the causal effect.
Two-sample Mendelian randomization analysis for causal link of SLE with MDD
After the clumping process, 52 LD-independent SNPs for exposure (SLE) remained for further analysis. Among them, 4 outlying SNP (rs1270942, rs13136219, rs501480, rs7768653) in the causality inference was detected based on MR-PRESSO analysis and excluded. Two palindromic SNPs (rs115531193, rs2736332) were detected and removed in the harmonization process. 46 SNP selected as instrumental variables were listed in Supplementary Table S1. As shown in Fig. 1, the overall causal relationship between SLE and MDD (IVW method, OR = 0.983; 95% CI, 0.974 to 0.991; p = 1.18 × 10−4) was significant. In addition, results from the “leave-one-out” analysis (Fig. 2A) demonstrated that no single SNP was driving the IVW point estimate. These results indicated that SLE was negatively associated with the risk of MDD. Figure 2B showed the forest plot of pooled MR estimates and individual estimates between SLE-associated IV and the risk for MDD. Finally, conducting reverse MR analysis with available SNPs listed in Supplementary Table S2, we gave the evidence that there is not causal effect of MDD on the risk of SLE (IVW method, OR = 0.846; 95% CI, 0.345 to 2.072; p = 0.714). However, we had limited power (27%) to test significant causal effect of SLE on the risk of MDD, possibly due to small sample size of the MDD GWAS and the ORs for the relationship was relatively limited.
Assessment of sensitivity analysis scores based on IVW analysis were consistent with weighted median and MR-Egger results. Figure 3 shows the scatter plot of the causal effect given by each MR estimator. The MR-Egger regression revealed that directional pleiotropy was unlikely to bias the result (Egger_intercept = −0.004, p = 0.283). Cochran Q test and the funnel test (Fig. 4) indicated no heterogeneity between SLE and MDD (Q value = 43.306, p = 0.544). And the result of the weighted median further supported the positive relationship, which confirmed that the results were not biased by heterogeneity. Moreover, our results of I2 value showed the absence of heterogeneity (I2 = 0%), indicating increased reliability of MR estimates. The Phenoscanner results of each SNP with the genetic traits are shown in Supplementary Table S3. No potential risk factors were detected to violate the robustness of our MR causality estimate.
Multivariable Mendelian randomization
Using a threshold of p < 5 × 10–8, those IVs after quality control were utilized to estimate the causal effect in MVMR were listed in Supplement table S4. There was strong evidence that SLE was causally associated with a lower risk of MDD, and with MVMR after conditioning with other four traits, the causal relationships was still robust (IVW method, OR = 0.981; 95% CI, 0.969 to 0.993; p = 2.75 × 10−3). Smoking and BMI was also causally associated with the risk of MDD (IVW method, BMI: OR = 1.085; 95% CI, 1.016 to 1.159; p = 0.016; Smoking: OR = 1.468; 95% CI, 1.236 to 1.744; p = 1.23 × 10−5). However, drinking and physical activity were detected insignificant causal effect on the risk of MDD (p value for drinking is 0.514 and for physical activity is 0.056). In a conclusion, known from the result of MVMR, the causal relationship between SLE and MDD was robust and it wouldn’t be biaed by these confounding factors.
This study obtained partly genetic evidence in support of the potential causal links between SLE and the lower risk of MDD by applying a validated structured MR approach. This relationship was significant in the main MR analyses and consistent across follow-up sensitivity analyses. These findings demonstrated that SLE patients tended to have a lower prevalence of MDD in genetics, which might be contrary to previous observational studies.
Observational studies have reported inconsistent findings on the relationship between SLE and MDD. That MDD was a risk factor on SLE disease activity have been reported in some cohorts, but in other cohorts, MDD prevalence was independent to SLE disease activity [31,32,33,34,35]. Study of Roberts et al. suggested that MDD increases the risk of SLE . However, another study demonstrated that improving patients’ mood did not significantly ameliorate the disease activity of SLE . Previous studies reported that serum anti-ribosomal P (anti-RP) titers were significantly more likely to be positive in SLE with MDD than without, implying that anti-RP plays a role in SLE-mediated depression . In addition, the regulatory relationship of SLE on depression may also be related to neuroinflammation and brain serotonin levels [39, 40]. Huang et al. analyzed data from a cohort of 1609 SLE patients who had no history of MDD prior and made a multivariate analysis, suggested that glucocorticoid use and skin manifestations were predictors of depression, but global disease activity of SLE was not. Interestingly, the authors found that the incidence of depression decreased as the time to SLE diagnosis increased, which may be due to better control of disease activity, less prednisone used and coping ability increased over time . Stojan et al. reported that 59% of SLE patients experienced a significant decrease in BMI within 5 years . Our study demonstrated that smoking and BMI are clear predictors of MDD and the relationship between lower BMI and lower risk of MDD was verified in our multivariable MR. Undoubtedly, it is necessary to explore the potential causal relationship of MDD to SLE at the gene level.
Some limitations could potentially bias the results of observational studies. Firstly, these observational studies cannot be used as direct evidence of the causal relationship between SLE and depression because of its design. Secondly, most studies used questionnaire reports to define depression, which may deviate from the strict definition of “major depression disorder”. Thirdly, most original reports lacked the assessment of attribution to MDD and fail to exclude confounding factors (such as drugs, smoking, BMI, etc.). Mental and physical health are tightly connected. When depressive symptom coexists with the development of SLE, health-related quality of life, disability, and costs tend to be much worse [11, 43, 44]. The relationship between MDD and SLE may be related to social income and compliance [45,46,47].
MR studies use genetic variation as a statistical tool and has been widely used for evaluating causal inference between disease risk factors and exposure outcomes. Our results showed that SLE was associated with the lower risk of MDD and MDD had no significant causal relationship with SLE. To date, this is the first MR study to explore the causal relationship between SLE and MDD. In this two-sample MR study, the potential causal relationship between genetically predicted SLE and MDD was investigated thoroughly. Instrumental variables were chosen from corresponding largest summary statistics of GWAS datasets after a set of rigorous process. Moreover, the absence of pleiotropic and heterogeneity minimized the effects of confounded estimates caused by single SNPs that could affect the outcome on different pathways. In addition, ancestry was controlled by selecting European samples in this MR study may help to minimize bias of the unmatched genetic variants frequencies among different ancestry. This MR analysis showed that SLE may have a mild protective causal relationship with MDD. This contrasts sharply with previous observational studies, thus, the mechanism of the potential protective effect of SLE on MDD needs further exploration.
Several limitations also exit in this study. First, only Europeans ancestry were included, and additional studies should be conducted to confirm whether our findings are generalizable in ethnically. Second, although we have performed multivariate MR analysis for possible potential confounders such as BMI, smoking, drinking, physical activity, we did not obtain gender or drugs information because of using summary data, so the impact of sex hormone or drugs differences on the results cannot be excluded. Although our results are contrary to previous observational studies, it shows that the relationship between SLE and MDD is still very complex, which needs further rigorous disease diagnosis and more detailed classification research.
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
McCarron RM, Shapiro B, Rawles J, Luo J. Depression. Ann Intern Med. 2021;174:Itc65–itc80.
Depression and other common mental disorders: Global health estimates. http://www.who.int/mental_health/management/depression/prevalence_global_health_estimates/en/. 2017
Dadi AF, Miller ER, Bisetegn TA, Mwanri L. Global burden of antenatal depression and its association with adverse birth outcomes: an umbrella review. BMC Public Health. 2020;20:173.
Polderman TJ, Benyamin B, de Leeuw CA, Sullivan PF, van Bochoven A, Visscher PM, et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47:702–9.
Moussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B. Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet 2007;370:851–8.
Seawell AH, Danoff-Burg S. Body image and sexuality in women with and without systemic Lupus Erythematosus. Sex Roles. 2005;53:865–76.
Hanly JG, Su L, Urowitz MB, Romero-Diaz J, Gordon C, Bae SC, et al. Mood disorders in systemic lupus erythematosus: results from an international inception cohort study. Arthritis Rheumatol. 2015;67:1837–47.
Wekking EM. Psychiatric symptoms in systemic lupus erythematosus: an update. Psychosom Med. 1993;55:219–28.
Zhang L, Fu T, Yin R, Zhang Q, Shen B. Prevalence of depression and anxiety in systemic lupus erythematosus: a systematic review and meta-analysis. BMC Psychiatry. 2017;17:70.
Bachen EA, Chesney MA, Criswell LA. Prevalence of mood and anxiety disorders in women with systemic lupus erythematosus. Arthritis Rheum. 2009;61:822–9.
Dietz B, Katz P, Dall’Era M, Murphy LB, Lanata C, Trupin L, et al. Major depression and adverse patient-reported outcomes in systemic lupus erythematosus: results from a prospective longitudinal cohort. Arthritis care Res. 2021;73:48–54.
Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65.
Bentham J, Morris DL, Graham DSC, Pinder CL, Tombleson P, Behrens TW, et al. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat Genet. 2015;47:1457–64.
Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.
Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ (Clin Res ed) 2018;362:k601.
Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat methods Med Res. 2017;26:2333–55.
Burgess S, Dudbridge F, Thompson SG. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med. 2016;35:1880–906.
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.
Bowden J, Davey, Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–14.
Greco MF, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34:2926–40.
Bae SC, Lee YH. Causal association between periodontitis and risk of rheumatoid arthritis and systemic lupus erythematosus: a Mendelian randomization. Z Rheumatol. 2020;79:929–36.
Mo X, Guo Y, Qian Q, Fu M, Lei S, Zhang Y, et al. Mendelian randomization analysis revealed potential causal factors for systemic lupus erythematosus. Immunology 2020;159:279–88.
Xiang K, Wang P, Xu Z, Hu YQ, He YS, Chen Y, et al. Causal effects of gut microbiome on systemic Lupus Erythematosus: A two-sample mendelian randomization study. Front Immunol. 2021;12:667097.
Ye D, Sun X, Guo Y, Shao K, Qian Y, Huang H, et al. Genetically determined selenium concentrations and risk for autoimmune diseases. Nutrition 2021;91-92:111391.
Ye D, Liu B, He Z, Huang L, Qian Y, Shao K, et al. Assessing the associations of growth differentiation Factor 15 with rheumatic diseases using genetic data. Clin Epidemiol. 2021;13:245–52.
Ye D, Zhu Z, Huang H, Sun X, Liu B, Xu X, et al. Genetically Predicted serum iron status is associated with altered risk of systemic lupus erythematosus among European populations. J Nutr. 2021;151:1473–8.
Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8.
Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet. 2018;27:3641–9.
Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, et al. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019;51:237–44.
Klimentidis YC, Raichlen DA, Bea J, Garcia DO, Wineinger NE, Mandarino LJ, et al. Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE. Int J Obes (2005) 2018;42:1161–76.
Nery FG, Borba EF, Viana VS, Hatch JP, Soares JC, Bonfá E, et al. Prevalence of depressive and anxiety disorders in systemic lupus erythematosus and their association with anti-ribosomal P antibodies. Prog Neuro-psychopharmacol Biol Psychiatry. 2008;32:695–700.
Knight AM, Trupin L, Katz P, Yelin E, Lawson EF. Depression risk in young adults with juvenile- and adult-onset lupus: twelve years of followup. Arthritis Care Res. 2018;70:475–80.
Jarpa E, Babul M, Calderón J, González M, Martínez ME, Bravo-Zehnder M, et al. Common mental disorders and psychological distress in systemic lupus erythematosus are not associated with disease activity. Lupus 2011;20:58–66.
Nery FG, Borba EF, Hatch JP, Soares JC, Bonfá E, Neto FL. Major depressive disorder and disease activity in systemic lupus erythematosus. Compr Psychiatry. 2007;48:14–9.
Parperis K, Psarelis S, Chatzittofis A, Michaelides M, Nikiforou D, Antoniade E, et al. Association of clinical characteristics, disease activity and health-related quality of life in SLE patients with major depressive disorder. Rheumatology. 2021;60:5369–78.
Roberts AL, Kubzansky LD, Malspeis S, Feldman CH, Costenbader KH. Association of depression with risk of incident systemic lupus erythematosus in women assessed across 2 decades. JAMA Psychiatry. 2018;75:1225–33.
Navarrete-Navarrete N, Peralta-Ramírez MI, Sabio-Sánchez JM, Coín MA, Robles-Ortega H, Hidalgo-Tenorio C, et al. Efficacy of cognitive behavioural therapy for the treatment of chronic stress in patients with lupus erythematosus: a randomized controlled trial. Psychother Psychosom. 2010;79:107–15.
Karimifar M, Sharifi I, Shafiey K. Anti-ribosomal P antibodies related to depression in early clinical course of systemic lupus erythematosus. J Res Med Sci: Off J Isfahan Univ Med Sci. 2013;18:860–4.
Cho T, Sato H, Wakamatsu A, Ohashi R, Ajioka Y, Uchiumi T, et al. Mood disorder in systemic Lupus Erythematosus induced by Antiribosomal P protein antibodies associated with decreased serum and brain Tryptophan. J Immunol 2021;206:1729–39.
Kong X, Zhang Z, Fu T, Ji J, Yang J, Gu Z. TNF-α regulates microglial activation via the NF-κB signaling pathway in systemic lupus erythematosus with depression. Int J Biol Macromol. 2019;125:892–900.
Huang X, Magder LS, Petri M. Predictors of incident depression in systemic lupus erythematosus. J Rheumatol. 2014;41:1823–33.
Stojan G, Li J, Wittmaack A, Petri M. Cachexia in systemic lupus erythematosus: risk factors and relation to disease activity and damage. Arthritis Care Res. 2021;73:1577–82.
Scott KM, Lim C, Al-Hamzawi A, Alonso J, Bruffaerts R, Caldas-de-Almeida JM, et al. Association of mental disorders with subsequent chronic physical conditions: world mental health surveys from 17 countries. JAMA Psychiatry. 2016;73:150–8.
Vamos EP, Mucsi I, Keszei A, Kopp MS, Novak M. Comorbid depression is associated with increased healthcare utilization and lost productivity in persons with diabetes: a large nationally representative Hungarian population survey. Psychosom Med. 2009;71:501–7.
Andersen I, Thielen K, Nygaard E, Diderichsen F. Social inequality in the prevalence of depressive disorders. J Epidemiol Community Health. 2009;63:575–81.
McCormick N, Trupin L, Yelin EH, Katz PP. Socioeconomic predictors of incident depression in systemic Lupus Erythematosus. Arthritis Care Res. 2018;70:104–13.
Alsowaida N, Alrasheed M, Mayet A, Alsuwaida A, Omair MA. Medication adherence, depression and disease activity among patients with systemic lupus erythematosus. Lupus 2018;27:327–32.
We thank the authors for providing GWAS data and making the GWAS summary data publicly available.
This study was supported by Science and Technology Planning Project of Guangdong Province (2017B020227005) and Science and Technology Planning Project of Guangdong Province (2019A141401002).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Li, W., Kan, H., Zhang, W. et al. Mendelian randomization study on the causal effects of systemic lupus erythematosus on major depressive disorder. J Hum Genet 68, 11–16 (2023). https://doi.org/10.1038/s10038-022-01080-7