Analysis of the Kidney–Brain Axis via the Fractional Amplitude of Low-Frequency Fluctuation in Patients with Diabetic Nephropathy

Analysis of the Kidney–Brain Axis via the Fractional Amplitude of Low-Frequency Fluctuation in Patients with Diabetic Nephropathy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of the Kidney–Brain Axis via the Fractional Amplitude of Low-Frequency Fluctuation in Patients with Diabetic Nephropathy jinlei lv This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5816556/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Central neuropathies caused by diabetic nephropathy (DN) share similar characteristics. The present study aimed to analyze the changes in brain function of patients with DN based on the kidney–brain axis. Methods The study population consisted of patients with DN and healthy controls (n = 23 per group). Brain resting-state functional magnetic resonance imaging examination was performed on all participants, and the fractional amplitude of low-frequency fluctuation (fALFF) values were calculated. The diagnostic authenticity was assessed through receiver operating characteristic curves using sensitivity, specificity, and Youden index. Statistical analysis included Pearson's correlation between mean fALFF values and DN data. Results The imaging analysis revealed that DN patients exhibited lower fALFF values in the right cingulum anterior segment (RCA) and left cingulum middle segment, and increased fALFF values in the right cingulum middle segment compared to control subjects. The correlation analysis demonstrated that mean fALFF values in the RCA correlated with the estimated glomerular filtration rate in DN patients. Conclusions The research findings demonstrated significant differences in fALFF values in the default mode network and visual cortex-related areas. These observations may be highly valuable for understanding the kidney–brain axis mechanisms of DN, as well as the associations between diabetic microvascular complications. diabetic nephropathy type 2 diabetes blood–brain barrier kidney–brain Axis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The incidence of diabetes mellitus (DM) is increasing. In 2020, the International Diabetes Federation reported that 465 million people were living with DM 1 , which is expected to reach 600 million globally by 2045 2 . With the increasing number of DM patients, diabetic microvascular complications are likely to become a significant public health issue worldwide 3 . Diabetic nephropathy (DN) is a dominant cause of end-stage renal disease (ESRD) globally, which is considered a primary cause of mortality in patients with DM 4 . Furthermore, there has been an increased awareness of diabetic central neuropathy. It has been reported that ESRD caused by DM can result in more severe neuropathy than that of other non-diabetic causes 5 . Our previous study showed that changes in brain function and cognitive decline are correlated with diabetic retinopathy (DR) and renal injury in DM patients 6 ; however, the neuropathological mechanisms linking DM and DN to cognitive impairment remain unclear. According to the Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines 7 , microalbuminuria and retinopathy are early predictors of DN in clinical practice. The retina is another organ that is affected by microvascular injury, and diabetic retinopathy (DR) is a major cause of blindness among young and aging people 8 . Studies have reported a positive relationship between DN and ESRD and cognitive decline, rather than “pure” type 2 DM patients 9 . Long-term hyperglycemia results in damage to the renal intrinsic cells due to inflammation and oxidative stress, which eventually leads to alterations in kidney and brain function 10 , 11 . Scholars proposed and proved the concept of a kidney–brain axis, through which kidney damage influences brain health(9). In the early stages of DN, these complications usually progress silently; however, as the disease progresses, physiological and structural changes in kidney and brain function occur 12 . Therefore, elucidating the mechanisms of DN and exploring the interplay between the brain and kidney could help to prevent cognitive impairment in DN populations. In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the clinic, especially in the detection and diagnosis of brain and eye diseases 13 . Among these measures, fractional amplitude of low-frequency fluctuation (fALFF) values allow the signal intensity during resting state to be studied, which reflects the level of spontaneous activity of each voxel based on energy expenditure, and provides local brain activity information with physiological significance 14 . A meta-analysis of rs-fMRI studies showed that compared to healthy controls, ESRD patients with cognitive impairment exhibit decreased resting-state activity. In certain brain areas, including the default mode, visual recognition, and executive control network, the fALFF was lower in patients with chronic kidney disease 15 . Studies have revealed a relationship between kidney damage and cognitive impairment 16 , 17 ; however, the pathophysiological mechanisms underlying the changes in brain activity and neural characteristics, as well as the correlation between altered fALFF values and the clinical features of DN, are not yet fully understood. We hypothesized that patients with DN present with alterations in brain activity based on the kidney–brain axis. To date, how DN influences brain function remains poorly understood, and the mechanism underlying the kidney–brain axis requires in-depth investigation. In the present study, we aimed to assess brain functional alterations of DN patients using the fALFF approach. We focused on the clinical aspects concerning the association between albuminuria, microvascular kidney disease, and brain functional changes in patients with DN. Methods Participants We recruited 23 patients with DN from Nephrology Department of our hospital, and 23 sex-matched HCs from the outpatient clinic between October 2019 and October 2020. The inclusion criteria for the DN group were as follows: (1) a minimum of 5 years duration of type 2 DM; (2) retinopathy (non-proliferative DR or PDRproliferative DR, Fig. 1 b); (3) a definite renal pathological diagnosis of DN (Fig. 1 a); and (4) an eGFR < 60 [ml/min]/1.73 m 2 . The inclusion criteria for the HC group were as follows: (1) normal brain MRI; (2) no history of neurological or psychiatric disorders; (3) absence of kidney diseases; and (4) no history of ocular diseases. We excluded patients or HCs with a history of neurological disorders; primary and congenital renal diseases; cardiovascular diseases; cataracts, glaucoma, or other eye diseases; or MRI contraindications. Patients with type 2 DM all used insulin or oral hypoglycemic drugs to control blood sugar levels, and their hemoglobin A1c (HbA1c) level was maintained within 7% to rule out the effects of hyperglycemia on brain function. We found no significant differences in sex, age, or other basic information between the two groups. All participants were aged between 18 and 75 years, were right-handed, and had more than 6 years of formal education. We collected the following data from all participants: name, sex, age, body weight, years of education, years of DM, systolic blood pressure, diastolic blood pressure, binocular vision values, serum creatinine level, relevant family history, and MRI contraindications. Additionally, the 24-h urinary protein value and history of hypoglycemia and insulin therapy data were collected from DN patients. Our research complied with the Code of Ethics of the World Medical Association (Declaration of Helsinki). All methods were performed in accordance with the relevant guidelines and regulations, and this study was approved in accordance with the ethical standards of First Affiliated Hospital of Nanchang University Ethical Committee and Institutional Review Board (Reference Number: 2021039). In addition, we informed all participants about the process and matters requiring attention for this study; all provided informed consent Fundus ophthalmoscopy An experienced ophthalmologist performed visual acuity and fundus examinations and determined that all patients with DM presented with associated retinopathy (Fig. 1 b). The eye condition of each patient was determined by that of their more severely affected retina. Calculation of eGFR Single-photon emission computed tomography was performed to calculate the eGFR of all participants. MRI parameters MRI data acquisition and analysis Statistical analysis Data were analyzed using SPSS 22.0 statistical software. Data are expressed as mean ± standard deviation (SD), and independent sample t-tests were used to analyze differences between the two groups. Correlations between variables were determined using Pearson’s correlation analysis. After preprocessing, the MRI data were analyzed using the REST software, and two independent whole-brain level samples were tested using the fALFF method. Following AlphaSim correction (full width half maximum, 8 mm; voxel connection radius, 5 mm), we considered all voxels with a p < 0.05 as statistically significant. We included the MRI data of all participants in the analysis and plotted the ROC curve. The AUC, sensitivity, specificity, and Youden index were calculated to assess the authenticity of the diagnosis. Results Demographic and clinical data Table 1 Demographic and clinical characteristics of patients with diabetic nephropathy (DN) and healthy controls (HCs). DN (n = 23) HCs (n = 23) t -value p value Male/female 11/12 11/12 - > 0.99 Age (years) 53.52 ± 8.28 53.57 ± 8.75 −0.017 0.99 Weight (kg) 62.78 ± 8.62 62.87 ± 7.64 −0.036 0.97 Scr (µmol/l) 277.61 ± 119.57 65.72 ± 7.50 8.482 < 0.05 ACR (mg/g) 2309.06 ± 1247.29 11.43 ± 10.82 8.834 < 0.05 PCR (g/g) 4.21 ± 2.04 0.09 ± 0.07 9.662 < 0.05 eGFR ([ml/min]/1.73㎡) 37.92 ± 10.73 105.15 ± 7.17 −24.982 < 0.01 24-hour urinary protein quantity (g) 3.81 ± 0.98 - - - Diabetes duration (years) 12.65 ± 5.20 - - - Notes: Table 1 presents the demographic information and relevant clinical characteristics of the participants. Data are presented as the number (%) or mean (SD), as appropriate. The two groups (DN patients and healthy controls (HCs)) were comparable regarding sex ( p > 0.99), age ( p = 0.99), and body weight ( p = 0.97). Scr serum creatinine, ACR urinary albumin/creatinine ratio, PCR urinary protein/creatinine, eGFR estimated glomerular filtration rate. Table 2 Brain regions alternation from our data and its potential impact Brain regions Experimental result Brain function Anticipated results Right cingulum anterior segment(RCA, BA11) DNs > HCs prefrontal associational integration, orbital and rectus gyri, rostral part of the superior frontal gyrus Visual function, Sense of smell, Social behavior Left cingulum middle segment(LCM, BA24) DNs < HCs emotional and cognitive processing, connects with the amygdala, orbitofrontal cortex, and hippocampus cognitive, emotional, memory, and other functions Right cingulum middle segment(RCM, BA24) DNs < HCs emotional and cognitive processing, connects with the amygdala, orbitofrontal cortex, and hippocampus cognitive, emotional, memory, and other functions HCs ,healthy controls; DN , diabetic nephropathy Differences in the fALFF values The fALFF values obtained from the right cingulum anterior segment (RCA) and left cingulum middle segment (LCM) were significantly lower in patients with DN than in HCs. The fALFF value obtained from the right cingulum middle segment (RCM) was significantly higher in DN patients than in HCs (Fig. 2 ). In our study, the AUC values were 0.982 (95% confidence interval [CI]: 0.946–1.000) for the RCA, 0.942 (95% CI: 0.863–1.000) for the LCM, and 0.956 (95% CI: 0.885–1.000) for the RCM (Fig. 6). The RCA and RCM had the same optimal cut-off value, sensitivity, and specificity (optimal cut-off value = 0.867, sensitivity = 100.0%, and specificity = 86.7%). The optimal cut-off value for the LCM selected according to the biggest Youden index formula was 0.800, with a sensitivity of 86.7% and a specificity of 93.3%. Discussion DM is a metabolic disease that causes various microvascular complications, ultimately leading to irreversible damage to organs, such as the kidneys and brain. With the increasing prevalence of DM, the current clinical challenge is to identify which patients will rapidly progress to ESRD according to the accompanying brain dysfunction. Although microvascular damage and inflammation are thought to be the common substrates of kidney–brain implications, the mechanisms linking DN to cognitive impairment remain unclear. In ESRD, kidney–brain dysfunction occurs upon uremic toxin accumulation and/or eGFR reduction, cerebrovascular injury, in addition to damage to the blood–brain barrier (BBB) 18 . Lei et al. 19 explored the neural basis of cognitive impairment in patients with DM. Furthermore, hyperglycemia has been reported to cause endothelial damage, which is mediated by increased oxidative stress, inflammatory factors, and sorbitol production 20 . In addition, studies have indicated that the modulation of endothelial inflammatory factors and oxidative stress mediate endothelial cell injury, which contributes to an impairment in the eGFR and an almost simultaneous increase in the permeability of the BBB 9 . On the other hand, with a decrease in the eGFR, the kidney becomes unable to efficiently purge potential pathogenic proteins, which enter and accumulate in the brain via the impaired BBB, further promoting oxidative stress, inflammation, and immune activation 21 . Furthermore, pro-inflammatory cytokines and immune proteins can degrade the tight junction proteins of endothelial cells of the BBB, thereby increasing BBB permeability 22 and altering BBB transport mechanisms, which similar to the gut–brain axis 23 . Studies revealed that damage to the endothelium is one of the key mechanisms triggering the breakdown of the BBB, and that there is an overlap between inflammatory markers in the brain and kidneys 24 , That is, alterations to the kidney–brain axis can result in an increased risk of developing a multitude of brain diseases characterized by progressive cognitive impairment. The most significant findings of the current study were the notable difference in the fALFF values in the RCA (Brodmann area (BA)11), LCM (BA24), and RCM (BA24) between patients with DN and HCs (Table.2, Fig.2-4), and the correlation between the mean fALFF values in the RCA and eGFR in patients with DN(Fig.5). fALFF is one of the fMRI studies of our DN research team. The ROC curve(Figure 6) shows that the AUC values of the RCA, LCM, and RCM were all higher than 0.90. Furthermore, the RCA had the largest AUC value and Youden index, which indicated relatively better authenticity. BA11 is located in the ventral medial prefrontal cortex, and BA24 is situated in the cingulate cortex, which connects with the amygdala, orbitofrontal cortex, and hippocampus as part of the limbic system. The cingulate and prefrontal cortices are important nodes in the default mode and cognitive control networks 25,26 , which are related to cognitive, emotional, memory, and other functions(Table.2). Several studies have demonstrated that patients with chronic kidney disease have cognitive deficits 27 , and are poor regulators of emotion in the early disease stages 28 . In patients with renal impairment, the involvement of certain brain regions, especially in the prefrontal cortex, can lead to serious cognitive deficits in memory, attention, and other functions 29 . Reports of microstructural changes and white matter fiber density variation (mostly in the cingulum), which mediates cognitive impairment in patients with ESRD 30 , are consistent with the results of the current study; the altered fALFF values in the default mode may indicate a decline in cognitive ability in patients with DN. These studies have provided compelling evidence for an intimate relationship between the kidney and the brain, which is valuable for understanding the pathophysiological mechanisms underlying the concept of the kidney–brain axis. The prefrontal cortex is not only important for advanced brain functions, such as cognitive and emotional regulation, but also for visual processing; it contains a large portion of the visual cortex and selectively facilitates visual perception 31 . Our findings were highly consistent with the literature. The visual area of the frontal cortex receives projections from the primary and associated visual cortices, and the prefrontal cortex regulates visual image processing and working memory via the integration of eye and visual movements. Abnormal neuronal activity in the visual network is a key factor in DR; differences in the functional areas of the visual cortex in patients with DR have been detected using diffusion-weighted imaging and magnetic resonance spectroscopy 32 . We observed altered fALFF values in the prefrontal cortex of DN patients, which is in line with the clinical presentation of DR. The changes in fALFF values reflect the impaired neuronal activity of DN patients from the perspective of energy. This is likely due to an insufficient oxygen supply to the neurons caused by cerebral blood vessel damage or persistent hyperglycemia, which directly lead to neuronal damage. Therefore, we speculate that the altered fALFF values in our patients were associated with cerebral microvascular lesions. Oxidative stress, advanced glycation end products, protein kinase C activation and insulin resistance may promote the progression of microvascular lesions in DM 33 . Although we did not find significant differences in fALFF values in the occipital lobe, which contains the visual cortex, we did observe alterations in fALFF values in the prefrontal cortex, which contains a large portion of the visual cortex(Table.2, Fig.3). Therefore, we suggest that retinopathy in DN patients is synchronized with changes in brain function. However, at present, the specific mechanisms underlying retinal and visual cortical changes have not been elucidated and require further research. Strong correlations have been reported among diabetic microvascular complications, which share several common risk factors, such as hypertension. Hypertension and hyperglycemia can result in reduced filtration in the kidneys, albuminuria, and cerebral small vessel disease in the brain 34 . Studies suggest an overlap of small vessel glomerular, cerebral, and retinal pathological changes 35 , that is, the condition of one organ may reflect the state of another. For example, retinopathy is recognized as an early diagnostic indicator of DN, which suggests that during the early stages, microvascular lesions may have occurred simultaneously in the kidneys, eye, and brain. From a pathophysiological perspective, microalbuminuria is not only a marker of renal small vessel disease but also an indicator of cerebral microvascular lesions 36 . Takashi et al. followed 608 patients with DM for an average duration of 7.5 years and found that cerebral microangiopathy is a powerful marker of renal failure 37 . Previously, it was reported that a decrease in cerebral blood flow manifests due to a decrease in fALFF values in a certain brain area 38 . In our study, there was a linear correlation between the mean fALFF values and the eGFR(Fig.3, Fig.5), which indicated that the decline in renal function was accompanied by cerebrovascular dysfunction. Almost all neurological disorders worsen with the progression of microangiopathy, and nerve lesions can be quantified using rs-fMRI. We found significant differences in fALFF values in the default mode network and visual cortex-related areas, and mean fALFF values were linearly correlated to eGFR in patients with DN, which indicated that central neuropathy caused by DM and DN share similar characteristics. Regarding DN patients, the pathophysiology of cognitive could be influenced by many interrelated factors. Microalbuminuria, a marker of glomerular endothelial dysfunction, was positively correlated with cognitive impairment and progresses largely in parallel with cerebral small vessel disease 39 . Studies also revealed that there is an overlap between inflammatory markers in the brain and kidneys 40,41 , suggesting a parallel course of inflammation in both organs. Our findings may be highly valuable for understanding the mechanisms underlying the concept of the kidney–brain axis in patients with DN, in contrast to “pure” DM patients, and the association with diabetic microvascular complications. The current study has certain limitations. First, as the patient age was concentrated between 40 and 60 years, we were not able to perform an age stratified study. Second, we were unable to determine whether the fALFF alterations in the prefrontal cortex were DN-specific, and further studies are needed to confirm the correlation of other disorders with fALFF values. Finally, we did not follow up on the changes in cognitive function of patients with DN. To further validate the above conclusions, it is necessary to conduct large-scale clinical experiments, which may reveal albuminuria and kidney function as mediators and/or secondary outcomes, to provide a better understanding of the new concept of the kidney–brain axis. In conclusion, fALFF values were specifically altered in the default mode network and areas related to cognitive and visual function in DN patients, which may indicate cognitive function decline and visual impairment in patients with DN. Our serial studies provide a novel insight into the mechanisms of the kidney–brain axis in patients with DN and contributes to our understanding of the relationship between diabetic microvascular lesions. Declarations Acknowledgements We acknowledge the radiologists for their technical assistance. Competing interests The authors declare no competing interests. Author contributions Q.Z. organized the data and wrote the original draft. S.L. investigated, organized, and verified the data. Y.Z. analyzed the data. W.W. and Y.S. investigated and analyzed data, participated in writing the original draft. Y.C. supervised the data and data curation. Z.W. and Q.C. supervised and verified the data. J.L. verified the data, reviewed and edited the draft, monitored progress, provided funding, and managed the project. Data Availability The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request. Funding This work was supported by National Natural Science Foundation of China, Grant/Award Number 81660129, 81160118, 82060140. Ethical approval Our research complied with the Code of Ethics of the World Medical Association (Declaration of Helsinki). All procedures were performed in accordance with the ethical standards of First Affiliated Hospital of Nanchang University Ethical Committee and the Institutional Review Board (Reference Number: 2021039). References Boulton A (2020) Strengthening the International Diabetes Federation (IDF). Diabetes Res Clin Pract 160:108029. 10.1016/j.diabres.2020.108029 Saeedi P et al (2019) Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Res Clin Pract 157:107843. 10.1016/j.diabres.2019.107843 Vinik A (2012) Grand challenges in diabetes. 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PLoS ONE 9:e105117. 10.1371/journal.pone.0105117 Jiménez-Balado J et al (2020) Kidney function changes and their relation with the progression of cerebral small vessel disease and cognitive decline. J Neurol Sci 409:116635. 10.1016/j.jns.2019.116635 Satizabal CL, Zhu YC, Mazoyer B, Dufouil C, Tzourio C (2012) Circulating IL-6 and CRP are associated with MRI findings in the elderly: the 3C-Dijon Study. Neurology 78:720–727. 10.1212/WNL.0b013e318248e50f Gupta J et al (2012) Association between albuminuria, kidney function, and inflammatory biomarker profile in CKD in CRIC. Clin J Am Soc Nephrol 7:1938–1946. 10.2215/cjn.03500412 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5816556","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":401220496,"identity":"be09f679-35c5-42e4-8005-a415e6c47482","order_by":0,"name":"jinlei lv","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIie3QMQrCQBCF4QEhsXhmrSRBIVdYsbEIepUVwcoipaUH0D7BS3iEkQGr3MBG8QIBGzuNdla7doL7s+X7ilkin+8Hi4gMU05Q6ZW5vjuQ4E00IVkvZody40aadPN4OZJ24EJic5FcZwPNVS0ESlWPrcRIoRdIDtu95GMaljtjIWAj0IJIOnspQEaf3MgDdMRZELiQcP0ijG4F+orMkRSBbj45tt+iWpjfsJpMVdy61vU9S1XfQojwsYht81chu6x8Pp/vn3sCs65COoH7t3gAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"jinlei","middleName":"","lastName":"lv","suffix":""}],"badges":[],"createdAt":"2025-01-13 04:32:32","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5816556/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5816556/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73760940,"identity":"3035e556-b78a-421d-9cc6-7ff6fcf5f355","added_by":"auto","created_at":"2025-01-14 11:27:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":558476,"visible":true,"origin":"","legend":"\u003cp\u003eClinical characteristics of patients with diabetes nephropathy (DN).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Renal biopsy photography \u003cstrong\u003e(b)\u003c/strong\u003e Fundus photography of the eye\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5816556/v1/6cfd4f4f471912c631dc0660.png"},{"id":73760941,"identity":"b1252d8d-2594-46c1-8778-b5518937de93","added_by":"auto","created_at":"2025-01-14 11:27:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":366551,"visible":true,"origin":"","legend":"\u003cp\u003eSpontaneous brain activity in the diabetic nephropathy (DN) group (n=23).\u003c/p\u003e\n\u003cp\u003eNotes: The mean fractional amplitude of low-frequency fluctuation (fALFF) values of altered brain regions. Significant differences in fALFF values were observed between DN patients and healthy controls (HCs). Abnormal fALFF brain areas included the right cingulum anterior segment (RCA), left cingulum middle segment (LCM), and right cingulum middle segment (RCM). The red area represents the higher mean fALFF values and the blue area represents the lower mean fALFF values.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5816556/v1/1b4b72d5771bc4ab09399a02.png"},{"id":73760942,"identity":"5f37f23a-5a36-45fe-a353-d223589c587c","added_by":"auto","created_at":"2025-01-14 11:27:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":239932,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean fALFF values of altered brain regions\u003c/p\u003e\n\u003cp\u003eNotes: Red area indicates increased fALFF brain regions and yellow area indicates decreased fALFF brain regions. The fALFF values of DN patients were increased to various extents compared to those of HCs: right cingulum middle segment(RCM) (BA24, t = -4.5609, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05)(Fig.3, red region 1). The fALFF values of the following regions were decreased to various extents in the patients with DN as compared to the values in HCs: left cingulum middle segment(LCM) (BA24, t = 4.048, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05).(Fig.3, yellow region 2).right cingulum anterior segment(RCA) (BA11, t = 4.1675, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05),(Fig.3, yellow region 3). RCA showed a correlation with impared visual function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: BA, Brodmann area. Red Region 1 RCM, Yellow region 2 LCM, Yellow region 3 RCA\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5816556/v1/92c839a25b075d0e450c4f13.png"},{"id":73759783,"identity":"66276bd2-11c7-41d1-8b62-00f532f06f3e","added_by":"auto","created_at":"2025-01-14 11:19:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":44061,"visible":true,"origin":"","legend":"\u003cp\u003eMean value of the changed fractional amplitude of low-frequency fluctuation (fALFF) values between the diabetic nephropathy (DN) and healthy control (HC) groups (each n=23).\u003c/p\u003e\n\u003cp\u003eNotes: The mean fALFF values changed between DN and HCs. Compared with HCs, in the RCA and LCM, we found the mean fALFF value was lower in the DN group, but the mean value of DN group in the RCM was higher. Asterisk means the statistical significance p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5816556/v1/e396c6ba6c559cae290db540.png"},{"id":73761140,"identity":"7959874c-b6f6-490d-96c2-61b20960078a","added_by":"auto","created_at":"2025-01-14 11:35:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":47465,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean fALFF values in the right cingulum anterior segment (RCA) showed a significant moderate correlation with theestimated glomerular filtration rate (eGFR; r = 0.707, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5816556/v1/a05d846fda0811423368bc36.png"},{"id":73759790,"identity":"25a06400-1ef8-4a54-b919-89554723c754","added_by":"auto","created_at":"2025-01-14 11:19:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":53958,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve analyses.\u003c/p\u003e\n\u003cp\u003eNotes: The ROC curve was first used for medical analyses during the 1960s and 1970s. The area under the ROC curve (AUC) ranges from 0 to 1 and is widely recognized as a good index of diagnostic accuracy. A higher AUC value indicates better classification performance and thus, greater diagnostic value.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5816556/v1/ee02d469209aac8cd5b4fda5.png"},{"id":73761143,"identity":"6a2ce38d-bf64-41c5-8da1-1a4008d4d376","added_by":"auto","created_at":"2025-01-14 11:35:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1772078,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5816556/v1/be9d33ff-d2c6-49dc-bec6-e50137fa46f2.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAnalysis of the Kidney–Brain Axis via the Fractional Amplitude of Low-Frequency Fluctuation in Patients with Diabetic Nephropathy\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe incidence of diabetes mellitus (DM) is increasing. In 2020, the International Diabetes Federation reported that 465\u0026nbsp;million people were living with DM \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, which is expected to reach 600\u0026nbsp;million globally by 2045 \u003csup\u003e2\u003c/sup\u003e. With the increasing number of DM patients, diabetic microvascular complications are likely to become a significant public health issue worldwide \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Diabetic nephropathy (DN) is a dominant cause of end-stage renal disease (ESRD) globally, which is considered a primary cause of mortality in patients with DM \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Furthermore, there has been an increased awareness of diabetic central neuropathy. It has been reported that ESRD caused by DM can result in more severe neuropathy than that of other non-diabetic causes \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Our previous study showed that changes in brain function and cognitive decline are correlated with diabetic retinopathy (DR) and renal injury in DM patients\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e; however, the neuropathological mechanisms linking DM and DN to cognitive impairment remain unclear.\u003c/p\u003e \u003cp\u003eAccording to the Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, microalbuminuria and retinopathy are early predictors of DN in clinical practice. The retina is another organ that is affected by microvascular injury, and diabetic retinopathy (DR) is a major cause of blindness among young and aging people \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Studies have reported a positive relationship between DN and ESRD and cognitive decline, rather than \u0026ldquo;pure\u0026rdquo; type 2 DM patients \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Long-term hyperglycemia results in damage to the renal intrinsic cells due to inflammation and oxidative stress, which eventually leads to alterations in kidney and brain function \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Scholars proposed and proved the concept of a kidney\u0026ndash;brain axis, through which kidney damage influences brain health(9). In the early stages of DN, these complications usually progress silently; however, as the disease progresses, physiological and structural changes in kidney and brain function occur \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Therefore, elucidating the mechanisms of DN and exploring the interplay between the brain and kidney could help to prevent cognitive impairment in DN populations.\u003c/p\u003e \u003cp\u003eIn recent years, resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the clinic, especially in the detection and diagnosis of brain and eye diseases \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Among these measures, fractional amplitude of low-frequency fluctuation (fALFF) values allow the signal intensity during resting state to be studied, which reflects the level of spontaneous activity of each voxel based on energy expenditure, and provides local brain activity information with physiological significance \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. A meta-analysis of rs-fMRI studies showed that compared to healthy controls, ESRD patients with cognitive impairment exhibit decreased resting-state activity. In certain brain areas, including the default mode, visual recognition, and executive control network, the fALFF was lower in patients with chronic kidney disease \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStudies have revealed a relationship between kidney damage and cognitive impairment \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e; however, the pathophysiological mechanisms underlying the changes in brain activity and neural characteristics, as well as the correlation between altered fALFF values and the clinical features of DN, are not yet fully understood. We hypothesized that patients with DN present with alterations in brain activity based on the kidney\u0026ndash;brain axis. To date, how DN influences brain function remains poorly understood, and the mechanism underlying the kidney\u0026ndash;brain axis requires in-depth investigation. In the present study, we aimed to assess brain functional alterations of DN patients using the fALFF approach. We focused on the clinical aspects concerning the association between albuminuria, microvascular kidney disease, and brain functional changes in patients with DN.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eWe recruited 23 patients with DN from Nephrology Department of our hospital, and 23 sex-matched HCs from the outpatient clinic between October 2019 and October 2020. The inclusion criteria for the DN group were as follows: (1) a minimum of 5 years duration of type 2 DM; (2) retinopathy (non-proliferative DR or PDRproliferative DR, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb); (3) a definite renal pathological diagnosis of DN (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea); and (4) an eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 [ml/min]/1.73 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The inclusion criteria for the HC group were as follows: (1) normal brain MRI; (2) no history of neurological or psychiatric disorders; (3) absence of kidney diseases; and (4) no history of ocular diseases. We excluded patients or HCs with a history of neurological disorders; primary and congenital renal diseases; cardiovascular diseases; cataracts, glaucoma, or other eye diseases; or MRI contraindications. Patients with type 2 DM all used insulin or oral hypoglycemic drugs to control blood sugar levels, and their hemoglobin A1c (HbA1c) level was maintained within 7% to rule out the effects of hyperglycemia on brain function. We found no significant differences in sex, age, or other basic information between the two groups. All participants were aged between 18 and 75 years, were right-handed, and had more than 6 years of formal education. We collected the following data from all participants: name, sex, age, body weight, years of education, years of DM, systolic blood pressure, diastolic blood pressure, binocular vision values, serum creatinine level, relevant family history, and MRI contraindications. Additionally, the 24-h urinary protein value and history of hypoglycemia and insulin therapy data were collected from DN patients.\u003c/p\u003e \u003cp\u003e Our research complied with the Code of Ethics of the World Medical Association (Declaration of Helsinki). All methods were performed in accordance with the relevant guidelines and regulations, and this study was approved in accordance with the ethical standards of First Affiliated Hospital of Nanchang University Ethical Committee and Institutional Review Board (Reference Number: 2021039). In addition, we informed all participants about the process and matters requiring attention for this study; all provided informed consent\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFundus ophthalmoscopy\u003c/h3\u003e\n\u003cp\u003eAn experienced ophthalmologist performed visual acuity and fundus examinations and determined that all patients with DM presented with associated retinopathy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The eye condition of each patient was determined by that of their more severely affected retina.\u003c/p\u003e\n\u003ch3\u003eCalculation of eGFR\u003c/h3\u003e\n\u003cp\u003eSingle-photon emission computed tomography was performed to calculate the eGFR of all participants.\u003c/p\u003e\n\u003ch3\u003eMRI parameters\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMRI data acquisition and analysis\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS 22.0 statistical software. Data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and independent sample t-tests were used to analyze differences between the two groups. Correlations between variables were determined using Pearson\u0026rsquo;s correlation analysis. After preprocessing, the MRI data were analyzed using the REST software, and two independent whole-brain level samples were tested using the fALFF method. Following AlphaSim correction (full width half maximum, 8 mm; voxel connection radius, 5 mm), we considered all voxels with a \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as statistically significant. We included the MRI data of all participants in the analysis and plotted the ROC curve. The AUC, sensitivity, specificity, and Youden index were calculated to assess the authenticity of the diagnosis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and clinical data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics of patients with diabetic nephropathy (DN) and healthy controls (HCs).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDN (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCs (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale/female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11/12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11/12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.52\u0026thinsp;\u0026plusmn;\u0026thinsp;8.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.57\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.78\u0026thinsp;\u0026plusmn;\u0026thinsp;8.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.87\u0026thinsp;\u0026plusmn;\u0026thinsp;7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr (\u0026micro;mol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e277.61\u0026thinsp;\u0026plusmn;\u0026thinsp;119.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.72\u0026thinsp;\u0026plusmn;\u0026thinsp;7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACR (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2309.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1247.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.43\u0026thinsp;\u0026plusmn;\u0026thinsp;10.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCR (g/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR ([ml/min]/1.73㎡)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.92\u0026thinsp;\u0026plusmn;\u0026thinsp;10.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.15\u0026thinsp;\u0026plusmn;\u0026thinsp;7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;24.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24-hour urinary protein quantity (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.65\u0026thinsp;\u0026plusmn;\u0026thinsp;5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic information and relevant clinical characteristics of the participants. Data are presented as the number (%) or mean (SD), as appropriate. The two groups (DN patients and healthy controls (HCs)) were comparable regarding sex (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.99), age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.99), and body weight (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eScr\u003c/em\u003e serum creatinine, \u003cem\u003eACR\u003c/em\u003e urinary albumin/creatinine ratio, \u003cem\u003ePCR\u003c/em\u003e urinary protein/creatinine, \u003cem\u003eeGFR\u003c/em\u003e estimated glomerular filtration rate.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBrain regions alternation from our data and its potential impact\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain regions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExperimental result\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrain function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnticipated results\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight cingulum anterior segment(RCA, BA11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNs\u0026thinsp;\u0026gt;\u0026thinsp;HCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eprefrontal associational integration, orbital and rectus gyri, rostral part of the superior frontal gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVisual function,\u003c/p\u003e \u003cp\u003eSense of smell,\u003c/p\u003e \u003cp\u003eSocial behavior\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft cingulum middle segment(LCM, BA24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNs\u0026thinsp;\u0026lt;\u0026thinsp;HCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eemotional and cognitive processing, connects with the amygdala, orbitofrontal cortex, and hippocampus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecognitive, emotional, memory, and other functions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight cingulum middle segment(RCM, BA24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNs\u0026thinsp;\u0026lt;\u0026thinsp;HCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eemotional and cognitive processing, connects with the amygdala, orbitofrontal cortex, and hippocampus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecognitive, emotional, memory, and other functions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eHCs\u003c/em\u003e ,healthy controls; \u003cem\u003eDN\u003c/em\u003e, diabetic nephropathy\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in the fALFF values\u003c/h2\u003e \u003cp\u003eThe fALFF values obtained from the right cingulum anterior segment (RCA) and left cingulum middle segment (LCM) were significantly lower in patients with DN than in HCs. The fALFF value obtained from the right cingulum middle segment (RCM) was significantly higher in DN patients than in HCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \n\u003cp\u003eIn our study, the AUC values were 0.982 (95% confidence interval [CI]: 0.946\u0026ndash;1.000) for the RCA, 0.942 (95% CI: 0.863\u0026ndash;1.000) for the LCM, and 0.956 (95% CI: 0.885\u0026ndash;1.000) for the RCM (Fig. 6). The RCA and RCM had the same optimal cut-off value, sensitivity, and specificity (optimal cut-off value = 0.867, sensitivity = 100.0%, and specificity = 86.7%). The optimal cut-off value for the LCM selected according to the biggest Youden index formula was 0.800, with a sensitivity of 86.7% and a specificity of 93.3%.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDM is a metabolic disease that causes various microvascular complications, ultimately leading to irreversible damage to organs, such as the kidneys and brain. With the increasing prevalence of DM, the current clinical challenge is to identify which patients will rapidly progress to ESRD according to the accompanying brain dysfunction. Although microvascular damage and inflammation are thought to be the common substrates of kidney\u0026ndash;brain implications, the mechanisms linking DN to cognitive impairment remain unclear. In ESRD, kidney\u0026ndash;brain dysfunction occurs upon uremic toxin accumulation and/or eGFR reduction, cerebrovascular injury, in addition to damage to the blood\u0026ndash;brain barrier (BBB) \u003csup\u003e18\u003c/sup\u003e. Lei et al. \u003csup\u003e19\u003c/sup\u003e explored the neural basis of cognitive impairment in patients with DM. Furthermore, hyperglycemia has been reported to cause endothelial damage, which is mediated by increased oxidative stress, inflammatory factors, and sorbitol production \u003csup\u003e20\u003c/sup\u003e. In addition, studies have indicated that the modulation of endothelial inflammatory factors and oxidative stress mediate endothelial cell injury, which contributes to an impairment in the eGFR and an almost simultaneous increase in the permeability of the BBB \u003csup\u003e9\u003c/sup\u003e. On the other hand, with a decrease in the eGFR, the kidney becomes unable to efficiently purge potential pathogenic proteins, which enter and accumulate in the brain via the impaired BBB, further promoting oxidative stress, inflammation, and immune activation \u003csup\u003e21\u003c/sup\u003e. Furthermore, pro-inflammatory cytokines and immune proteins can degrade the tight junction proteins of endothelial cells of the BBB, thereby increasing BBB permeability \u003csup\u003e22\u003c/sup\u003e and altering BBB transport mechanisms, which similar to the gut\u0026ndash;brain axis \u003csup\u003e23\u003c/sup\u003e. Studies revealed that damage to the endothelium is one of the key mechanisms triggering the breakdown of the BBB, and that there is an overlap between inflammatory markers in the brain and kidneys\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e, That is, alterations to the kidney\u0026ndash;brain axis can result in an increased risk of developing a multitude of brain diseases characterized by progressive cognitive impairment.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The most significant findings of the current study were the notable difference in the fALFF values in the RCA (Brodmann area (BA)11), LCM (BA24), and RCM (BA24) between patients with DN and HCs (Table.2, Fig.2-4), and the correlation between the mean fALFF values in the RCA and eGFR in patients with DN(Fig.5). fALFF is one of the fMRI studies of our DN research team. The ROC curve(Figure 6) shows that the AUC values of the RCA, LCM, and RCM were all higher than 0.90. Furthermore, the RCA had the largest AUC value and Youden index, which indicated relatively better authenticity. BA11 is located in the ventral medial prefrontal cortex, and BA24 is situated in the cingulate cortex, which connects with the amygdala, orbitofrontal cortex, and hippocampus as part of the limbic system. The cingulate and prefrontal cortices are important nodes in the default mode and cognitive control networks \u003csup\u003e25,26\u003c/sup\u003e, which are related to cognitive, emotional, memory, and other functions(Table.2). Several studies have demonstrated that patients with chronic kidney disease have cognitive deficits \u003csup\u003e27\u003c/sup\u003e, and are poor regulators of emotion in the early disease stages \u003csup\u003e28\u003c/sup\u003e. In patients with renal impairment, the involvement of certain brain regions, especially in the prefrontal cortex, can lead to serious cognitive deficits in memory, attention, and other functions \u003csup\u003e29\u003c/sup\u003e. Reports of microstructural changes and white matter fiber density variation (mostly in the cingulum), which mediates cognitive impairment in patients with ESRD \u003csup\u003e30\u003c/sup\u003e, are consistent with the results of the current study; the altered fALFF values in the default mode may indicate a decline in cognitive ability in patients with DN. These studies have provided compelling evidence for an intimate relationship between the kidney and the brain, which is valuable for understanding the pathophysiological mechanisms underlying the concept of the kidney\u0026ndash;brain axis.\u003c/p\u003e\n\u003cp\u003eThe prefrontal cortex is not only important for advanced brain functions, such as cognitive and emotional regulation, but also for visual processing; it contains a large portion of the visual cortex and selectively facilitates visual perception \u003csup\u003e31\u003c/sup\u003e. Our findings were highly consistent with the literature. The visual area of the frontal cortex receives projections from the primary and associated visual cortices, and the prefrontal cortex regulates visual image processing and working memory via the integration of eye and visual movements. Abnormal neuronal activity in the visual network is a key factor in DR; differences in the functional areas of the visual cortex in patients with DR have been detected using diffusion-weighted imaging and magnetic resonance spectroscopy \u003csup\u003e32\u003c/sup\u003e. We observed altered fALFF values in the prefrontal cortex of DN patients, which is in line with the clinical presentation of DR. The changes in fALFF values reflect the impaired neuronal activity of DN patients from the perspective of energy. This is likely due to an insufficient oxygen supply to the neurons caused by cerebral blood vessel damage or persistent hyperglycemia, which directly lead to neuronal damage. Therefore, we speculate that the altered fALFF values in our patients were associated with cerebral microvascular lesions.\u003c/p\u003e\n\u003cp\u003eOxidative stress, advanced glycation end products, protein kinase C activation and insulin resistance may promote the progression of microvascular lesions in DM \u003csup\u003e33\u003c/sup\u003e. Although we did not find significant differences in fALFF values in the occipital lobe, which contains the visual cortex, we did observe alterations in fALFF values in the prefrontal cortex, which contains a large portion of the visual cortex(Table.2, Fig.3). Therefore, we suggest that retinopathy in DN patients is synchronized with changes in brain function. However, at present, the specific mechanisms underlying retinal and visual cortical changes have not been elucidated and require further research. Strong correlations have been reported among diabetic microvascular complications, which share several common risk factors, such as hypertension. Hypertension and hyperglycemia can result in reduced filtration in the kidneys, albuminuria, and cerebral small vessel disease in the brain \u003csup\u003e34\u003c/sup\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eStudies suggest an overlap of small vessel glomerular, cerebral, and retinal pathological changes \u003csup\u003e35\u003c/sup\u003e, that is, the condition of one organ may reflect the state of another. For example, retinopathy is recognized as an early diagnostic indicator of DN, which suggests that during the early stages, microvascular lesions may have occurred simultaneously in the kidneys, eye, and brain. From a pathophysiological perspective, microalbuminuria is not only a marker of renal small vessel disease but also an indicator of cerebral microvascular lesions \u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTakashi et al. followed 608 patients with DM for an average duration of 7.5 years and found that cerebral microangiopathy is a powerful marker of renal failure \u003csup\u003e37\u003c/sup\u003e. Previously, it was reported that a decrease in cerebral blood flow manifests due to a decrease in fALFF values in a certain brain area \u003csup\u003e38\u003c/sup\u003e. In our study, there was a linear correlation between the mean fALFF values and the eGFR(Fig.3, Fig.5), which indicated that the decline in renal function was accompanied by cerebrovascular dysfunction. Almost all neurological disorders worsen with the progression of microangiopathy, and nerve lesions can be quantified using rs-fMRI. We found significant differences in fALFF values in the\u0026nbsp;default mode network and visual cortex-related areas, and mean fALFF values were linearly correlated to eGFR in patients with DN, which indicated that central neuropathy caused by DM and DN share similar characteristics. Regarding DN patients, the pathophysiology of cognitive could be influenced by many interrelated factors. Microalbuminuria, a marker of glomerular endothelial dysfunction, was positively correlated with cognitive impairment and progresses largely in parallel with cerebral small vessel disease \u003csup\u003e39\u003c/sup\u003e. Studies also revealed that there is an overlap between inflammatory markers in the brain and kidneys \u003csup\u003e40,41\u003c/sup\u003e, suggesting a parallel course of inflammation in both organs. Our findings may be highly valuable for understanding the mechanisms underlying the concept of the kidney\u0026ndash;brain axis in patients with DN, in contrast to \u0026ldquo;pure\u0026rdquo; DM patients, and the association with diabetic microvascular complications.\u003c/p\u003e\n\u003cp\u003eThe current study has certain limitations. First, as the patient age was concentrated between 40 and 60 years, we were not able to perform an age stratified study. Second, we were unable to determine whether the fALFF alterations in the prefrontal cortex were DN-specific, and further studies are needed to confirm the correlation of other disorders with fALFF values. Finally, we did not follow up on the changes in cognitive function of patients with DN. To further validate the above conclusions, it is necessary to conduct large-scale clinical experiments, which may reveal albuminuria and kidney function as mediators and/or secondary outcomes, to provide a better understanding of the new concept of the kidney\u0026ndash;brain axis.\u003c/p\u003e\n\u003cp\u003eIn conclusion, fALFF values were specifically altered in the default mode network and areas related to cognitive and visual function in DN patients, which may indicate cognitive function decline and visual impairment in patients with DN. Our serial studies provide a novel insight into the mechanisms of the kidney\u0026ndash;brain axis in patients with DN and contributes to our understanding of the relationship between diabetic microvascular lesions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the radiologists for their technical assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQ.Z. organized the data and wrote the original draft. S.L. investigated, organized, and verified the data. Y.Z. analyzed the data. W.W. and Y.S. investigated and analyzed data, participated in writing the original draft. Y.C. supervised the data and data curation. Z.W. and Q.C. supervised and verified the data. J.L. verified the data, reviewed and edited the draft, monitored progress, provided funding, and managed the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China, Grant/Award Number 81660129, 81160118, 82060140.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur research complied with the Code of Ethics of the World Medical Association (Declaration of Helsinki). 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Clin J Am Soc Nephrol 7:1938\u0026ndash;1946. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2215/cjn.03500412\u003c/span\u003e\u003cspan address=\"10.2215/cjn.03500412\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National Natural Science Foundation of China","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"diabetic nephropathy, type 2 diabetes, blood–brain barrier, kidney–brain Axis","lastPublishedDoi":"10.21203/rs.3.rs-5816556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5816556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCentral neuropathies caused by diabetic nephropathy (DN) share similar characteristics. The present study aimed to analyze the changes in brain function of patients with DN based on the kidney\u0026ndash;brain axis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study population consisted of patients with DN and healthy controls (n\u0026thinsp;=\u0026thinsp;23 per group). Brain resting-state functional magnetic resonance imaging examination was performed on all participants, and the fractional amplitude of low-frequency fluctuation (fALFF) values were calculated. The diagnostic authenticity was assessed through receiver operating characteristic curves using sensitivity, specificity, and Youden index. Statistical analysis included Pearson's correlation between mean fALFF values and DN data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe imaging analysis revealed that DN patients exhibited lower fALFF values in the right cingulum anterior segment (RCA) and left cingulum middle segment, and increased fALFF values in the right cingulum middle segment compared to control subjects. The correlation analysis demonstrated that mean fALFF values in the RCA correlated with the estimated glomerular filtration rate in DN patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe research findings demonstrated significant differences in fALFF values in the default mode network and visual cortex-related areas. These observations may be highly valuable for understanding the kidney\u0026ndash;brain axis mechanisms of DN, as well as the associations between diabetic microvascular complications.\u003c/p\u003e","manuscriptTitle":"Analysis of the Kidney–Brain Axis via the Fractional Amplitude of Low-Frequency Fluctuation in Patients with Diabetic Nephropathy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-14 11:19:13","doi":"10.21203/rs.3.rs-5816556/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7006801b-8111-4daa-a0a9-043f08f6a91a","owner":[],"postedDate":"January 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-14T11:19:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-14 11:19:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5816556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5816556","identity":"rs-5816556","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}