The incidence of uterine cancer is increasing worldwide in recent decades, especially in the young age population. In parallel, a trend of early onset of diabetes mellitus (DM) and obesity was also observed in the young population. Since DM and obesity are known risk factors of uterine cancer, we proposed to investigate the age-specific risks of uterine cancer in subjects with DM and related metabolic comorbidities. In this nationwide population-based study, we found the risk of uterine cancer in DM subjects was significantly higher in the younger age groups, especially in the age group 30–39 years. Obesity, polycystic ovarian syndrome, hyperlipidemia, statin use, and hormone replacement therapy were also significantly associated with uterine cancer in subjects younger than 50 years of age. Therefore, subjects with DM, especially younger women with respective comorbidities, should be recommended to have appropriate uterine cancer screenings for early detection.p < 0.0001). The effect of DM was highest at age 30–39 years . In the group of <50 years old, DM patients had at least a twofold higher risk of developing uterine cancer . Subjects among all ages diagnosed with polycystic ovary syndrome (PCOS) , obesity , and those undergoing hormone replacement therapy (HRT)  were also positively associated with uterine cancer. Positive associations of hyperlipidemia  and statin use  on uterine cancer were only observed in subjects <50 years. On the contrary, hyperlipidemia was negatively associated with uterine cancer in subjects ≥50 years . Conclusions: DM is in general the most important risk factor for uterine cancer, especially in premenopausal women. Obesity, PCOS, HPL, statin use, and HRT were also associated with uterine cancer in subjects younger than 50 years. Premenopausal women with DM and respective comorbidities should be aware of the development of uterine cancer.Introduction: The global incidence of uterine cancer has increased substantially in recent decades. We evaluated if the trend of increasing prevalence of diabetes mellitus (DM) and obesity are attributed to the development of uterine cancer. Methods: Using data derived from the National Health Insurance database and Taiwan Cancer Registry, multivariate Cox proportional hazards regression models were adapted to analyze the risk factors of uterine cancer with potential confounding variables. Results: There were a total of 5,104,242 women aged 30–70 years enrolled in the study and 147,772 of them were diagnosed with DM during 2005–2007. In a total of 11 years of follow-up, 14,398 subjects were diagnosed with uterine cancer. An elevated risk of uterine cancer was observed in women with DM of all ages (HR 1.66, 95% CI 1.53–1.81,  In the United States, uterine cancer ranked as the fourth most common cancer and the seventh leading cause of cancer death in women , whose iIn Taiwan, the incidence and prevalence of DM are steadily increasing, especially in the young population . SimilarDM and obesity both belong to the spectrum of metabolic syndrome, which is a collection of several clinical conditions, including insulin resistance, hyperlipidemia (HPL), and hypertension. Among these, HPL is also associated with an increased risk of cancer ; howeverPreviously, Chen et al. reported that DM significantly increases the risk of EC in all age, and the highest relative risk was observed in women younger than 50 years of age . HoweverTherefore, we proposed to utilize a representative nationwide database, the National Health Insurance (NHI) database, and the Taiwan Cancer Registry (TCR), to investigate the age-specific risks of uterine cancer in subjects with diabetes and related metabolic comorbidities.The information of the subjects was obtained from the National Health Insurance Database in Taiwan. This single-payer National Health Insurance Program was launched in 1995 and the entire population in Taiwan, nearly 23 million subjects, were enrolled in this program. Women aged between 30–70 years old from 1 January 2005 to 31 December 2015 were in this analysis. Women younger than 30 years were excluded to ensure that most of the DM were type 2, since the onset of type 1 DM occurs more frequently in people under 30 years of age. Women aged older than 70 years were excluded since most uterine cancers were diagnosed before 70. Additional exclusion criteria included: (1) subjects diagnosed with DM before 2005, (2) subjects diagnosed with any cancer before 2005, (3) women with a medical record of hysterectomy in 2004, (4) uterine cancer diagnosed prior to the diagnosis of DM, and (5) hysterectomy prior to the diagnosis of DM. We use the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to code the diagnosis of uterine cancer, DM, obesity, PCOS, and HPL of the subjects enrolled . In Taiwhttps://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=269&pid=14913, accessed on 13 January 2022). All subjects enrolled in this study were regularly followed up either until the end of 2015 or until the date of censoring. The date of censoring was (1) the date of diagnosis of uterine cancer, (2) the mortality time, or (3) the date of hysterectomy. This project was approved by the Ethics Committee and the Institutional Review Board of Fu Jen Catholic University (IRB No. C107021), New Taipei City, Taiwan.Vital status during the follow-up period and pathological subtypes of uterine cancer in the enrolled subjects were determined by data link with the healthy profiles of the National Cancer Registry and the National Death Certification System in Taiwan, respectively. Taiwan Cancer Registry (TCR) is a medical system that contains information for epidemiology, diagnosis, and treatment summary of newly diagnosed cancer cases in Taiwan since 1979 . The comHistopathological subtypes of uterine cancers were classified as carcinoma and sarcoma. Uterine carcinomas include endometrioid adenocarcinoma, serous adenocarcinoma, clear cell adenocarcinoma and carcinosarcoma. Uterine sarcomas were composed of low grade endometrial stromal sarcoma (LGESS), endometrial stromal sarcoma (ESS), leiomyosarcoma (LMS) and adenosarcoma . Subject2 using the criteria defined by the Department of Health in Taiwan. Hormone replacement therapy (HRT) was determined according to the Anatomical Therapeutic Chemical (ATC) classification system, and prescription data were retrieved from the NHI database  and ≥50 years  groups to see the age-specific risk of developing uterine cancer in subjects with DM, PCOS, obesity, HPL and under HRT, and use of statins in both age groups. Moreover, we performed a stratified analysis for each given comorbidity in subjects with or without DM to clarify the independent risk of each comorbidity. All significance tests were two-tailed, and the value of α was 0.05. All statistical analyses were performed using the Statistical Analysis System (SAS version 9.4).p < 0.0001). It indicates that subjects diagnosed with DM were older than those in the non-DM group. There was a total of 11,164, 833 women seeking medical care in 2005 to 2007 based on the NHI database, and 6,274,517 subjects were excluded after applying exclusion criteria . Subsequp < 0.0001). Regarding obesity, it was 3.09% and 0.69% in the DM and non-DM groups, respectively (p < 0.0001). Statins were also prescribed more frequently in the DM group than in the non-DM group . In contrast, HRT was prescribed more frequently in the non-DM group . Rate of PCOS was similar between these two groups (0.57% vs. 0.54% in DM and non-DM groups).As expected, more metabolic comorbidities were observed in the DM group, especially obesity and HPL. HPL was present in 39.42% of subjects in the DM group, but only 8.27% in the non-DM group (p < 0.0001). Similar to general cohort .The accumulated probability of uterine cancer in subjects with DM and without DM during follow-up years is shown in p < 0.0001) of developing uterine cancer followed by the 40–49 age group  and 60–69 . Among DM and comorbidities, PCOS has the highest HR among all potential risk factors . Obesity and DM were significantly associated with risks of developing uterine cancer . Furthermore, subjects under HRT were also at risk of developing uterine cancer . HPL and use of statins were not correlated with the development of uterine cancer. In the multivariate Cox proportional hazard regression model, with or without adjustment, age was positively associated with uterine cancer . The 50–Our findings indicated that subjects of specific age, diagnosis of PCOS, obesity, DM, and HRT were at an increased risk of developing uterine cancer. p < 0.0001). DM was positively associated with developing uterine cancer, but RR gradually decreases with aging  were at a significantly higher risk of developing uterine cancer compared to postmenopausal subjects with DM . The association between HPL and uterine cancer was specifically significant in the <50 years group . Similarly, the association between statin users and uterine cancer was also only observed in the <50 years group . In contrast, in the ≥50 years group, we observed a 9% reduction in the risk of uterine cancer  in subjects with HPL, and a 5% reduction in risk in subjects prescribed statins, although it was not significant . However, if we further considered the interactions between HPL and statin use after age stratification and corresponding adjustments, HPL alone, statin use alone, and HPL plus statin use all showed trends of risk reduction in developing uterine cancer in ≥50 years group (p = 0.001)  and ≥50 years  groups for further survey. After using the multivariate Cox proportional hazard regression model and age stratification, the risk of developing uterine cancer was statistically significant in subjects with DM, obesity, and under HRT in both age groups . Premenors group . In part= 0.001) .p < 0.0001; ≥50 years, HR 2.05, 95% CI 1.67–2.51, p < 0.0001). HRT was positively associated with uterine cancer in both age groups, but the risk was even higher in the ≥50 years group . Since PCOS cannot be diagnosed after menopause, the association between PCOS and uterine cancer was only observed in the premenopausal group .Regarding obesity, both age groups had a similar risk of developing uterine cancer  was significantly associated with uterine cancer compared to subjects without PCOS in a non-DM group. A similar effect was also observed in the non-DM group with HRT . Higher HR was found in DM with PCOS  or DM with HRT  without statistical significance. Obesity is the comorbidity associated with uterine cancer in both non-DM and DM groups . Although PCOS and HRT were risk factors for uterine cancer, significance was only observed in the non-DM group in this analysis. This result might be attributed to the small sample size in subjects of DM with uterine cancer that was further stratified by PCOS or HRT. The numbers, person years, incidence of uterine cancer, and adjusted HR were further calculated in different sub-groups stratified by diabetes and comorbidities simultaneously . Since DUterine cancer is a worldwide public health issue. In the United States, the median age of uterine cancer diagnosis was 63 years from 2012 to 2016 . HoweverThe WHO estimated that the worldwide prevalence of DM was 171 million in 2000, and would be approximately 366 million by 2030 . In TaiwWe also found that obesity was associated with an increased risk of uterine cancer in both the DM and non-DM groups. Obese women were at two to four times greater risk of developing uterine cancer than those with normal body weight in the United States . In a reIn addition to the associations between DM, obesity, and uterine cancer development, there was also increased cancer mortality in patients with DM after age and comorbidities were adjusted . This phIn our cohort, nearly 40% of the subjects in the DM group had HPL, whereas only 8% in the non-DM group. A case-control study suggested that lower levels of serum cholesterol and low-density lipoprotein cholesterol (LDL) were associated with a higher risk of endometrial cancer . HoweverIn our study, HRT was positively associated with uterine cancer development after age, DM, and other comorbidities were adjusted. No significance was found in DM subjects with HRT and uterine cancer, which could be explained by the small sample size of the DM with HRT group. Different regimens of HRT (estrogen only or estrogen plus progestin) might also affect cancer risk. In subjects diagnosed with uterine cancer, HRT was associated with higher cancer risk in both premenopausal and postmenopausal groups. Endometrial cancer was strongly associated with the use of unopposed estrogen in postmenopausal women without hysterectomies . This riThere were some strengths as well as limitations of our study. Regarding the strengths, the study cohort was collected from the NHI database with links to the TCR database, which is a representative nationwide database with a large sample size and high completeness. Second, multivariate Cox proportional hazard regression models with adjustment for potential confounding factors were performed. Third, age-stratification was carried out to analyze the risk in premenopausal and postmenopausal populations. There are some limitations in this study; first, we cannot differentiate type 1 or type 2 DM in our cohort, but we excluded DM subjects younger than 30 years old to ensure that most of the DM were type 2. Second, information on the duration or severity of DM and treatment regimens was lacking in our cohort. Studies have reported that a significantly increased incidence of uterine cancer was seen within the first month after type 2 DM was diagnosed , or evenIn this nationwide population-based study, we have identified positive associations between DM and uterine cancer of all ages, and this risk was much higher in the younger subjects, especially in the age group 30–39 years. Similarly, obesity, PCOS, HPL, statin use, and HRT were also significantly associated with uterine cancer in subjects younger than 50 years of age. Among all risk factors of uterine cancer, DM is overall the most important one. We instead observed a protective effect of HPL and statin use on uterine cancer at an age ≥50 years. Therefore, subjects with DM, especially younger women with respective comorbidities, should be recommended to have appropriate uterine cancer screenings to achieve early detection. Furthermore, HRT should be prescribed with caution, especially in postmenopausal women with DM or obesity.


Olfactory disorder is one of the sensory features that reflects a decline in cognitive function. However, olfactory changes and the discernibility of smell testing in the aging population have yet to be fully elucidated. Therefore, this study aimed to examine the effectiveness of the Chinese Smell Identification Test (CSIT) in distinguishing individuals with cognitive decline from those with normal aging and to determine whether the patients with MCI and AD show changes in their olfactory identification abilities.This cross-sectional study included eligible participants aged over 50 years between October 2019 and December 2021. The participants were divided into three groups: individuals with mild cognitive impairment (MCI), individuals with Alzheimer's disease (AD), and cognitively normal controls (NCs). All participants were assessed using neuropsychiatric scales, the Activity of Daily Living scale, and the 16-odor cognitive state test (CSIT) test. The test scores and the severity of olfactory impairment were also recorded for each participant.P < 0.001). An analysis showed that 19.9% of NCs exhibited mild olfactory impairment, while 52.7% of patients with MCI and 69% of patients with AD exhibited mild to severe olfactory impairment. The CSIT score was positively correlated with the MoCA and MMSE scores. The CIST score and the severity of olfactory impairment were identified as robust indicators for MCI and AD, even after adjusting for age, gender, and level of education. Age and educational level were identified as two important confounding factors that influence cognitive function. However, no significant interactive effects were observed between these confounders and CIST scores in determining the risk of MCI. The area under the ROC curve (AUC) generated from the ROC analysis was 0.738 and 0.813 in distinguishing patients with MCI and patients with AD from NCs based on the CIST scores, respectively. The optimal cutoff for distinguishing MCI from NCs was 13, and for distinguishing AD from NCs was 11. The AUC for distinguishing AD from MCI was 0.62.In total, 366 eligible participants were recruited, including 188 participants with MCI, 42 patients with AD, and 136 NCs. Patients with MCI achieved a mean CSIT score of 13.06 ± 2.05, while patients with AD achieved a mean score of 11.38 ± 3.25. These scores were significantly lower than those of the NC group (14.6 ± 1.57; The olfactory identification function is frequently affected in patients with MCI and patients with AD. CSIT is a beneficial tool for the early screening of cognitive impairment among elderly patients with cognitive or memory issues. Alzheimer's disease (AD) is the most common subset of dementia, and its incidence has been steadily rising with the aging of the global population. As a clinical transitional phase to AD, mild cognitive impairment (MCI) affects ~3–42% of the adult population and is associated with a conversion rate ranging from ~2–31%, depending on heterogeneity, diagnostic criteria, and sample characteristics , the Connecticut Chemosensory Clinical Research Center (CCCRC) olfactory test, the Sniffin' Sticks olfactory test, and the recently developed Brief Smell Identification Test (B-SIT), which is based on the UPSIT  participants between the age of 50 and 85 years; (2) those with no visual or hearing impairments; and (3) those capable of completing neuropsychiatric tests and who volunteered for the olfactory function test and provided signed and informed consent. The exclusion criteria were as follows: (1) secondary dementia due to another known etiology; (2) severe concomitant intracranial diseases such as encephalitis, meningitis, acute cerebral stroke, seizure, brain tumors, and other diseases; (3) vascular or mixed MCI as assessed by the Hachinski Ischemia Scale (HIS) score of ≥ 4; (4) acute respiratory tract infections or olfactory diseases such as rhinitis and sinusitis within 2 weeks; (5) long-term exposure to pesticides, metal dust, and other volatile substances; and (6) impairment or loss of olfactory function due to other known etiologies, such as head trauma, rhinosinusitis, post-infectious sequelae from COVID-19, medication side effects, and other rare conditions. Demographic data for the eligible participants were collected, including age, gender, educational levels, smoking, alcohol consumption, concurrent diseases, and memory complaints. The participants were asked whether they experienced a subjective memory decline for 3 months or not and whether they were worried about this decline. Meanwhile, they were asked the question “Did your parents ever have Alzheimer's disease/dementia?” to document their family history of AD/dementia. In comparison to the age- and sex-matched population, participants with reduced sleep duration reported subjective sleep disorder, difficulty falling asleep, frequent awakenings, early morning awakenings, and excessive daytime sleepiness. The enrolled participants were categorized into the NC, MCI, and AD groups based on the corresponding diagnostic criteria after carefully considering clinical characteristics, medical history, daily life ability, and neuropsychiatric testing performance.MCI was diagnosed in accordance with the Petersen criteria, as demonstrated by Petersen et al.  and MarcCognitive function was assessed using the MMSE, the Montreal Cognitive Assessment (MoCA), and the CDR scales, and daily functioning was measured using the Activity of Daily Living scale. MMSE is one of the most widely used questionnaires used in the aging population, with high acceptability by professionals and researchers as a diagnostic instrument. This scale features 30 items relating to the assessment of orientation, recall, attention, calculation, language, and visuospatial abilities of the participants. A score on the MMSE scale of <24 was used to identify patients with dementia. The MoCA scale covers extensive and comprehensive cognitive domains and elevates the scores of the visuospatial and executive domains. The total score on this scale is 30 points, and the higher the score, the better the cognitive ability. When the number of years of education was ≤12, one more point was added to the original total score, and the final score of <26 indicated cognitive impairment and of 21 indicated dementia  containing 40 or 16 odors was developed by Zhou's team in 2019 . The detailed odor items were reported and the reliability of CSIT was validated against UPSIT and Sniffin' Sticks test in China  curves were plotted to detect the possible non-linear dependency between the risk of MCI or AD and the CSIT score using three knots at specified locations according to the percentiles of the distribution of CSTI scores. Receiver operator characteristic curves (ROCs) were generated. The area under the curve (AUC) was calculated by taking the clinical diagnosis of MCI or AD as the outcome index and the CSIT score as a predictor. The maximum Youden's index  based on a two-sample t-test of the means and SD of CIST scores and the sample size in the AD and MCI groups, with regard to the smaller AD sample size than the other two groups. The significance level was set at 0.05 (two-sided). The estimated power of this study was 0.894. When we input the CIST score and sample size in the NC and MCI groups, the estimated power reached 0.982.All data were processed using R software version 4.1.2  and SPSS version 26.0 . Numerical data were expressed as frequency and percentage (%). The difference between the three groups was compared using the chi-squared test or Fisher's exact test, with multiple comparisons between every two groups adjusted using Bonferroni's test. Quantitative data were presented as means ± standard deviation (SD) or medians with quartiles. Statistical differences among multiple groups were compared using the one-way analysis of variance (ANOVA), followed by Bonferroni's F statistics = 11.839, P = 0.003), with a higher mean age in patients with MCI and patients with AD when compared with the NCs; the age was even higher in patients with AD when compared with patients with MCI. A significant difference was also detected for educational levels among the three groups ; the proportions of patients with <9 educational years were 28, 49.5, and 71.4% in the NC, MCI, and AD groups, respectively, thus indicating that age and educational levels were two potential confounders affecting the cognitive performance of the population aged over 50 years. No significant differences were detected between the three groups with respect to other sociological features such as marital and living status, physical exercise frequency, smoking, and alcohol consumption status. The three groups of participants showed a comparable incidence of concurrent diseases, including hypertension, diabetes, coronary artery disease, and hyperlipidemia. Approximately half of the enrolled participants reported sleep difficulties, and ~90% reported subjective memory decline; there were no significant differences between the groups in this respect. As expected, the mean MMSE and MoCA scores gradually decreased as the severity of cognitive impairment increased. The participants in the AD and MCI groups had significantly lower scores than those in the NC group (both P < 0.001).A total of 366 participants completed all tests and were eligible for this cross-sectional study: 188 patients with MCI, 42 patients with AD, and 136 cognitively normal controls. The demographic characteristics and cognitive scores of the participants are compared in Dimocarpus longan Lour, a subtropical and tropical evergreen tree belonging to the Sapindaceae family that is widely distributed in China, Southeast Asia, and South Asia , except for the odors of orange, almond, and fried fish. When comparing the NC and MCI groups, we observed a significant difference in the identification rate for eight odors , especially for chocolate, coffee, apple, and lemon . Compared with the MCI group, patients with AD exhibited a reduced ability to distinguish the odors of garlic, floral water, sesame oil, and longan . Compared with NC, patients with AD had poor performance in distinguishing 11 odors. Patients with MCI achieved a mean CSIT examination score of 13.06 ± 2.053, while those in the AD group achieved a mean score of 11.38 ± 3.253; these scores were significantly lower than the NC group , respectively. For participants in the NC group, CSIT significantly correlated with MoCA  rather than MMSE . In the MCI group, the correlation coefficients between CSIT and MOCA/MMSE scores were 0.459 (P < 0.001) and 0.346 (P < 0.001). In the AD group, the coefficients between CSIT and MOCA/MMSE were 0.468 (P < 0.001) and 0.431 (P < 0.001).Based on the observed differences in olfactory functions across the three groups, we evaluated the correlation between the CSIT and cognitive scores. Scatter plots were used to illustrate the CSIT scores against MMSE or MoCA scores for everyone in the whole population , B. SpeaP-value were shown. In the adjusted models, age, gender, educational levels, and concurrent diseases were further adjusted as confounders in stepwise logistic regression. As shown in P < 0.001); furthermore, the OR increased to 0.81  after adjusting for the confounders of age, gender, and educational levels but remained unchanged when we adjusted for concurrent diseases in addition to the covariates in Model 2, thus indicating that the higher the CSIT score, the lower the probability of MCI. When modeling olfactory impairment level as an ordinal categorical variable, each stepwise elevation of olfactory rank increased the risk of MCI by 1.83-fold (P = 0.008); this significant association remained robust after adjusting for age, gender, educational levels, and other covariates. Similarly, a significant association between olfactory performance and cognitive function was also observed when comparing MCI with AD and AD with NC, regardless of whether we modeled the olfactory function as a continuous or rank predictor. This association remained significant after subtracting the effects of age, gender, and educational levels (Model 2) in addition to other covariates (Model 3). A restricted spline curve (RSC) was generated to illustrate the potential non-linear association between the CSIT score and the OR of MCI when compared with NC after adjusting for age and educational levels (Pnon − linear = 0.17) and the risk of MCI increased as the CSIT score decreased, especially when the CSIT score was < 10 points. In the adjusted RSC, when differentiating AD from MCI, a decreasing trend line showed that a CSIT score of < 13 points led to an increase in the risk of AD  as dependent variables. In the crucial logistic model, only the CSIT score or olfactory impairment level was fitted, and the odds ratio (OR) with a 95% CI and a l levels . The cursk of AD . The RSCsk of AD .education level × CSIT in the regression formula. The coefficients of the constructed variable were 0.99  when classifying MCI from NC and 1.02  while classifying MCI from AD, thus indicating that there was no significant effect of educational level on CSIT when predicting MCI. However, the educational level had an interactive effect on CSIT in predicting AD.Later, we performed a subgroup analysis to further investigate the effects of age and education on the relationships between CSIT and the risk of MCI or AD. The participants were stratified into three intervals of age and four subgroups based on their educational background; a cross-table listing the CSIT scores of patients in the NC, MCI, and AD groups is given in Finally, we used the ROC analysis to assess the diagnostic value of the CSIT score for classifying MCI or AD. The analysis showed that the AUC for the CSIT score when classifying MCI from NC was 0.738, with a sensitivity of 0.521 and a specificity of 0.801, with an optimal cutoff score of 13; the AUC increased to 0.751 when combined with age and educational level . A CSIT In this cross-sectional study, we used a local 16-odor CSIT to assess olfactory identification ability in an aging Chinese population to assist in the early diagnosis of MCI and AD. Our results demonstrated that olfactory identification impairment occurred in 19.9% of the cognitively NCs, 52.7% of patients with MCI, and 69% of patients with AD. After adjusting for associated confounders, a lower CSIT score and higher olfactory impairment severity were identified as independent predictors of MCI or AD. The CSIT score was more effective in distinguishing patients with MCI and patients with AD from people with normal aging than in distinguishing patients with AD from patients with MCI. Our research also validated the use of the Chinese version of the CSIT in identifying cognitive decline among older Chinese adults with cognitive complaints. This test may serve as a new tool for the early detection of cognitive decline in clinical settings.Olfactory function measurement involves multiple domains, including olfactory detection, intensity, familiarity, hedonics, discrimination, identification, and passive smelling / as we did not perform a special memory test for assessing the performance and capacity of the memory domain, such as the Wechsler Memory Scale—Fourth Edition, the Clinical Memory Scale, or the Fuld Object Memory Test. Meanwhile, the MoCA and MMSE scales performed in this study are not sufficient to split MCI into naMCI or aMCI, although these scales contain memory domain assessment. The discriminative efficiency of CSIT for aMCI should be evaluated in further studies. Finally, careful consideration of genetic background, pathological changes, or clinical covariates will enhance our understanding of the mechanism of olfactory dysfunction during the progression of AD.Our study demonstrated that patients with MCI and patients with AD exhibited a higher incidence of olfactory identification deficits and lower CSIT scores than individuals with normal aging. A lower CSIT score and higher olfactory dysfunction severity independently predicted the risk of MCI or AD even after adjusting for a wide array of confounders, such as age, gender, education, and comorbidity. Approximately 75% of the odors provided in the CSIT had a correct identification rate of over 90% in normal elderly adults. Patients with MCI and AD showed an accelerated decline in olfactory function. Our study found that the CSIT test is effective in distinguishing between patients with MCI and patients with AD from the general aging population. We recommend the use of the CSIT test in diagnosing AD in China due to its high acceptance, adaptability, novelty, and reliability.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Affiliated Hospital of Northwest University . The patients/participants provided their written informed consent to participate in this study.YT, GZ, and WS conceived and revised the manuscript. XM and YM wrote the manuscript. YM, SD, CD, XL, and HT performed the experiment and data collection. XM, JZ, and QZ participated in the statistical analysis of the data. All authors read and approved the final version of the manuscript.


Major depressive disorder (MDD) is a common cause of disability and morbidity, affecting about 10% of the population worldwide. Subclinical depression (SD) can be understood as a precursor of MDD, and therefore provides an MDD risk indicator. The pathogenesis of MDD and SD in humans is still unclear, and the current diagnosis lacks accurate biomarkers and gold standards.A total of 40 MDD, 34 SD, and 40 healthy control (HC) participants matched by age, gender, and education were included in this study. Resting-state functional magnetic resonance images (rs-fMRI) were used to analyze the functional connectivity (FC) of the posterior parietal thalamus (PPtha), which includes the lateral habenula, as the region of interest. Analysis of variance with the post hoc t-test test was performed to find significant differences in FC and clarify the variations in FC among the HC, SD, and MDD groups.Increased FC was observed between PPtha and the left inferior temporal gyrus (ITG) for MDD versus SD, and between PPtha and the right ITG for SD versus HC. Conversely, decreased FC was observed between PPtha and the right middle temporal gyrus (MTG) for MDD versus SD and MDD versus HC. The FC between PPtha and the middle frontal gyrus (MFG) in SD was higher than that in MDD and HC. Compared with the HC group, the FC of PPtha-ITG (left and right) increased in both the SD and MDD groups, PPtha-MTG (right) decreased in both the SD and MDD groups and PPtha-MFG (right) increased in the SD group and decreased in the MDD group.Through analysis of FC measured by rs-fMRI, the altered FC between PPtha and several brain regions  has been identified in participants with SD and MDD. Different alterations in FC between PPtha and these regions were identified for patients with depression. These findings might provide insights into the potential pathophysiological mechanisms of SD and MDD, especially related to PPtha and the lateral habenula. Major depressive disorder (MDD) is characterized by marked and persistent depression and is chronic, recurrent, and difficult to cure. Additionally, MDD is associated with a high suicide rate. These characteristics produce a severe adverse effect on the quality of life of patients with MDD –5.Subclinical depression (SD) is regarded as an early stage indicator or precursor of MDD –8 becausResting-state functional magnetic resonance imaging (rs-fMRI) examines the temporal correlation of spontaneous fluctuations in blood oxygen level dependent (BOLD) signals across brain regions , 13. FunStudies have shown that the lateral habenula (LHb) has an important relationship with the pathogenesis of depression , 19. TheThe LHb has become an anatomical target for testing antidepressants such as ketamine in animals and humans , 32–35. In this study, we compare the FC connected with LHb among MDD, SD and HC groups, and aim to clarify the FC alterations in HC, SD, and MDD. This study includes the collection of rs-fMRI data of three groups, extraction of the PPtha as the region of interest (ROI), calculation of the FC between PPtha and all other regions in the whole brain, and statistical analysis of altered FC.All participants in this study were college students from Guangzhou Medical University. A total of 40 MDD , 34 SD  and 40 HC  subjects were screened from about 1000 college students over the period 2018–2019.Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) , and precuneus is lower in the MDD group. Detailed results are shown in Table The FC between the PPtha and right ITG , MFG, and postcentral gyrus is higher in the SD group than that in the HC group. Conversely, the FC between the PPtha and left superior parietal gyrus  is lower, as shown in Table By comparing the FC alterations in MDD vs. SD, MDD vs. HC and SD vs. HC, the potential tendency for depression have been identified. Specifically, increased FC was found in ITG in the comparison of MDD vs. SD (left side) and SD vs. HC (right side). Conversely, decreased FC of the MDD group was observed between the PPtha and right MTG in the comparison of MDD vs. SD and MDD vs. HC. Additionally, the FC between the PPtha and right MFG was higher in the SD group than in both the MDD and HC groups. The locations of the four identified regions,  are shown in Fig. p < 0.005 and cluster p < 0.05; (2) voxel p < 0.001 and cluster p < 0.05. As shown in Table p < 0.001 and cluster p < 0.05, potentially suggesting that the correct standard was too strict.It should be noted that GRF correct method was used with two criteria: (1) voxel To the best of our knowledge, this is the first study comparing the FC of PPtha among patients with MDD, SD, and HC. Altered FC between PPtha and several regions was identified in all three pairwise group comparisons. The regions included the ITG, MTG, and MFG. The FC alteration tendency from HC to SD and MDD was identified by comparing the three groups. These findings provide a new perspective and thinking for exploring the pathophysiological mechanism of depression. The significance of these findings is discussed in the following subsections.SD has been considered the precursor to MDD. The cluster discharge of the LHb in rats is sufficient for the occurrence of depression , 29, 31.3 per hemisphere based on structural MRI and postmortem measurements [3 per hemisphere, which can be smaller than the voxel size of standard fMRI [3. In this case, the conventional fMRI voxel size is larger than the LHb. Finally, the smoothing kernels with 5–12 mm FWHM are larger than the LHb. The habenula signal is likely to be contaminated by adjacent structures, such as the medial dorsal thalamus or the epithalamic paraventricular nucleus [The PPtha in the Brainnetome Atlas was used as the ROI, instead of LHb, because the precise location of the LHb is difficult to determine. First, the small volume of the LHb was a limitation in some studies. The habenula volume in vivo is estimated to be closer to 18.5 mmurements , 60. Thiard fMRI , 59, 61. nucleus .All three pairwise group comparisons showed abnormal FC between the PPtha and the temporal lobe. Specifically, the FC between the PPtha and ITG in the MDD group was higher than that in the SD group. FC in the SD group was higher than that in the HC group. It is speculated that the FC between the PPtha and ITG is gradually enhanced during the progression from HC to MDD.The FC between the PPtha and right MTG was lower in the MDD group than that in the SD and HC groups. It is speculated that the FC between the PPtha and right MTG gradually decreases as depression progresses. One study reported a decreased connectivity between the prefrontal thalamus and the MTG in MDD patients , which mThe ITG and the MTG are crucial regions in the temporal cortex. Some research has reported that thalamo-temporal connectivity changes significantly in MDD patients . MoreoveIn the MDD and SD groups, and the SD and HC groups, the FC between the PPtha and frontal lobes showed an abnormal value. The FC between the PPtha and right MFG in the MDD group was lower than that in the SD group. Moreover, the FC in the SD group was higher than that in the HC group. The FC between the hippocampus and MFG was impaired in MDD patients , 67. MorNotably, the MFG is a crucial part of the dorsolateral prefrontal cortex (DLPFC), which has been tightly linked to depression . In deprThe mechanism underlying the higher PPtha-MFG connection in the SD group compared with both the MDD and HC groups is unknown. Here, we speculate that the connection between the PPtha and MFG may be severely changed in MDD patients. Another possibility is that the MFG or the PPtha may have been impaired in the post-depression stage.There are several limitations in this study. First, although this study included three groups of rs-fMRI data, the sample size is largely small. This factor limited the statistical significance. Second, we didn’t extract the accurate LHb as the ROI but used the PPtha region from the Brainnetome Atlas. This change may affect our findings. This study investigated the FC between the PPtha and ITG, MTG, and MFG for MDD and SD. The validity of our findings requires verification through further studies.In future work, the sample size will be expanded. Subjects from the WU-Minn Human Connectome Project (HCP) Consortium’s 500 Subjects Release can be included , 71. FurThrough analyzing the FC measured by using rs-fMRI data, this study explored the altered FC between PPtha and other brain regions in SD and MDD subjects. The FC between the PPtha and ITG, right MTG, and right MFG showed abnormality in both MDD and SD subjects. Different alterations for FC between PPtha and these brain regions were present for HC, SD, and MDD: increased PPtha-ITG (left and right) FC; decreased PPtha-MTG (right) FC; initially increased and subsequently decreased PPtha-MFG (right). These findings might provide insight into potential pathophysiologic mechanisms of SD and MDD, especially related to the lateral habenula.


Colposcopy-guided biopsy with pathological diagnosis is the gold standard in diagnosing CIN and invasive cervical cancer. However, it struggles with its low sensitivity in differentiating cancer/HSIL from LSIL/normal, particularly in areas with a lack of skilled colposcopists and access to adequate medical resources.the model used the auto-segmented colposcopic images to extract color and texture features using the T-test method. It then augmented minority data using the SMOTE method to balance the skewed class distribution. Finally, it used an RBF-SVM to generate a preliminary output. The results, integrating the TCT, HPV tests, and age, were combined into a naïve Bayes classifier for cervical lesion diagnosis.the multimodal machine learning model achieved physician-level performance , and it could be interpreted by feature extraction and visualization. With the aid of the model, colposcopists improved the sensitivity from 53.7% to 70.7% with an acceptable specificity of 81.1% and accuracy of 79.6%.using a computer-aided diagnosis system, physicians could identify cancer/HSIL with greater sensitivity, which guided biopsy to take timely treatment. With 570,000 new cases and 311,000 cases of death in 2018, cervical cancer accounts for the highest morbidity of gynecologic malignancies in women worldwide . In clinThe standard screening methods for cervical cancer include ThinPrep cytologic test (TCT), human papillomavirus (HPV) tests, and colposcopy . TCTs arComputer-aided medical diagnosis can successfully complete a variety of medical tasks by efficiently exploring the essence of a large amount of clinical data. The colposcopy-guided cervical biopsy is essential for detecting CIN in cervical cancer screening, but there are difficulties with increasing sensitivity globally. Pilot studies used the k-nearest neighbor (K-NN) algorithm  and the These aforementioned algorithms shared a common drawback. They were not resistant to noises using just VIA images. The result was lack of diagnostic confidence due to the imaging quality and normal epithelium shades. Compared to VIA alone, co-testing with VILI appeared to boost performance . A neuraIn this work, we collected VIA and VILI images and clinical information from each of the 1361 patients, and the gold standard of pathologic diagnosis was used as the ground truth. The model used an SVM with radial basis function kernel (RBF-SVM) to generate a preliminary output after extracting color and texture features from the cervical regions that were automatically segmented. The output of the first stage was passed on to the second stage input, which combined the TCT, HPV test, and age, to build a naïve Bayes algorithm for cervical lesion diagnosis. This model’s performance was compared to that of colposcopists and other machine learning models. The visualized interpretable features help with biopsy by identifying potential lesion sites.We gathered clinical data, TCTs, HPV tests, and cervical images of colposcopy (TR6000C) from 1,361 patients (ages ranged from 16 to 83) at the First Affiliated Hospital of Wenzhou Medical University in China during the period from 1 August to 31 November 2020 for this study. Each patient signed a consent authorizing colposcopy with biopsy. The colposcopy examination was carried out by nine physicians with specialized knowledge, including one chef physician, four attending physicians, and four resident physicians. Physicians made a diagnosis for each patient based on the cervigrams. The pathological result of cervical biopsy served as the ground truth to identify the degree of cervical lesions.Among 1,361 patients, we eliminated 229 patients with blurred images or unclear exposed cervix portion , 71 patients with a history of hysterectomy or cervical operation , or cold knife conization (CKC)), and 75 patients with information loss . After filtering the images, the study included a total of 986 patients and 1,972 cervical images (each patient had one VIA image and one VILI image). These patients were categorized into normal , LSIL , HSIL , and cancer  according to pathological results. The training set consisted of 701 patients from the first 3 months, and the test set consisted of the additional 285 patients from the fourth month.HPV tests used in this study were HPV DNA Test (Tellgenplex HPV 27 Genotyping Assay). The results of HPV tests were divided into positive  and negative 101, 10.2%). HPV positive was subdivided into three classes 01, 10.2% low-riskWe defined the region of interest with a minimum rectangle in the vaginal wall around the cervix and conducted a further investigation because vaginal sidewalls and speculums affected the identification of cervical lesions. We used the transfer learning technique to pretrain the weights of five different deep learning architectures . Four neurons representing width, height, and the coordinates of the left-bottom endpoint were used in place of the classification head. Physicians annotated 200 original VIA and 200 original VILI images with bounding boxes around the cervix to serve as the ground-truth labels, and they were divided into training, validation, and test sets with a 120/30/50 split, respectively.In order to assess the performance of the five deep learning models, we calculated the mismatch loss of the 50 images between the predicted regions and the ground truth in the testing stage. According to the mean and standard deviation of loss, the four models highlighted in bold in All VIA and VILI images were segmented using the two well-trained deep learning models. Physicians then double-checked them and made any necessary corrections to those with a significant deviation (more than about 10%). In fact, only 4.45% VIA and 2.50% VILI images were altered by physicians, confirming the models’ validity. Pixels were used to measure the width and height of each image. The cropped images have an average size of (905 ± 66) × (866 ± 101) for the original images .The lesion localization is determined mainly according to the VIA and VILI images. A crucial diagnostic tool for cervical dysplasia is color features. In order to fully characterize the color features of an image, a color space is used to represent color that can be reproduced on an image. The most popular color space is RGB, but it completely ignores the environment’s lighting and the camera’s sensitivity, taking the pixels in a digital image at face value. With its foundation in hue, saturation, and lightness, the hue–saturation–value (HSV) space is more perceptually relevant and intuitive. A grayscale is a color space that displays an RGB image’s luminance data. A fairly logical way to arrange colors is from black to white (luminance: Ll),green to red (La), and blue to yellow (Lb) (CIELAB). In this space, colors that are separated by the same amount appear to have approximately equal differences. Luminance, Chroma blue, and Chroma red (YCbCr) occupy a different space, where Luminance (the Y component) denotes the color’s brightness and Cb and Cr denote the blue and red components, respectively, in relation to the green component .We calculated the statistical dispersion with the standard deviation and identified central tendency with the mean, median, and mode in order to investigate the statistical characteristics of each color channel. To automatically determine a threshold value and reduce the weighted within-class variance, we also used the Otsu thresholding algorithm . To descAn image’s texture is defined as the regular distribution of patterns and colors. When acetic acid is applied to a cervix with lesions, different abnormal epithelium textures, such as textured mosaicism, punctation, and vascular structures, are frequently visible . HaralicThe pixel pair distance offsets of 1, 5, 10, and 15 pixels were used to compute the GLCMs before arriving at the four features within each offset. The GLCM’s default adjacency calculation was done from left to right (0 in degree), but the texture features actually tended to be dispersed throughout all directions. Additionally, we took note of the other three pixel-pair directions  and averaged them to determine the features. Six Haralick features—angular second moment (ASM), contrast, correlation, dissimilarity, energy, and homogeneity—were extracted from these GLCMs in each pixel offset, resulting in 24 texture features for each image. The characteristics were formulated mathematically:Contrast:Dissimilarity:Homogeneity:P is the GLCM value, and P indicates the number of times that gray-level j occurs at a distance of d an angle of θ from gray-level i.Sensitivity measures the percentage of cancer/HSIL (H-group) patients who are correctly identified, and specificity is the extent to which LSIL/normal (LN-group) ones are correctly identified as such. Accuracy calculates the correct prediction percentage all over samples. In binary, unbalanced classification tasks, the area under the precision-recall curve (AUPRC) is frequently used. The macro-averaged F1 score, which can be calculated as follows, is the arithmetic mean of the per-class F1 scores and balances sensitivity and specificity:iSen and iSpec are the sensitivity and specificity for the thi class.where The macro-averaged F1 score equally weighs each class’s sensitivity and specificity. It is an appropriate metric for thoroughly assessing model performance. Sensitivity is set as one of the key performance metrics because it is of the utmost clinical importance to identify patients with severe cervical lesions.A single VIA or VILI image yielded 24 Haralick texture features and 65 color features. Due to redundancy and a greater emphasis on noise in machine learning algorithms, an excessive number of features resulted in a failed classification, though. To identify features that significantly differed between the means of the two classes, a T-test was used. We used the T-test to analyze the features in 701 VIA training images, and then we sorted them according to their adjusted P-values. Similar to that, VILI image features were also sorted. We used various combinations of the top-ranked features to feed the model, and we used the entire training process described in subsection 2.7. To assess all performance metrics and choose the best VIA and VILI feature combination, the macro-averaged F1 score was used.It was common that H cases (cancer/HSIL) were much fewer than LN ones , and in this work, H cases accounted for 137 out of 986. The machine learning algorithm would have a tendency to predict the majority in a skewed class distribution. Undersampling reduces the majority samples and achieves a balanced class proportion, wasting valuable medical data in the process. By duplicating minority data, the oversampling technique increases the sample size of the minority class . Despiteγ∥ x−x′ ∥2) where γ was 0.72. The other parameter, C, was set to 0.12 as a compromise between the training samples’ misclassification and the simplicity of the decision surface.Using a support vector machine (SVM) classifier, which maps data onto points in a high-dimensional space and finds an ideal hyperplane to divide data into classes, we increased the H cases to achieve a similar proportion of LN ones. Using the balanced augmented data, we trained an SVM with the radial basis function (RBF) kernel. The kernel was expressed as follows: exp, and 1D-convolutional neural network (CNN). RBF-SVM algorithms were also performed with three input configurations—VIA images only, VILI images only, and VIA and VILI images. Additionally, the SMOTE algorithm could be integrated in RBF-SVMs to balance the skewed class distribution.All of the machine learning models mentioned above were optimized. The Gini impurity was used by the random forest classifier as the splitting criterion, and 100 decision trees were used to generate the out-of-bag predictions. Neural networks used the Adam optimization algorithm and the three-layer 10–25–2 configuration with tanh activations. The predictive threshold was indicated by the number in parenthesis after NN. A 1D-convolution layer with 64 1 × 8-sized feature maps, a max-pooling layer, and two fully connected layers made up the 1D-CNN.Modern CNN architectures like ResNet-50 and VGG-16 were frequently utilized to carry out image classification tasks. We used transfer learning to train the classification head of the ResNet-50/VGG-16 models for VIA/VILI images to identify H-group patients because these two models showed excellent performance in identifying the cervix. For further diagnosis, we also fed the naïve Bayes classifier the output and clinical data. We implemented five models with the best parameter configurations for each of the 15 algorithms listed above, and we averaged the results in Eighty-eight (8.9%), 558 (56.6%), and 340 (34.5%) out of the 986 patients had diagnoses from the chef physicians, attending physicians, and resident physicians, respectively. Their performance and the number of diagnosed cases are listed in According to In this study, we presented an algorithm for diagnosing cervical dysplasia using VIA and VILI images and clinical data of age, TCT, and HPV tests. The multimodal machine learning algorithm extracted the color and texture features to implement a SMOTE-based RBF-SVM model. Combining the clinical information, the algorithm achieved a sensitivity, specificity, and accuracy of 51.2%, 86.9%, and 81.8%, respectively, and physicians’ sensitivity, specificity, and accuracy were on average 53.7%, 89.8%, and 84.6%, respectively. The performance of the algorithm was comparable to physicians. In recent high-quality studies, the sensitivity and specificity to detect HSIL varied greatly, ranging between 33%–93% and 53%–95%, respectively –40, whicWe combined the cervical images of VIA and VILI to enhance the ability to recognize cervical lesions in comparison to earlier studies on cervical image analysis , 27, 28.The skewed class distribution made training and testing difficult, which always made the machine learning algorithms like the aforementioned random forest, neural network, and SVM to predict the majority of outcomes. Traditional oversampling techniques only replicated the minority data, which prevented the machine learning model from receiving any new data or variation. The K-NN algorithm was used by the SMOTE strategy to generate artificial data from the minority and balance the classes. Thus, as shown in This study has several limitations, including the following: first, because the cervical canal is not visible in cervical images, the algorithm is unable to accurately identify lesions in patients with lesions in the canal; second, the imbalance of data distribution (H:LN ≈ 1:7) affected the sensitivity to some extent; third, the estimated probability in the naïve Bayes classifier is inaccurate to some extent because of the naïve assumption; and fourth, the system is evaluated on the data from one single hospital, since the colposcope’s illumination characterization and imaging specifications vary with each individual colposcope equipment or environment.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The research protocol and colposcopic images, clinical information of patients used in this study were reviewed and approved by the Ethics Committee of First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China[2021-zz-155]. The Ethics Committee also granted a waiver for informed consent of images and clinical information,as no biological sample was involved, no privacy of patients was included and it was a retrospective observational study.All authors contributed to the concept, design, and drafting of the study. J-HM designed the survey and wrote the manuscript. S-FY and ZF designed the model and analyzed the data. Y-YC collected the data. J-SX and X-LL critically reviewed the data and annotated images. YH Guided the research. YH and ZF critically reviewed the data, manuscript, and gave final approval. All authors contributed to the article and approved the submitted version.We thank Dr. Huan Hu in XMU for helpful discussions. We also thank the cervical diagnosis and treatment group, Department of Gynecology, The First Affiliated Hospital, for their statistical assistance, including Dr. Z-Z Shi, Dr. B-Y Huang, Dr. QZ, Dr. Y-L Wang, Dr L-L Chen, Dr R-Y Zheng, and Dr. Q-Y Zhu.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Food strategies are currently used to improve inflammation and oxidative stress conditions in chronic pain which contributes to a better quality of life for patients. The main purpose of this systematic review is to analyze the effectiveness of different dietary strategies as part of the treatment plan for patients suffering from chronic pain and decreased health. PubMed, Web of Science, ProQuest, Scopus, Cumulative Index to Nursing & Allied Health Literature (CINAHL), Cambridge Core, and Oxford Academy databases were used to review and to appraise the literature. Randomized clinical trials (RCT), observational studies, and systematic reviews published within the last 6 years were included. The Physiotherapy Evidence Database (PEDro) scale, the PEDro Internal Validity (PVI), the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a variety of fields (QUALSYT), and the Quality Assessment Tool of Systematic Reviews scale were used to evaluate the risk of bias of the included studies. A total of 16 articles were included, of which 11 were RCTs and 5 were observational studies. Six of them showed an improvement in pain assessment, while two studies showed the opposite. Inflammation was shown to be decreased in four studies, while one did not show a decrease. The quality of life was shown to have improved in five studies. All of the selected studies obtained good methodological quality in their assessment scales. In the PVI, one RCT showed good internal validity, five RCTs showed moderate internal quality, while five of them were limited. Current research shows that consensus on the effects of an IF diet on pain improvement, in either the short or the long term, is lacking. A caloric restriction diet may be a good long term treatment option for people suffering from pain. Time restricted food and ketogenic diets may improve the quality of life in chronic conditions. However, more studies analyzing the effects of different nutritional strategies, not only in isolation but in combination with other therapies in the short and the long term, are needed. Chronic pain has an enormous impact on a personal and socioeconomic level . Its natChronic musculoskeletal pain is defined as pain that recurs for more than three months, has a significant impact on functionality, and causes emotional stress . This tyA prolonged neuroinflammation, which is a failure in the response of the immune system when helping to promote homeostasis, may result in chronic pain . When thRecent research confirms that nutritional interventions mainly in the dietary pattern, the intake of specific nutrients, and the number of meals can provide significant relief for patients with chronic pain. This happens through a great diversity of molecular systems that support both neural function and plasticity, and they produce effects similar to antidepressants . These iOne of the most widely used diets for weight loss is caloric restriction, which reduces the daily calorie intake without causing malnutrition. It is a nutritional strategy, which has been shown to play a crucial role in people’s longevity and health . In the Time restricted feeding is the regulation of food intake over a period of time. It consists of maintaining a daily meal routine, with a certain number of hours designated to fasting and others to eating . This stThe ketogenic diet (KD), which is a proven strategy for epilepsy, is characterized by maintaining the carbohydrate intake to below 50 g per day or no more than 10% of the total energy ingested ,22. ImprThe Mediterranean diet (MD)is characterized by the frequent consumption of olive oil, fresh or dried fruit and vegetables, a moderate amount of fish, unrefined cereals, dairy and meat, and many condiments and spices, which are all accompanied by wine or its infusions; and, therefore, it is rich in nutrients and bioactive compounds with anti-inflammatory effects . Many diFurthermore, other factors have to be taken into account such as the microbiota–brain axis, which is the relationship between the microbiota and the CNS. The existence of intestinal inflammation produces an inflammatory response which may reach the CNS and lead to chronic pain and its intensification . On the An essential part of attaining musculoskeletal well-being is the nutritional and dietary behavior of the patient in order to avoid the alteration or perpetuation of chronic musculoskeletal pain conditions. Therefore, research on the nutritional strategies used and which strategies are considered to be useful for the improvement of health and quality of life in terms of chronic musculoskeletal pain and inflammation is of great importance.Thus, the main goal of the study was to review current research focused on the efficacy of intermittent fasting, time restricted feeding, caloric restriction, and ketogenic and Mediterranean diets when applied to improve both health and chronic musculoskeletal pain. Furthermore, we assessed the methodological quality of the studies analyzed.The present study is a systematic review of the literature on randomized clinical trials, observational studies, and systematic reviews. It was carried out following the criteria defined in the PRISMA declaration for the preparation and the development of systematic reviews and meta-analyses .Seven electronic databases were used for the bibliographic search from inception to March 2022 by two independent investigators (M.C.-C. and J.J.P.-M.): PubMed, Web of Science, ProQuest, Scopus, CINAHL, Cambridge Core, and Oxford Academy.The research question followed the description of the components of the PICO strategy .A sensitive search strategy using relevant search terms that were developed from Medical Subject Headings (MeSH) and Descriptors in Health Sciences dictionaries (DeCS) was used, as shown in The PECOS framework, as aforementioned, was followed to determine which studies were included in the present systematic review. Each study had to meet the following inclusion criteria:(i) published from January 2015 to March 2022, (ii) published in the English language and internationally peer-reviewed, (iii) classified as RCTs, observational studies, (iv) full text available, (v) adults over 18 years of age, and (vi) participants had to be suffering from chronic musculoskeletal pain for at least 3 months. The exclusion criteria were studies that (i) did not evaluate chronic pain, (ii) did not apply food strategies in people with chronic pain as treatment, and (iii) did not show results or interpretation of their data.All studies identified by the search strategy were screened using the eligibility criteria that were previously specified. The first stage of assessment involved the screening of titles and abstracts by two reviewers (M.C.-C. and J.J.P.-M.). The same reviewers undertook the second stage, screening the full text. In cases of disagreement, a decision was made by consensus or, when necessary, a third reviewer (S.N.-L.) was consulted.Two independent reviewers (M.C.-C. and J.J.P.-M.) who were blinded to each other extracted the following relevant data from each study: study details , characteristics of participants, setting, pain condition, SE measuring instrument, outcome measures, duration of follow-up, and study design. If there was any discrepancy between reviewers, a third reviewer was consulted (S.N.-L.).The scope of the search included all aspects of research within the field of nutrition, such as macronutrients, micronutrients, foods, food groups, dietary supplements, diet analysis, diet patterns, applied diets, dietary behavior. However, this approach resulted in 24,500 eligible studies with evidence of a link between nutrition  and chronic pain.In order to increase the quality of the study, an extra search was carried out which included different etiologies of when non-musculoskeletal chronic pain appeared. Additionally, we also attempted to consider individuals’ overall dietary intake and eating habits. This provided a quantity of more than 13,000 articles. However, a review that includes so many articles with a broad nutritional focus would decrease readability and understandability. Therefore, it was decided to narrow down, and focus the review on chronic musculoskeletal pain. However, the scientific evidence on chronic musculoskeletal pain from other pathologies was reflected, which, even narrowing the search criteria, continued to appear, and we understood that it was important to add it given the similar clinical characteristics.The methodological quality of the RCTs was performed using the PEDro scale translated and adapted into Spanish. This scale allows the validity of the articles to be evaluated.The PEDro scale examines, through 11 items, the external and the internal validity of a study. For each completed item, with the exception of the first, the scale adds 1 point to the total score; therefore, the global score of the PEDro scale has a range of 0 to 10 points . The evaThe methodological quality of the observational studies was performed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies scale.This scale is made up of 14 items that allow us to examine and to determine the internal quality of an observational study. Each item, if it is fulfilled, adds a point to the global score, which ranges from 0 to14 points; where 0 means poor methodological quality and 14 good .The quality of the study depends on the score acquired. The evaluation of the methodology is determined as “high”, “average”, and “low” .It is understood that naming a study as “good” quality means it tends to have a lower risk of bias and its results are treated as valid. When rated as “fair”, the possibility of bias is relative, and the results are insufficiently valid. Finally, a methodology classified as “bad” is understood as one that presents a significant risk of bias and poor validity .The results found in the different selected studies are detailed below.The searches performed in the seven different databases yielded: PubMed studies, Web of Science studies, ProQuest studies, Scopus studies, CINAHL studies, Cambridge Core studies, and Oxford Academy studies.All those that did not meet the aforementioned inclusion criteria were eliminated.Finally, a total of 16 studies were selected, including 11 RCTs and 5 observational studies. The study selection process is shown in A methodological quality assessment was carried out using the different scales with the results being shown in The intensity, quality, and frequency of pain in the patients who participated in the application of dietary strategies were not reduced in the studies by Baurersfeld SP. et al. .On the contrary, the studies carried out by, Holton KF. et al.  and Towep = 0.025) [A decrease in pain levels was observed via SF-36 and AIMS2-2F ). In add= 0.025) ,41,42.The concentration of slCAM-1, a soluble component of intercellular adhesion, which is a part of the inflammatory process, was studied and analyzed in different tissues and in structures in patients suffering from chronic pain and altered health. In the study by Stekovic S. et al. , they shOn the other hand, the studies by Liu B. et al.  and Vadep = 0.02) [p = 0.04) [p ≤ 0.001 in both cases) [The levels of inflammation seen in tissues increased after the ingestion of food ( = 0.02) , and it  = 0.04) .The conch cases) .The studies by Towery P. et al.  and Vadep = 0.0001), WOMAC Scores (p = 0.01) [p = 0.023), and General Questionnaires (p = 0.033) [A statistically significant association between diet and quality of life was found via the following questionnaires: a MED ( = 0.01) , Health = 0.033) ,40.-Visual Analogue Scale (VAS): a scale used to measure pain. It consists of a horizontal or a vertical line that represents the pain intensity with a score from 0 to 10, where the number 0 is synonymous with “no pain” and 10 is synonymous with “very intense pain/worst pain imaginable” .-SF-36: a tool for the evaluation of Health-Related Quality of Life (HRQoL). Scale applicable to any population. It contains 36 items that lead to the assessment of a positive and a negative quality of life. These questions are divided into eight scales in which physical function, physical role, bodily pain, general health, vitality, social function, emotional role, and mental health are questioned .-Functional Assessment of Cancer Therapy (FACT-G): a quality of life assessment scale. This questionnaire is made up of 27 items that collect, on four scales, the values of physical state, family and social environment, emotional state, and functional capacity. Each item has a value of 0 to 4 points .-Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F): a questionnaire related to health and quality of life associated with chronic pain illnesses. It consists of 27 general questions that encompass physical well-being, family/social well-being, emotional well-being, and functional well-being .-Functional Assessment of Chronic Illness Therapy-Trial Outcome Index (FACIT-TOI): a calculator for any illness, treatment or condition scale. An appraisal criterion that is normally used in clinical trials since it responds to physical and to functional changes .-Fibromyalgia Survey Questionnaire (FSQ): a questionnaire for the evaluation of symptoms in people suffering from fibromyalgia. It incorporates the WPI scale and the modification of the SSS .-Arthritis Impact Measurement Scales 2 (AIMS2-SF): a questionnaire that assesses physical, mental, and social conditions based on nine scales: mobility, physical activity, stroke, dexterity, household activities, pain, social activity, depression, and anxiety. Each scale is made up of four to seven items. The overall score ranges from 0 to 10 points .-Numerical Pain Scale (NPRS): a one-dimensional tool for measuring pain in adults, consisting of 11 items. It is a version of the VAS scale that has scores from 0 to 10, with 0 being synonymous with “no pain” and 10 being “the most intense pain imaginable” .-Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC): a specific questionnaire for the measurement and the evaluation of pain, stiffness, and functional capacity in knee and hip osteoarthritic disease. It is composed of three scales , which includes 24 items. The items are answered verbally using a scale which has five levels (0–4), ranging from little to a lot .-EuroQoL-5D (EQ-5D):an assessment of the standard of living in Europe. It consists of five questions about mobility, personal care, day-to-day activities, pain/discomfort, and anxiety/depression .-Health Assessment Questionnaire (HAQ): a questionnaire used to calculate the health of people with rheumatoid arthritis. It quantifies gastropathy, risk factors, and the risk of death in rheumatoid arthritis .-Patient Global Assessment (PGA): one of the most used assessments in rheumatoid arthritis. It is based on a question related to general health or disease activity that has a numerical answer from 0 to 10 or 0 to 100 .-Short-Form Health Outcome Survey (SF-12): a survey comprised of 12 questions that evaluates health in relation to quality of life .-Knee Injury and Osteoarthritis Outcome Score (KOOS): this separately evaluates pain (9 items), symptoms (7 items), ADL function (17 items), sport and recreation functioning (5 items), and quality of life (4 items). Each item can be scored on a scale of 0 to 4 .-Disease Activity Score on 28 joints (DAS28): a system to evaluate the activity of the disease and the treatment response in rheumatoid arthritis. The stipulated limit for an increase in treatment is 3.2 .-Simple Disease Activity Index (SDAI): a system to evaluate, at any time or place, the activity of rheumatoid arthritis. Doctors can choose the evaluation tool that best fits the environment they find themselves in .-Rheumatic Arthritis Impact of Disease (RAID): this evaluates pain, functional impairment, fatigue, sleep, and physical and emotional functioning in patients with rheumatoid arthritis. It is a validated tool that is highly trustworthy and sensitive to change .-Healthy Eating Index 2015 (HEI-2015): a measuring tool to evaluate whether certain food groups follow the Dietary Guidelines for Americans (DGA). Each time a new DGA is published, the HEI is updated. In comparison to the 2010 update, the 2015 version included specific recommendations on the limitation of added sugars and saturated fats .Intermittent fasting in healthy individuals ,32 over Fasting significantly reduced the levels of plasma insulin but left them unchanged if anti-oxidant supplementation was consumed .A decreaIn the short term (less than 12 weeks), intermittent fasting appears to improve quality of life and fatigue in healthy subjects  and in pA reduction in total weight was observed in subjects who had a decrease in lean muscle mass, although overall there was no significant change in the weight of subjects during intermittent fasting. Those who presented a decrease in lean muscle mass led to a reduction in total weight, however there was no significant change in the overall weight of subjects during intermittent fasting ,32. In tInflammation markers, in serum  and in subcutaneous adipose tissue and skeletal muscle , remained unchanged after the eating phases . In addiMoreover, changes in measurements, such as average dolorimetry, symptom score, tender point number, myalgic score, the Chalder Fatigue Scale, and significantly less Gulf War Illness symptoms, have been reported .Intermittent fasting improved cardiovascular markers and b-hydroxybutyrate levels increased even on non-fasting days. On fasting days, pro-aging amino-acid methionine was periodically depleted, while polyunsaturated fatty acids were elevated .On the other hand, an increase in LDL, plasma TNF-alfa, plasma cortisol, and systolic blood pressure, and a decrease in HDL, VO2 max, and the CANTAB score were produced by intermittent fasting, while avoiding physical activity and spending time resting in bed .p < 0.001) and 15% (p < 0.001), respectively, after the intervention of TRF. There was a significant improvement in the HOMA-β (p = 0.005) and HOMA-IR. (p = 0.02) scales along with a reduction in body weight and BMI [The results obtained showed that levels of biomarkers HbA1 and fasting plasma glucose were reduced by 18% ( and BMI .p = 0.62) [In regards to the eating window, no significant differences were found when comparing results between groups ( = 0.62) .p < 0.001) [After the intervention, an improvement in quality of life was produced in those that received TRF. The final rating on the SF-12 questionnaire was favorable (< 0.001) .p = 0.01). A significant increase in the level of tissue inflammation after eating (p = 0.02) was found. These levels decreased with intermittent fasting (p ≤ 0.01) but not with caloric restriction. Body weight and body fat decreased (p < 0.05) [The level of adipocytes decreased in all groups ( < 0.05) .p = 0.956), HDL-C (p = 0.728), or LDL-C (p = 0.726) [Once the follow-up time period had ended, no significant changes were observed in glucose level (= 0.726) .p = 0.001) and an increase in oxidative metabolic stress, at the cellular level, were observed in patients who received radiation or chemotherapy after having completed 90 days of the intervention [p = 0.43), triglycerides(p = 0.42), HDL-C (p = 0.39), LDL-C (p = 0.45), TC:HDL-C ratio (p = 0.28), TG:HDL-C ratio (p = 0.94), thyroid hormone, and electrolytes showed no difference between groups. After the intervention, lactate values decreased significantly after 12 weeks of intervention(p = 0.02) [A reduction in glucose levels (rvention .The leve = 0.02) ,35.p < 0.0001), daily calories (p < 0.01), weight loss (p < 0.001), BMI (p < 0.001), body fat (p = 0.03), and the sensation of appetite/hunger were all observed after 12 weeks of a KD intervention (p = 0.02). In addition, blood lipids were improved [A decrease in sugar consumption (improved ,34,36.Subjects with a higher adherence to a Mediterranean diet, of unspecified duration, scored better results in quality of life scales ,41,42, hThe strict adherence to a Mediterranean diet is significantly related to a healthier score in quality of life and chronic disease symptom scales and questionnaires ,40,41,42The objective of this study was to systematically review the existing scientific evidence on the efficacy of dietary strategies applied as part of the treatment of chronic musculoskeletal pain and health. As a result, 16 articles were selected of which 11 were RCTs and 5 were observational studies.Within the selected studies, six of them showed an improvement in chronic pain symptoms in response to a specific food strategy. These strategies included: a low glutamate diet for Gulf War Syndrome , the dieStudies have shown that the use of nutritional strategies may have an impact on pain and health. In this regard, current research shows that intermittent fasting improves pain perception in patients suffering from chronic pain in the short term. However, other studies have shown these changes to have no significant effect . CaloricPain symptoms have been shown to improve by following a diet low in glutamate. High levels of glutamate can produce an excess of oxytocin, which is one of the causes of oxidative stress and neuroinflammation, leading to cell death. The purpose of a low glutamate diet is to reduce exposure to glutamate, while taking in nutrients that protect against an excess of oxytocin and oxidative stress. This type of diet improves the metabolic energy of the neuronal cell, which not only helps to explain improvements in fatigue but also increases the potential for glutamate release in the neurotransmission process . In addiThe increased consumption of sugar has become an aggravating factor in health  as thereOther nutritional strategies, such as the KD and TRF, have not been found to produce changes in pain perception. However, the majority of these studies were carried out in healthy or overweight and obese adults, and therefore studies using these strategies are needed .A Mediterranean diet has been shown to positively affect low inflammatory diseases since it is comprised of elements that promote health and regulate certain immunological parameters . FurtherTRF has been shown to produce quantitative and qualitative changes in the level of health, including the regulation of the circadian rhythm, which represents different physiological processes that occur 24 h a day such as blood pressure, sleep cycle, respiration, heart rate, and cognitive processes. Our knowledge of how meal frequency and timing impact health and the circadian rhythm has yet to be explored, and it needs to be increased ,63. In tFinally, the KD seems to improve the quality of life in those suffering from breast cancer, but evidence is scarce and more studies are needed to corroborate these findings .This review presents a number of strengths. First, the use of different scales to ensure a good methodological quality of the studies collected and, therefore, the results obtained. Second, a wide variety in the studies analyzed, namely RCTs, observational studies, and systematic reviews. Finally, this study exposes current gaps in our understanding of how food strategies affect people suffering from chronic pain, and it proposes new directions to follow in this line. On the other hand, some weak points should be mentioned. The extensive variety of food strategies that can be used in this field of research makes the extrapolation of the results difficult to summarize. Moreover, due to the heterogeneity of the participants the results from this study should be interpreted with caution. Finally, due to the vast number of studies obtained during the search we did not research nor make comparisons between combinations of regimens as we did with the nutritional strategies in isolation.Current scientific literature shows that for diseases such as rheumatoid arthritis ,61, cancStudies that show consensus on the effects of an IF diet on pain improvement, in either the short or the long term, are lacking. IF improves mitochondrial metabolism, and with the addition of exercise both insulin resistance and inflammation levels may be decreased, thereby improving glucose metabolism, cognitive functions, and the cardiovascular system.Caloric restriction is a good long term treatment option for people suffering from pain of a rheumatic etiology. The TRF intervention improves health by affecting the circadian rhythm, and it therefore helps blood pressure, sleep quality, breathing quality, heart rate, and cognitive processes. The KD may improve the quality of life in those suffering from breast cancer.The quality of the studies collected on dietary strategies in the treatment of chronic pain and health is good. Further studies analyzing the effects of different food strategies on pain, quality of life, the circadian rhythm, and sleep quality, not only in isolation but in combination with other therapies in the short and the long term, are needed.


Based on the etiology, membranous nephropathy (MN) can be categorized into idiopathic membranous nephropathy (IMN) and secondary membranous nephropathy. Malignancy-associated membranous nephropathy (MMN) is a common type of secondary MN. Its incidence is only second to that of lupus nephritis. As the treatment and prognosis of MMN differ significantly from those of other MNs, the identification of MMN is crucial for clinical practice. The purpose of this study was to develop a model that could efficiently discriminate MMN, to guide more precise selection of therapeutic strategies.via calibration curve and decision curve analysis.A total of 385 with IMN and 62 patients with MMN, who were hospitalized at the First Affiliated Hospital of Zhengzhou University between January 2017 and December 2020 were included in this study. We constructed a discriminant model based on demographic information and laboratory parameters for distinguishing MMN and IMN. To avoid an increased false positivity rate resulting from the large difference in sample numbers between the two groups, we matched MMN and IMN in a 1:3 ratio according to gender. Regression analysis was subsequently performed and a discriminant model was constructed. The calibration ability and clinical utility of the model were assessed + T cell counts, levels of cystatin C, albumin, free triiodothyronine and body mass index, with a diagnostic power of 0.860 and 0.870 in the training and test groups, respectively. The model was validated to demonstrate good calibration capability and clinical utility.We constructed a discriminant model based on age, CD4In clinical practice, patients demonstrating higher scores after screening with this model should be carefully monitored for the presence of tumors in order to improve their outcome. Membranous nephropathy (MN), the most common cause of nephrotic syndrome in adults, is pathologically characterized by diffuse glomerular basement membrane thickening with subepithelial immune complex deposition. The immune complex is composed of immunoglobulin G, related antigens, complement components, and the membrane attack complex. MN can be categorized into idiopathic membranous nephropathy (IMN) and secondary membranous nephropathy according to the etiology. The most common cause of secondary membranous nephropathy is autoimmune disease, mainly systemic lupus erythematosus. Malignancy-associated membranous nephropathy (MMN) ranks second. Other causes include infections, drugs, and heavy metals .In 1996, Lee et al.  first prAs the treatment of MMN differs completely from that of IMN, immunosuppressive therapy recommended for IMN may exacerbate the malignancy; accurate diagnosis of MMN is therefore crucial. There are currently three criteria for MMN: i. the clinical symptoms and pathological features of renal disease are relieved after complete tumor remission, ii. pathological examination of renal tissue demonstrates positivity for the tumor antigen or antibody, and iii. recurrence or worsening of kidney disease is observed after tumor recurrence. However, the clinical application of the abovementioned standards is relatively difficult. As many antigens remain undiscovered, obtaining an early warning is currently difficult . ResearcIn this study, we aimed to build a simple and highly feasible differential model based on demographic characteristics and laboratory indicators, to identify MMN at an early stage and achieve the purpose of early detection, diagnosis, and treatment.We included patients with MN who were admitted to the First Affiliated Hospital of Zhengzhou University between January 2017 and December 2020. This study was approved by the First Affiliated Hospital of Zhengzhou University Ethics Review Committee (ZY-2021-0008). The inclusion criteria for IMN were as follows: i. patients aged 18–80 years, ii. initial onset of MN, and iii. diagnosed with MN by renal biopsy. The exclusion criteria were: i. prior history of administration of corticosteroids or immunosuppressors, and ii. presence of secondary membranous nephropathy, such as MN associated with autoimmune disease, cancer, infection, or drug toxicity. A total of 71 patients having both MN and malignancy were included in the study.Due to the current lack of clear criteria for MMN, contemporary diagnoses of MN and malignancy are generally accepted as a surrogate for the association between these two diseases . Therefo+ T cell (CD4) and CD8+ T cell (CD8) counts, the CD4/CD8 ratio, estimated glomerular filtration rate (eGFR) and erythrocyte sedimentation rate. Data regarding tubular atrophy and interstitial fibrosis were collected as pathological indicators.Demographic, clinical, and pathological baseline data were obtained at the time of renal biopsy or when patients were first admitted for nephrotic syndrome. The demographic information included age, gender, body mass index, systolic blood pressure, diastolic blood pressure, history of hypertension, and family history of malignancy. Clinical indices included white blood cell and platelet counts, and levels of hemoglobin, alanine aminotransferase, aspartate aminotransferase, serum creatinine (Scr), uric acid, cystatin C (Cysc), albumin (ALB), total cholesterol, triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), β2-microglobulin, α1-microglobulin, C-reactive protein, complement 3, complement 4, free triiodothyronine (FT3), free thyroxine, thyroid stimulating hormone, M-type phospholipase A2 receptor (PLA2R), and 24-h uric total protein (24hTP), CD4The proportions of missing values were as follows: 24hTP had 1.1% missing values; Cysc, HDL, and LDL had 2.2% missing values; and CD4 and CD8 had 4.3% missing values. In order to address the issue of missing data, achieve maximum statistical power, and reduce bias, we used multiple imputation method to impute missing values . The mea2, evaluated the degree of fit of the regression equation. Decision curve analysis was performed to determine the clinical utility of the discriminant model at different threshold probabilities by quantifying the net benefit  and MMN (n=62) are presented in The cancer prevalence in patients with MN was 16.1%; this increased significantly with age Table 3.Owing to limited data regarding patients with MMN, we performed internal validation with the dataset instead of external validation. Participants were randomly divided into training (n=173) and test (n=75) groups. As shown in y = 2.336*Cysc+0.050*age-0.002*CD4+0.550*FT3+0.062*ALB-0.099*BMI-6.017.We performed receiver operating curve analysis to evaluate the combined diagnostic power of the regression-screened indicators. The area under the receiver operating curve, known as the C statistic, is considered to be a metric for assessing the validity of the model. We found that in training group, area under the curve (AUC) was 0.860  plot showed that Cysc (33.2%) accounted for the highest proportion of variance in the model, followed by age (22.1%), CD4 (20.8%), FT3 (11.4%), and ALB (6.5%).A nomogram was constructed to determine the possibility of MMN more intuitively and to increase usability of the model. Using this nomogram, clinicians can calculate a patient’s total score by taking the score corresponding to each predictor and then reading the corresponding MMN likelihood based on the total score Figure 4The decision curve analysis is shown in We established a discriminant model for early screening and distinction between MMN and MN, to facilitate individualized precision treatment. We included 62 patients diagnosed as MMN, and 186 patients diagnosed as IMN. Model was constructed based on age, levels of Cysc, FT3, ALB, CD4+ T cell counts and BMI . This model is of considerable significance for the early diagnosis of MMN, selection of treatment, and follow-up monitoring.Distinction between MMN and IMN is critical owing to considerable differences in terms of treatment and prognosis. Some studies have discussed the early identification and screening of MMN. For instance, certain previous studies have indicated that PLA2R, THSD7A, and IgG subclasses may have the potential to aid recognition of MMN , 17. AltAs a quantitative tool for risk and benefit assessment, clinical prediction models can provide doctors, patients, and medical policy makers with more intuitive and rational information for decision-making , 19. Cli+ T cell count, FT3 levels, ALB levels and BMI. Cysc is a member of the cysteine protease inhibitor family and is used to evaluate glomerular filtration rate  in distinguishing MMN and IMN. We suggest that patients with a high risk of MMN should be carefully monitored for the existence of tumors using appropriate tumor screening. Patients with high-risk score who have no tumor at the initial diagnosis should be carefully followed-up, since the sign of cancer may not be immediately obvious.The model has certain limitations. The overall sample size was small, and external validation was not performed. Multi-center and multi-regional validation will be needed in future studies. Nevertheless, this is the first model to be constructed relating to this issue. We believe that it could help early screening and identification of MMN, thereby guiding treatment and follow-up.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the First Affiliated Hospital of Zhengzhou University Ethics Review Committee. The patients/participants provided their written informed consent to participate in this study.ZZ and JS designed the study; TW and WY collected the data and drafted the paper; TW, FW and YZ analyzed the data and made the figures; ZZ and JS revised the paper. All authors approved the final version of the manuscript.This work was supported by the National Natural Science Foundation of China (Grant Nos. 81873611 and 8217033050), Science and Technology Innovation Team of Henan (Grant No. 17IRTSTHN020), 2020 key project of medical Science and Technology to JS.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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