The US Preventive Services Task Force recommends blood pressure (BP) measurements using 24-h ambulatory monitoring (ABPM) or home BP monitoring before making a new hypertension diagnosis.Compare clinic-, home-, and kiosk-based BP measurement to ABPM for diagnosing hypertension.Diagnostic study in 12 Washington State primary care centers, with participants aged 18–85 years without diagnosed hypertension or prescribed antihypertensive medications, with elevated BP in clinic.Randomization into one of three diagnostic regimens: (1) clinic ; (2) home (duplicate BPs twice daily for 5 days); or (3) kiosk (triplicate BPs on 3 days). All participants completed ABPM at 3 weeks.Primary outcome was difference between ABPM daytime and clinic, home, and kiosk mean systolic BP. Differences in diastolic BP, sensitivity, and specificity were secondary outcomes.P<.001); home ; and kiosk . Differences for diastolic BP were clinic ; home ; and kiosk . Sensitivities for clinic, home, and kiosk compared to ABPM were 31.1% , 82.2% , and 96.0% , and specificities 79.5% , 53.3% , and 28.2% , respectively.Five hundred ten participants  with 434 (85.1%) included in primary analyses. Compared to daytime ABPM, adjusted mean differences in systolic BP were clinic (−4.7mmHg ; Single health care organization and limited race/ethnicity representation.Compared to ABPM, mean BP was significantly lower for clinic, significantly higher for kiosk, and without significant differences for home. Clinic BP measurements had low sensitivity for detecting hypertension. Findings support utility of home BP monitoring for making a new diagnosis of hypertension.ClinicalTrials.gov NCT03130257 https://clinicaltrials.gov/ct2/show/NCT03130257The online version contains supplementary material available at 10.1007/s11606-022-07400-z. A study using National Health and Nutrition Examination Survey 2017–2018 data estimated that 23% of US adults with high BP (systolic ≥140 mmHg or diastolic ≥90 mmHg) were unaware they had hypertension and were untreated.3Elevated blood pressure (BP) is the leading contributor to cardiovascular events and mortality.4 and other hypertension guidelines recommend follow-up BP testing outside of clinics by 24-h ambulatory BP monitoring (ABPM) or home BP monitoring (HBPM), to avoid overdiagnosis and unnecessary treatment.7For patients with high screening BP in clinic, US Preventive Services Task Force (USPSTF)8 HBPM is an alternative, but patients need to use validated monitors, be trained on proper use, and take multiple measurements,11 leading to questions about the accuracy of using HBPM to diagnose hypertension. BP kiosks in pharmacies or clinic waiting areas are an alternative for HBPM, but to our knowledge, no studies have compared kiosk to ABPM for making a new hypertension diagnosis. Blood Pressure Checks for Diagnosing Hypertension (BP-CHECK) was a randomized diagnostic study comparing the accuracy of clinic BP, HBPM, and kiosk BPs to ABPM for making a new diagnosis of hypertension.Currently, ABPM is infrequent in the USA, partly from lack of availability, low reimbursement, and perceptions of lower patient acceptability.12 Electronic health records (EHRs) were used to identify individuals aged 18–85 with elevated BP (≥138 mmHg systolic or ≥88 mmHg diastolic at last outpatient visit) with no hypertension diagnosis in the prior 2 years and no prescribed antihypertensive medications in the prior 12 months. EHR data were used to exclude patients with BP ≥180 mmHg systolic or ≥110 mmHg diastolic; pregnancy; life-limiting illness ; and conditions that might make participation difficult  or BP monitoring less accurate .The setting was 12 KPWA primary care centers in Western Washington. Enrollment was May 11, 2017, to March 4, 2019. Study design and methods were published.11 After 5 minutes' rest, BP was measured twice 1 min apart using a validated Omron 907XL monitor.13Potentially eligible individuals were mailed an invitation with a study description and $2 bill and called to confirm eligibility. Those willing to participate were scheduled for screening visits. At visits, a research assistant confirmed individuals had not engaged in heavy exercise, used tobacco, or had caffeinated drinks in the prior 30 min. Individuals sat in a chair with back support and the upper left arm was measured with an appropriately sized cuff used.Individuals with BP ≥140 mmHg systolic or ≥90 mm Hg diastolic on both measurements were eligible. Those with mean ≥180 mmHg systolic or ≥110 mm Hg diastolic were excluded and told to make a physician’s appointment.The biostatistician used R statistical software (version 3.2.2) to randomly assign patients to clinic, home, or kiosk groups. Randomization was stratified by clinic, age (<60 or ≥60 years), and mean baseline systolic BP (<150 or ≥150 mmHg), with random block sizes of 3 or 6 within each stratum. Except for study biostatisticians, investigators were blinded until all outcome data were collected. Participants and study staff were aware of group assignments.14 or, less frequently, with a validated oscillometric BP monitor (GE CARESCAPE V100).15 For high initial BP (systolic BP ≥140 mmHg or diastolic BP ≥90 mmHg), standard procedure is to measure BP again after at least 5 min.16 If BP is elevated again, the patient’s clinician is notified to make a plan and a follow-up BP-check visit is scheduled. These steps are repeated until BP is <140/90 mmHg.The clinic group received routine care for high BP at KPWA. Participants were instructed to make an appointment at their clinic for a BP recheck. Clinic BP measurements are usually taken by a medical assistant using a wall-mounted aneroid BP monitor (Welch Allyn Tyco)17 with an appropriately sized upper-arm cuff to take duplicate measurements twice daily for 5 days: after rising and at bedtime .18 Home BPs were collected by study staff via Bluetooth.Home participants received a validated Bluetooth-enabled oscillometric Omron N786 BP monitor19 at KPWA clinics or nearby pharmacies, with triplicate measurements on three separate days.20 Measurements were linked to participants via a kiosk smartcard and collected using the vendor’s cloud-based service.Kiosk participants were asked to measure BP using validated PharmaSmart BP kiosksClinic, home, and kiosk participants received verbal and written instructions. All participants received the same reminder 2 weeks after their initial visit to complete their diagnostic assignment if they had not already done so.21 measuring BP every 30 min, 7AM to 11PM, and hourly, 11PM to 7AM. Patient participants and their physicians received ABPM results and were advised to follow up on tests that were positive for hypertension. ABPM results and communications were documented in the EHR (home and kiosk BPs were not).All participants were asked to return at 3 weeks for ABPM diagnostic testing by validated Welch Allyn 7100 ABPM ,2; <30 vs. ≥30); cardiovascular disease (CVD) risk; and race.The primary outcome was participant’s mean systolic BP for assigned diagnostic method using all available BP data. Mean diastolic BP and diagnostic accuracy were secondary outcomes. Daytime ABPM was defined as the mean of BPs collected 7AM-11PM. Nighttime plus daytime measurements were used for secondary outcomes of mean 24-h and mean nighttime ABPM. Adverse events potentially related to study participation were reported using the question: “Since your last research visit, have you experienced any new or serious health concerns?” BP outcomes at 6 months included receipt of a new hypertension diagnosis in the EHR . Prespecified subgroup comparisons were based on potential to influence diagnostic performance: baseline systolic BP (<150 mmHg vs. ≥150); age (<60 vs. ≥60 years); arm size (<33 vs. ≥33 cm); body mass index (BMI as kg/m22With a sample size of 510 (170 per group) and assuming an outcome ascertainment rate of at least 80%, we could detect a 4.1-mmHg difference in systolic and a 2.8-mmHg difference in diastolic BP between any two groups (assuming standard deviation 12.1 mmHg systolic and 8.3 mmHg diastolic). BP differences and standard deviation were based on prior studies comparing clinic, home, and kiosk BP to ABPM.23 Group differences in the comparability of the diagnostic test systolic BP relative to ABPM (primary outcome), were assessed using linear regression models with the dependent variable defined as within-person difference in systolic BP between the mean diagnostic test and ABPM measures. Models included indicators for diagnostic group, with adjustment for age, sex, BMI, education, and baseline systolic and diastolic BP. Generalized estimating equations with robust standard error were used to relax normality assumptions.24 Fisher protected least significant difference approach was used to protect against multiple comparisons, requiring that the omnibus test of any differences between groups was statistically significant before making pairwise comparisons.25 To address bias due to missing data, a sensitivity analysis was conducted using inverse probability weighting for the primary analysis population only. Group differences for diastolic BP measurement (secondary outcome) were assessed using similar methods. In exploratory analyses, we restricted analyses to those defined a priori as adherent to assigned diagnostic regimen, based on evidence for home and kiosk,20 and usual care for clinic: clinic, 1 outpatient clinic BP; home, 16 measurements over ≥4 days; kiosk, 6 measurements over ≥2 days. Effect modification by baseline BP and participant characteristics  was assessed by including interactions between diagnostic method and subgroups. Bland-Altman plots were used to show agreement between diagnostic BP measurements and ABPM.Primary and secondary outcomes were analyzed among participants completing ≥1 BP diagnostic test measurement and ≥14 daytime reference test ABPM measurements.27 Sensitivity was estimated by fitting a logistic regression model with an indicator for a positive test result as the dependent variable and diagnostic group as the dependent variable among participants with hypertension by ABPM. Specificity estimates were similar but with an indicator for a negative test result as dependent variable and diagnostic group as independent variable among participants without hypertension by ABPM. We also estimated positive and negative predictive values, and positive and negative likelihood ratios for each group. Exploratory analyses estimated these diagnostic metrics for a variety of systolic/diastolic pairs of thresholds for defining a positive diagnostic test using receiver operating characteristic (ROC) curves and computed area under the curve (AUC) separately for systolic and diastolic BP. AUC was not calculated for the curve based on systolic/diastolic pairs, because there is no way to vary the paired thresholds across the range of possible values.Secondary analyses assessed the diagnostic performance of clinic, home, and kiosk BP measures by estimating the sensitivity and specificity of each diagnostic method compared to the reference standard, daytime ABPM. Hypertension diagnosis according to the reference standard was defined as mean daytime ABPM systolic ≥135 mmHg and/or diastolic ≥85 mmHg. For each diagnostic method, we defined a positive test as mean diagnostic BP above a threshold for hypertension of systolic BP ≥140 mmHg and/or diastolic BP ≥90 mmHg for clinic and systolic ≥135 mmHg and/or diastolic ≥85 mmHg for home and kiosk.26 Analyses used Stata statistical software (release 15). Statistical inference used 2-sided hypothesis tests, and significance threshold P<.05.Additional exploratory analyses included diagnostic performance assessment using the following: (a) mean 24-h ABPM instead of daytime ABPM as the reference standard with hypertension thresholds of mean systolic ≥130 mmHg and mean diastolic ≥80 mmHg and (b) American College of Cardiology/American Heart Association (ACC/AHA) recommendations for diagnosing stage 1 hypertension .Fig.TableFig., Appendix Table We mailed 9434 invitation letters to patients with EHR data showing BP ≥138/88 at their last clinic visit, no hypertension diagnosis, and no antihypertensive medications (P<.001); home ; and kiosk  (TableP<.001); home ; and kiosk . Bland-Altman plots demonstrate the variability of within-person differences  clinic, 82.2%  home, and 96.0%  kiosk. Specificities were 79.5%  clinic, 53.3%  home, and 28.2%  kiosk  analyses for systolic BP  of individuals with hypertension based on ABPM testing (reference standard), 40.9% (137/335) had a hypertension diagnosis recorded in the EHR by their provider between the baseline and 6-month follow-up visit, with no differences by randomization group.n=20), skin irritation (n=7), sleep disturbance (n=7), and anxiety or restlessness (n=5).No adverse events related to clinic, home, or kiosk BP monitoring were reported. ABPM-related adverse events were reported by 36 participants with 39 complaints: arm discomfort , end-number rounding, and unintentional bias toward lower BP readings.29 Our findings align with a cluster randomized trial that found systolic BP 7.5 mmHg lower in clinics using manual measurements than clinics with BP measured by validated oscillometric automated monitors (P<.001), with substantial reductions in end-number rounding .30 Routine use of automated monitors, especially with multiple readings taken over several minutes, could lead to average clinic BPs closer to ABPM;32 however, this practice is uncommon in the USA.33A systematic review for the USPSTF evaluated the accuracy of office-based BPs compared to ABPM for screening and confirming hypertension.34 This policy, recommended by our healthcare organization and national guidelines was informed in part by Handler et al., which analyzed data from National Health and Nutrition Examination Surveys among 22,641 adults with 3 BP readings.16 Among those with Joint National Committee 7 (JNC7)35 defined stage 1 hypertension (systolic BP ≥140 to <160 mmHg or diastolic ≥90 to <100 mmHg) or stage 2 hypertension (systolic ≥160 mmHg or diastolic ≥100 mmHg), 18.2% and 33.5%, respectively, were reclassified to a lower stage using the mean of the second and third BP readings, and less than 0.5% were reclassified to a higher stage. This was interpreted to mean that no additional measures are needed if the first BP is below a threshold, but if the first BP is high, it should be checked again after 5 min of rest. Criticisms of this paper include lack of comparison to ABPM or repeated BPs taken on separate days. Thus, downward reclassification after repeated BP measurements may lead to a bias of using the lowest BP instead of the true average, which our study suggests.Another reason that clinic BPs may have been lower than ABPM in our setting is because BP is rechecked only if the initial BP was >140/90 as recommended by our healthcare organization and national guidelines.7 initial screening BPs had low sensitivity and moderate specificity for detecting ABPM-confirmed hypertension, with pooled sensitivity and specificity from 15 studies of 54% and 90%, respectively. However, confirmatory office-based BPs  tended to overdiagnose hypertension with pooled sensitivity and specificity from 8 studies of 80% and 55%.7 These results are in sharp contrast to our study, with clinic sensitivity 31.1% and specificity 79.5%. The negative likelihood ratio  was close to 1.0, indicating that a clinic BP less than 140 mmHg systolic and 90 mmHg diastolic (negative test) had little impact on the likelihood that the ABPM would be negative. Our study suggests that confirmatory BPs in clinic, as practiced in routine care, may be more likely to underdiagnose than overdiagnose hypertension.Lower clinic BPs resulted in lower sensitivity for detecting hypertension than home and kiosk. In the USPSTF review,36 Home provided many more BPs, with average standard deviation smaller than single or duplicate clinic measurements. Home had higher sensitivity than clinic for detecting hypertension, but at the expense of specificity. However, as most participants had hypertension, false positive rates were relatively low. Our Home BP results are similar to those reported by the USPSTF review,7 which had HBPM pooled sensitivity and specificity 84.0% and 60.0%, respectively. While ABPM is considered the “gold standard,” controversy remains.27 Both ABPM and HBPM are more predictive of cardiovascular events than clinic BPs. Three studies demonstrated that ABPM more closely correlates with left ventricular hypertrophy/mass than HBPM.39 However, no trials have compared the effectiveness of ABPM and HBPM in preventing CVD events.Home BP was similar to ABPM. BP is highly variable, particularly among individuals with untreated hypertension, with systolic often varying by 30–40 mmHg or more during daytime hours.19 Another study compared it to ABPM,20 with 100 individuals without treated hypertension taking triplicate kiosk BP measurements at local pharmacies on 4 separate days. Kiosk BP was only slightly higher than ABPM . Kiosks in our study were often in busy waiting rooms, possibly leading to higher BPs. While participants were instructed to rest 5 min before measurements, we are not certain they did. Additional study could determine if protocol or equipment adjustments  improve BP kiosk performance.Mean systolic and diastolic BPs were significantly higher for kiosk than ABPM. Kiosk diagnostic accuracy was still better than clinic across a range of BP thresholds. The BP kiosk we used was validated against mercury manometers.5. Using ACC/AHA stage 1 BP thresholds, 86% of our population tested positive for hypertension. The ACC/AHA guidelines recommend lifestyle behavior change to lower BP for individuals with stage 1 hypertension at low risk for CVD and antihypertensive medications for those at moderate-to-high CVD risk. Most of our population was at moderate-to-high CVD risk and would qualify for antihypertensive medications at the lower stage 1 threshold.40Our study used ACC/AHA stage 2 recommendations for making a new diagnosis of hypertension41 This US study is testing a multilevel strategy that includes provider presentations, patient information, nurse training for teaching patients to conduct HBPM, EHR-embedded clinical decision support, and increased access to a culturally adapted ABPM service. Results of this study might inform next steps for improving hypertension diagnosis.Even though home BP monitoring performed better than clinic and kiosk, many implementation challenges remain. The tools we used to systematically collect and average home BPs (Bluetooth connectivity and a database for averaging BPs) are not typically available in primary care. Furthermore, although participants and their physicians received ABPM results and the diagnostic interpretation of the test, only 41% of those with a positive test had a hypertension diagnosis recorded in the EHR by 6 months. We know of only one implementation study that is testing different strategies for improving hypertension diagnosis, with results not yet published.42 Fourth, participants were aware of their diagnostic group assignment and that they had high BP. Contextual and behavioral factors might have influenced BP diagnostic results. Fifth, diagnostic performance may have differed if we included individuals with lower BPs. Sixth, statistical comparisons of AUCs for ROCs of systolic or diastolic BP between diagnostic methods and the reference standard should be interpreted cautiously because the diagnosis of hypertension is based on both systolic or diastolic BP, rather than considering each measure separately, and thresholds for diagnosing hypertension differ across guidelines and subgroups. Last, assessment of comparative performance across measurement methods might have been more robust if participants had completed all three diagnostic regimens. However, our study compared hypertension diagnosis methods as used in real-world settings, which would not be possible with that study design.Study limitations were first, since the study was conducted at a single healthcare organization, results might differ at other settings with different standards for measuring BP, such as use of automated BP. Second, African American and other racial/ethnicity groups were underrepresented. Third, although the accuracy of all BP monitors used was validated by established protocols, results might differ with other monitors.In this diagnostic study, compared to ABPM, clinic had significantly lower and kiosk significantly higher mean BP measures. HBPM was not significantly different, lending further credibility to the utility of home measurements. Most participants with high BP on screening and ABPM diagnostic testing did not receive a hypertension diagnosis. New strategies are needed to improve hypertension diagnosis.ESM 1(DOCX 448 kb)


Proportions of significant fibrosis (LSM ≥2.97 kPa) in no glucose intolerance, prediabetes, and diabetes groups were 3.1%, 4.4%, and 16.7%, respectively (p<0.001). Compared with those with no glucose intolerance, those with diabetes had higher risk of significant fibrosis . However, there was no difference between prediabetes and no glucose intolerance . A subgroup analysis also showed that prediabetes, unlike diabetes, was not associated with significant fibrosis in subjects with or without liver disease. Diabetes, but not prediabetes, is a risk factor for significant liver fibrosis. This finding is consistent regarldess of the pressence of liver disease.The aim of this study was to assess the risk of liver fibrosis in those with no glucose intolerance, prediabetes, or diabetes. A cross-sectional study was conducted based on a cohort from a health examination program which included a magnetic resonance elastography (MRE). Participants were classified into three groups according to glucose tolerance: no glucose intolerance, prediabetes, and diabetes mellitus. Liver fibrosis was evaluated by liver stiffness measurement (LSM) value using two-dimensional real-time MRE. The risk of significant liver fibrosis was compared among three groups. A total of 2,090 subjects were included: no glucose intolerance (n = 889); prediabetes (n = 985); and diabetes (n = 216). Mean values of LSM in those with no glucose intolerance, prediabetes, and diabetes were 2.37 ± 0.43 kPa, 2.41 ± 0.34 kPa, and 2.65 ± 0.70 kPa, respectively ( Diabetes mellitus is a chronic, metabolic disease characterized by elevated levels of blood glucose. It causes serious damage to the heart, eyes, kidneys, blood vessels and nerves . Liver iLiver fibrosis is an essential condition for determining the prognosis of chronic liver diseases such as viral hepatitis, alcoholic liver disease, and NAFLD . It is aPrediabetes is a condition in which blood glucose level is higher than normal, but not high enough to be diagnosed as diabetes mellitus . HyperglThis cross-sectional study was conducted based on a subset of a cohort which consisted of subjects aged 18 or over who underwent a health examination at the Kangbuk Samsung Hospital Total Healthcare Centers in Seoul and Suwon, Korea. The population of this study was composed of 2,130 subjects who participated in a health examination including MRE between 2015 and 2018. This study was approved by the Institutional Review Board (IRB) of Kangbuk Samsung Hospital (IRB No. 2021-08-024). The requirement for informed consent was waived due to the use of a preexisting de-identified dataset that combined the data routinely collected during the health screening process.To investigate the association between diabetes or prediabetes and liver fibrosis, the target population was defined based on the following criteria . First, Height, weight, waist circumference, systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured. Participants also underwent blood tests such as platelet count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), total cholesterol, triglyceride, high density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, FBS, HbA1c, serum ferritin level, hepatitis B virus surface antigen (HBsAg), and hepatitis C virus (HCV) antibody. Insulin resistance was assessed using homeostasis model assessment of insulin resistance (HOMA-IR) . FibrosiInformation about use of anti-hypertensive drugs, anti-diabetic drugs, lipid-lowering agents, and alcohol consumption was evaluated based on a self-administered questionnaire. Hypertension was defined as BP≥ 140/90 mmHg or use of anti-hypertensive drugs. Metabolic syndrome was defined based on updated National Cholesterol Education Program Adult Treatment Panel III standards , 16. AlcThe diagnosis of fatty liver was based on abdominal ultrasonography operated by several experienced radiologists who were blinded to the aim of the present study. Ultrasonographic diagnosis of fatty liver was determined by known standard criteria, including a diffuse hyperechoic echotexture in the liver parenchyma compared with right renal cortex, deep-echo attenuation, and vascular blurring. Increased liver echogenicity at ultrasound examination reflected the degree of steatosis but not of fibrosis in asymptomatic patients with mild/moderate abnormalities of liver transaminases .To investigate the association between diabetes or prediabetes and liver fibrosis according to the presence of liver disease, we determined the risk group for liver disease as follows: (i) viral hepatitis (n = 191) including HBsAg positivity (n = 181) and HCV antibody positivity (n = 10); (ii) nonalcoholic fatty liver disease (NAFLD) (n = 640); (iii) significant alcoholic consumption (n = 579); and (iv) AST > 40 U/L or ALT > 40 U/L by any cause other than viral hepatitis, NAFLD, and significant alcoholic consumption (n = 25). We defined ‘no liver disease’ as the absence of these risk factors for liver disease (n = 655).To assess liver fibrosis, two-dimensional real-time MRE was used in this study. Magnetic resonance imaging (MRI) examinations for the liver, including axial T2-weighted and T1-weighted, coronal T2-weighted and diffusion-weighted images, were performed using 1.5 T MRI systems . The median stiffness value was calculated based on the per‐pixel stiffness values across the region of interests (ROIs). Results were automatically displayed on each slice in units of kilopascals (kPa). The average of median stiffness values of these three slices was used as the liver stiffness measurement (LSM) value for each patient.This study assessed liver fibrosis in five stages according to LSM values in MRE: F0 (no fibrosis); F1 ; F2 (significant fibrosis); F3 (advanced fibrosis); and F4 (cirrhosis). Cut-off values of LSM in F1, F2, F3, and F4 were 2.61 kPa, 2.97 kPa, 3.62 kPa, and 4.70 kPa, respectively . Signifip-value < 0.05. All statistical analyses were performed using SPSS Statistics ver. 24.0 .Students’ t-test and chi-square test were used to analyze continuous and categorical variables, respectively. One-way ANOVA and chi-square test were used to calculate differences between continuous and categorical variables among three groups . Post-hoc analysis was performed using multiple comparisons with Bonferroni correction to assess the difference among the three groups. A multivariable logistic regression model was used to investigate the influence of glucose intolerance on significant liver fibrosis. Multivariable analysis was performed in the following manners. First, we included any variable that was related to the clinical outcome (significant fibrosis) at a P-value of <0.05. Second, the multivariable logistic regression analysis was performed based on a minimum of 10 outcomes per predictor variable. Third, multivariable analysis was performed using a forward conditional stepwise procedure to avoid multicollinearity. A statistical significance was regarded at 2. Mean FBS, HbA1c, and HOMA-IR levels were 101.6 ± 17.5 mg/dL, 5.7 ± 0.6%, and 1.86 ± 1.51, respectively. Mean AST, ALT, and GGT levels were 26.7 ± 15.9 U/L, 29.8 ± 21.5 U/L, and 51.6 ± 72.6 U/L, respectively. Mean LSM in MRE was 2.42 ± 0.43 kPa. The prevalence of hypertension, metabolic syndrome, and NAFLD were 25% (n = 530), 23% (n = 474), and 31% (n = 640), respectively. HBsAg was detected in 181 (9%) patients. HCV antibody was founded in 10 (1%) patients. Of these 191 patients, the use of antiviral agents was observed in 29 (15%) patients. Significant alcohol consumption was observed in 579 (28%) patients. p < 0.05). Mean values of LSM in no glucose intolerance, prediabetes, and diabetes groups were 2.37 ± 0.43 kPa, 2.41 ± 0.34 kPa, and 2.65 ± 0.70 kPa, respectively (p < 0.001). In the post-hoc analysis, LSM in the diabetes group was significantly higher than that in the no glucose intolerance group and the prediabetes group (both p < 0.001). However, there was no statistical difference in LSM between the no glucose intolerance group and the prediabetes group (p = 0.141).Baseline characteristics of 2,090 participants are shown in p < 0.001). In the no glucose intolerance group (n = 889), numbers of subjects with F0, F1, F2, F3, and F4 were 730 (82.1%), 131 (14.7%), 22 (2.5%), 2 (0.2%), and 4 (0.4%), respectively. In the prediabetes group (n = 985), numbers of subjects with F0, F1, F2, F3, and F4 were 769 (78.1%), 173 (17.8%), 36 (3.7%), 6 (0.6%), and 1 (0.1%), respectively. In the diabetes group (n = 216), numbers of subjects with F0, F1, F2, F3, and F4 were 136 (63.0%), 44 (20.4%), 22 (7.3%), 9 (4.2%), and 5 (2.3%), respectively. We also evaluated significant fibrosis and advanced fibrosis according to glucose tolerance (p < 0.001). In the post-hoc analysis, the proportion of significant fibrosis in the diabetes group was significantly higher than that in the no glucose intolerance group and the prediabetes group (p < 0.001 for both). However, there was no statistical difference in the proportion of significant fibrosis between no glucose intolerance and prediabetes groups (p = 0.169). There was a significant difference in advanced fibrosis (≥ F3) among no glucose intolerance, prediabetes, and diabetes groups. Proportions of advanced fibrosis in no glucose intolerance, prediabetes, and diabetes groups were 0.7% (n = 6), 0.7% (n = 7), and 6.5% (n = 14), respectively (p < 0.001). In the post-hoc analysis, the proportion of advanced fibrosis in diabetes group was significantly higher than that in the no glucose intolerance group and the prediabetes group (p < 0.001 for both). However, there was no statistical difference in the proportion of advanced fibrosis between no glucose intolerance and prediabetes groups (p = 0.926). We additionally investigated the difference of liver fibrosis according to glucose intolerance in subjects with or without liver disease (olerance . There w disease  Tables. p = 0.019), BMI ≥25 kg/m2 , AST ≥40 U/L , ALT ≥40 U/L , GGT ≥60 U/L , ferritin ≥300 ng/mL , Platelet count ≤160 x103/mm2 , HDL cholesterol ≤40 mg/dL , hypertension , metabolic syndrome , viral hepatitis without the use of antiviral agents , viral hepatitis with the use of antiviral agents , and diabetes . The multivariable analysis showed that the significant risk factors for significant fibrosis were AST ≥40 U/L , GGT ≥60 U/L , Platelet count ≤160 x103/mm2 , viral hepatitis without the use of antiviral agents , viral hepatitis with the use of antiviral agents , and diabetes . However, there was no significant relationship between prediabetes and significant fibrosis in the univariable and multivariable analyses . We additionally analyzed the risk of significant fibrosis in no glucose intolerance, prediabetes, and diabetes groups in patients with or without liver disease  were associated with liver fibrosis. Elevated liver enzymes mean inflammation or damage to cells in the liver. Chronic inflammation in the liver could induce liver fibrosis regardless of the etiology of liver , 20. MetResults of this study indicate that diabetes is a risk factor for significant fibrosis in subjects without or with liver disease. The study population included subjects without liver disease because the cohort was based on a health screening program. Previous studies have shown that diabetes is associated with liver fibrosis in case of no liver disease , 26. DoyPrediabetes is an intermediate stage between normal glucose tolerance and diabetes. The prevalence of prediabetes in US adults was 12.3–43.5% . MicrovaThe study has some limitations. First, liver biopsy, the gold standard method to evaluate liver fibrosis, was not included in this study. MRE was used as a noninvasive exam to assess liver fibrosis because the study population was enrolled from a health screening program with a large number of participants. However, MRE provides a higher diagnostic performance than transient elastography and shear wave elastography to evaluate early stages of liver fibrosis based on the histologic findings , 36. SecDiabetes, but not prediabetes, is a risk factor for significant liver fibrosis in the general population. This finding is consistent regarldess of the pressence of liver disease. These findings provide clinical evidence on the difference of liver fibrosis according to glucose tolerance.S1 FigSignificant fibrosis and advanced fibrosis according to glucose intolerance in subjects with (A and B) or without liver disease (C and D).(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S1 Dataset(DOCX)Click here for additional data file.


Peripheral and central hemodynamic indices are modifiable by lifestyle and medical intervention. We aimed to determine the long-term effect of intensive multifactorial treatment on peripheral and central hemodynamic indices among people with screen-detected diabetes.Between 2001 and 2006, people with screen-detected type 2 diabetes were included in the Anglo-Danish-Dutch study of Intensive Treatment of Diabetes in Primary Care (ADDITION) trial . In the Danish arm, participants were invited to a clinical examination in 2015–2016, 13 years after inclusion and 8 years after trial-end. Out of 586 eligible participants who attended the clinical examination, 411 had a valid examination of central and peripheral hemodynamic indices (242 received intensive treatment and 169 received routine care). Carotid-femoral pulse wave velocity (cfPWV), central blood pressure and augmentation index were assessed by applanation tonometry. We used mixed-effect models to examine the intervention effect adjusting for cluster randomization and heart rate.Randomization to intensive treatment during the trial-period was associated with a 0.58 m/s lower cfPWV (95% CI − 1.09 to − 0.06) at follow-up. Adjustment for blood pressure attenuated the association. Differences between intervention groups for central augmentation index were − 1.25% (95% CI: − 3.28 to 0.78), central pulse pressure − 1.74 mmHg (95% CI − 4.79 to 1.31), central systolic blood pressure − 3.06 mmHg (− 7.08 to 0.96), and central diastolic blood pressure − 1.70 mmHg (− 3.74 to 0.34).Intensive multifactorial treatment of screen-detected type 2 diabetes has a sustained positive effect on aortic stiffness measured by cfPWV. Although all estimates pointed in favor of intensive treatment, we observed no clear beneficial effect on other hemodynamic indices.The online version contains supplementary material available at 10.1186/s13098-022-00890-1. Cardiovascular disease (CVD) is a frequent complication of type 2 diabetes  leading Aortic stiffness, measured as carotid-femoral pulse wave velocity (cfPWV), is an independent predictor of CVD and mortality , 10. cfPIntensive risk factor management with antihypertensive and lipid-lowering treatment has been shown to reduce aortic stiffness –19. AlsoWe hypothesized that intensive multifactorial treatment early in the course of diabetes, including target driven glycemic control, lipid-lowering medication and antihypertensive medication, may have long-term effects on both central and peripheral hemodynamics and reduce the process of arterial stiffening in people with screen-detected type 2 diabetes.The current study is a post-hoc analysis of the central and peripheral hemodynamic outcomes from the Danish arm of the ADDITION-trial collected 13 years following randomization. The rationale, methods and results from the cluster randomized-controlled ADDITION-trial have been reported in detail previously . BrieflyThe study was conducted in accordance with the principles of the 1996 Declaration of Helsinki, approved by the local Ethics committee in the Central Denmark Region , and approved by the Danish Data Protection Agency . All study participants gave written informed consent. The ADDITION trial is registered with NCT00237549, ClinicalTrials.gov.Using standardized operating procedures, a physical health assessment including anthropometrics and venous blood samples was performed at baseline and follow-up. Biochemistry measures included HbA1c, total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL cholesterol), high-density lipoprotein cholesterol (HDL cholesterol). Albumin-to-creatinine ratio [u-ACR] was measured in spot urine samples. From self-reported questionnaires, we obtained information on smoking status and alcohol consumption, while participants’ general practitioners provided baseline records of prescribed medication. At follow-up prescribed medication was obtained from the Danish National Health Service Prescription Database .The assessment of central and peripheral hemodynamics was performed by trained staff according to standardized study protocols and has been described previously . cfPWV, Before assessment of central hemodynamic indices, the brachial blood pressure was measured after 5 min of rest with the patient in supine position . The mean blood pressure was calculated as diastolic blood pressure + 0.4 × pulse pressure .We assessed the velocity of the pulse waves between the right carotid and right femoral arteries. The travel distance was determined by subtracting the distance from the suprasternal notch to the carotid artery (measured with a tape measure) from the distance from the suprasternal notch to the femoral artery (measured with an anthropometer). Recordings of pulse waves at the carotid and femoral arteries were then assessed by applanation tonometry along with an electrocardiogram. The transit time was determined using the intersecting tangent method  and the Using the SphygmoCor device and its built-in software, we estimated central systolic and diastolic blood pressure, central pulse pressure, and central augmentation index from peripheral pressure waveforms recorded at the radial artery. Pulse wave analysis data with an operator index < 75 were excluded.Brachial systolic and diastolic blood pressure were measured after 10 min of rest in a sitting position with an automated blood pressure recorder . The blood pressure was measured three times and the average was used in the analysis. We estimated brachial pulse pressure as the difference between brachial systolic blood pressure and diastolic blood pressure.We tabulated characteristics of included individuals at baseline, trial-end and at follow-up by randomization group. Data are presented as medians (p25–p75) and proportions (%).We used linear mixed-effect models (as we accounted for cluster-randomization) to estimate the effect ) of randomization to multifactorial treatment on hemodynamic indices in an intention-to-treat approach. The models were adjusted for clustering at practice level and heart rate at the time of measurement (model 1) and furthermore adjusted for age and sex (model 2). The analysis of cfPWV was further adjusted for mean blood pressure at the time of measurement. We assessed any effect modification by sex by including an interaction term between sex and randomization group. To assess the internal validity of our study sample, we tabulated baseline characteristics and 13 years follow-up characteristics between participants who attended the clinical follow-up examination but did not have an assessment of cfPWV or central hemodynamic indices and our study sample. Furthermore, to assess potential selection bias, we examined cfPWV at five year by randomization group in those attending the 13 years follow-up examination.www.R-project.org).Calculations and graphs were made in R version 3.3.3  inclusion in the ADDITION trial. Measurements of hemodynamic indices were successfully obtained in 411 of the 586 attending participants and this group constitutes the study sample in the current study. Of these, 242 participants attended general practices originally randomized to deliver intensive treatment and 169 participants attended practices randomized to deliverer routine care  compared with the routine care group after adjustment for cluster randomization and heart rate. With further adjustment for age and sex, the cfPWV difference was 0.48 m/s (95% CI − 0.97 to 0.01 m/s) in favor of intensive treatment. Accounting for the effects of mean blood pressure the cfPWV difference between treatment groups attenuated further to 0.35 m/s lower (95% CI − 0.84 to 0.14 m/s) in the intensive treatment group. With adjustment for heart rate and cluster randomization, central mean arterial pressure was 2.14 mmHg lower (95% CI − 4.87 to 0.59 mmHg) and central augmentation index was 1.25% lower (95% CI − 3.28 to 0.78%) in the intensive treatment compared with the routine care group. In line with this, we found no statistically significant differences between treatment arms in other central or peripheral hemodynamic indices. After adjustment for age and sex, the associations attenuated, but all hemodynamic estimates still pointed in favor of intensive treatment   were highly similar to the characteristics of the study sample (those with measures of hemodynamic indices). However, participants in the study sample were a few years younger and were more often prescribed antihypertensive medications at inclusion  was sustained, but the difference between treatment arms was attenuated or lost [Overall, there was a reduction of most risk factors by the end of the ADDITION trial (at five year follow-up) with some differences between randomization groups . At 10 y or lost . In line or lost , 25.Our study points towards a small positive effect of multifactorial intervention on all hemodynamic indices. The marked attenuation of the difference in cfPWV observed in the study by further adjustment for the mediating effect of mean brachial blood pressure, supports the notion that blood pressure changes partly mediate the lower cfPWV, although, we did not have power to detect a marked difference between randomization groups in mean arterial blood pressure.The first line drugs for the management of peripheral hypertension both during the trial period and now are ACE inhibitors or ARBs . They afThe main strengths of the present study are its randomized design and long follow-up. We present a post-hoc analysis of measures that were not part of the registered primary or secondary end-points of the ADDITION-trial, but are conceptually closely related as intermediate measures. The study sample in this analysis is broadly representative of those attending the ADDITION-Denmark clinical examination 13 years after randomization but is selected in comparison to the baseline trial population. Previously, we have assessed differences between those attending the clinical follow-up and those who did not . Those aIntensive multifactorial treatment, including targeted blood pressure, lipids and HbA1c, does not only have a positive effect on aortic stiffness during intervention in screen-detected type 2 diabetes, it also has a sustained effect at least 8 years after intervention. Notably, any effect of intervention on other peripheral or central hemodynamic indices was less clear, although all estimates pointed in favor of intensive treatment. Lower cfPWV is associated with lower CVD risk. A recent meta-analysis has shown, that 1 m/s lower cfPWV is associated with 12% lower CVD risk and 9% lower CVD mortality . The difAdditional file 1: Table S1. Characteristics of the study sample at the end of the trial (by 5 years follow-up). Table S2. Baseline and at follow-up characteristics of persons with and without hemodymanic assessment at 13 years follow-up


The presence of acute kidney injury (AKI) in pre-eclampsia complicates treatment including; increasing length of hospital stay and a need to access services like dialysis which are largely expensive in resource-limited settings. We aimed to determine incidence and predictors of acute kidney injury among women with severe pre-eclampsia at Mbarara Regional Referral Hospital in southwestern Uganda.We carried out a hospital-based prospective cohort study from 16 November  2018 to 18 April 2019, among pregnant women with severe pre-eclampsia followed up in the hospital. We enrolled 70 mothers with severe pre-eclampsia and eclampsia; we excluded patients with a history of chronic kidney disease, chronic hypertension, and gestational hypertension. Data on socio-demographics, laboratory parameters, health system, obstetric and medical factors were collected. Baseline serum creatinine, complete blood count, and CD4 T-cell count were all done at admission (0-hour). A second serum creatinine was done at 48-hours to determine the presence of AKI and AKI was defined as a relative change of serum creatinine value at least 1.5 times the baseline  within 48 h. The proportion of women diagnosed with acute kidney injury among the total number of women with severe pre-eclampsia was reported as incidence proportion. Univariate and multivariate logistic regression was used to establish the association between acute kidney injury and severe pre-eclampsia.Incidence of acute kidney injury was high (42.86%) among women with severe pre-eclampsia. Antenatal care attendance was protective with an odds ratio of 0.14 , p-value 0.020 at bivariate analysis but had no statistical significance at multivariate analysis. Eclampsia was an independent risk factor for acute kidney injury. , p-value 0.014.The incidence of acute kidney injury in patients with severe pre-eclampsia is high. Eclampsia is an independent risk factor of acute kidney injury. The findings of this study highlight the urgent need for more research and better perinatal care for these women. Acute Kidney Injury (AKI) is defined as an abnormality of the excretory function of the kidney characterized by an accumulation of nitrogenous waste which includes urea and creatinine.Incidence and factors associated with acute kidney injury in women with severe pre-eclampsia vary. Severe renal impairment occurs frequently when pre-eclampsia is complicated by abruption placentae or hemolysis, elevated liver enzymes, and low platelet count (HELLP) syndrome . A histoThe most common cause of AKI in pregnancy is pre-eclampsia, which is worsened with eclampsia, which can ultimately lead to an increase in maternal morbidity and mortality . HoweverIn Africa, AKI is a challenging problem because of the burden of disease, the late presentation of patients to health care facilities, and the lack of resources to support patients with established AKI in many countries. A high The presence of AKI in pre-eclampsia complicates the treatment including increasing the length of hospital stay, the need to access services like dialysis which are largely expensive. It is therefore paramount to know the incidence and predictors of AKI in the setting of Mbarara Regional Referral Hospital in Southwestern Uganda in mothers admitted with pre-eclampsia. Knowing the incidence and predictors of AKI in this population will help increase awareness among healthcare providers and will contribute to improving service delivery in our settings.We prospectively enrolled70 participants on the maternity ward at Mbarara Regional Referral Hospital (MRRH) to determine the incidence and predictors of acute kidney injury in mothers admitted with severe pre-eclampsia. MRRH is a public regional referral hospital that serves as a teaching hospital for Mbarara University of Science and Technology. The hospital serves as a referral for the western region of Uganda but also receives patients from the neighbouring countries like Rwanda, Congo, and Burundi. The hospital offers specialized services, employs eleven obstetricians and 32 midwives together with resident doctors who perform over 10,000 deliveries annually. The hospital also can perform laboratory tests including chemistries, urinalysis, and ultrasonography among others. A resident nephrologist in the hospital can perform acute hemodialysis for patients who require the service.The study population consisted of all women having severe pre-eclampsia and eclampsia between November 2018 and April 2019. We included all women who were diagnosed with severe pre-eclampsia/eclampsia defined as high blood pressure (≥ 140/90mmHg) plus one or more of the following symptoms of severity . Women who had a history or documentation of chronic kidney disease, chronic hypertension and gestational hypertension were excluded. Eligible women were recruited prior to delivery until the sample size was attained. Consent was obtained and blood samples were taken for baseline renal function tests. Serum creatinine was measured using the kinetic colorimetric method (fixed rare) Jaffe reaction without deproteinization. After 48 h another sample was drawn for a 2nd creatinine. A relative change of serum creatinine value at least 1.5 times the baseline  within 48 h was considered AKI and patients were followed up by the study team while in hospital from the time of admission to when they were discharged. In this study, the KDIGO definition of AKI without urine criteria was used. Acute kidney injury was staged as follows: Non-AKI < 1.5 times change of the baseline creatinine, stage 1 as 1.5 to 1.9 times the baseline creatinine and stage 2 as 2.0 to 2.9 times the baseline and stage 3 which is defined as a change > 3.0 times the baseline creatinine. The definition of AKI in pregnancy has not been standardized. Different studies use different parameters (consensus definitions of AKI) which can range from a change in serum creatinine or the need for dialysis . The phyThe major outcome variable of the study was acute kidney injury.The primary exposure variable was severe pre-eclampsia while the secondary exposure variables were sociodemographic factors, healthcare system factors, obstetric factors which included gravidity/parity, gestation age at diagnosis of pre-eclampsia and admission to the hospital, postpartum hemorrhage, antepartum hemorrhage, eclampsia, HELLP syndrome and previous history of pre-eclampsia. The medical factors were a history of diabetes, and HIV infection.P-value < 0.5. Backward stepwise elimination was used to create the final model, and all variables with P-values < 0.05 in the final model were considered significant independent factors associated with acute kidney injury.Data was captured using Redcap, and exported to Excel. All analyses were performed using Stata software Version 13.0. Continuous variables such as age were presented as means and standard deviation for normally distributed variables or median and interquartile range for variables not normally distributed. The incidence proportion of AKI was derived as the proportion of women diagnosed with acute kidney injury among the total number of women with severe pre-eclampsia/eclampsia. Bivariate and multivariate logistic regression models were used to identify predictors of acute kidney injury. Predictor variables were variables with odds ratio. Additional variables were included if on bivariate analysis they demonstrated a correlation with the outcome of interest (acute kidney injury) with a A total of 4547 obstetrics and gynecology admissions were conducted during the study period, out of which 75 met the inclusion criteria. Among those eligible, some participants refused to participate while others took their own discharge from the hospital before official discharge. 70 participants were recruited.The mean age of our study participants was 27.5(± 6.4) years with 87.1% of them being married. 65.7% of the participants were housewives and 76.7% of the participants were referred from lower health facilities. A majority (85.3%) reported having attended antenatal care clinics. The commonest gestational age at delivery was 36 weeks and above. HELLP syndrome accounted for 27.1% of cases while 21.4% had eclampsia. The mean systolic blood pressure was 166 (± 28.5) while the diastolic was 108(± 16.8) mmHg. The mean baseline creatinine was 0.9 mg/dl (± 0.3). 68.6% of the participants had proteinuria and the mean hemoglobin level was 11.3 g/dl (± 2.4). The details of the participants’ baseline characteristics are shown in Table The incidence of acute kidney injury among women with severe pre-eclampsia/ eclampsia was 30/70 (42.86%) and is diagrammatically represented in Fig. None of the study participants had severe AKI and neither did any of them progress to severe AKI or require hemodialysis as represented in Fig. Acute kidney injury was staged as follows: Non-AKI < 1.5 times change of the baseline creatinine, stage 1 as 1.5 to 1.9 times the baseline creatinine and stage 2 as 2.0 to 2.9 times the baseline. We had no patient in stage 3 which is defined as a change > 3.0 times the baseline creatinine.Antenatal care attendance was protective with a crude odds ratio of 0.14 0.027, 0.73), p-value 0.020 in bivariate analysis but there was no statistical significance at multivariate analysis. Eclampsia was an independent risk factor of acute kidney injury , p-value 0.014. The details of the predictors of acute kidney injury among women with severe pre-eclampsia are shown in Table 7, 0.73, In this prospective study, our main goal was to establish the incidence and predictors of AKI among mothers admitted with severe pre-eclampsia to the maternity ward of Mbarara Regional Referral Hospital (MRRH). We found a high cumulative incidence of AKI of 42.86% and eclampsia as the main independent predictor of AKI, within 48 h after admission.Our findings were lower than what was reported among severe pre-eclampsia/eclampsia patients in Cameroon among whom 66.7% developed acute kidney injury. This could be because a majority ( 72.5% ) of the enrolled participants in the study done in Cameroon had eclampsia  comparedIn our study, eclampsia was the main independent predictor of AKI , p-value 0.014. This is in agreement with a prospective hospital-based study done in India which revealed that acute kidney injury was common among patients with eclampsia as high as 25–50% . This maIn our study, we noted that antenatal care attendance was associated with a reduction in the incidence of acute kidney injury compared to non-attendance; crude odds ratio 0.14, 95% C.I, 0.027, 0.73, p-value 0.02. However, this was not significant at multivariate analysis and there was no association between the number of times the mothers attended antenatal care and the incidence of AKI. There is evidence to suggest that women with severe pre-eclampsia who present with severe complications are likely to have attended ANC fewer times than those without major complications . This may be so because antenatal attendance is associated with early detection of disease and timely intervention due to the structured routine screening of blood pressure and urine protein during antenatal visits.However,some studies found no association between the number of times of antenatal care attendance and the severity of pre-eclampsia on presentation , 17. ThiThe inability to use measure more sensitive renal biomarkers for the diagnosis of AKI was one of our study limitations. Serum creatinine takes time to rise and may lead to late detection of AKI. The timing of sample collection in this study was done at admission and 48 h later. This may have prohibited some cases being identified as it is possible that AKI might have been detected even after 48 h and within 7 days from admission. Furthermore, the physiological decrease in serum creatinine in pregnancy compared with similarly healthy, non-pregnant individuals which occurs due to volume expansion and vasodilatation which ultimately leads to increased renal plasma flow and glomerular filtration rate resulting in an increased GFR [Urine output is not routinely monitored quantitatively unless in very sick or catheterized patients.The incidence of acute kidney injury in patients with pre-eclampsia/eclampsia is high (42.86%).The presence of eclampsia in patients admitted with severe pre-eclampsia is associated with the development of AKI within 48 h after hospitalization. We recommend a larger prospective study that will include the use of biomarkers for acute kidney injury and closer monitoring of mothers with eclampsia for features of acute kidney injury. It is also important to increase both clinician and patient awareness to minimize the risk of AKI in women with pre-eclampsia.


Objective: The issue of when to start treatment in patients with hyperuricemia (HUA) without gout and chronic kidney disease (CKD) is both important and controversial. In this study, Raman spectroscopy (RS) was used to analyze urine samples, and key genes expressed differentially CKD were identified using bioinformatics. The biological functions and regulatory pathways of these key genes were preliminarily analyzed, and the relationship between them as well as the heterogeneity of the urine components of HUA was evaluated. This study provides new ideas for the rapid evaluation of renal function in patients with HUA and CKD, while providing an important reference for the new treatment strategy of HUA disease.Methods: A physically examined population in 2021 was recruited as the research subjects. There were 10 cases with normal blood uric acid level and 31 cases with asymptomatic HUA diagnosis. The general clinical data were collected and the urine samples were analyzed by Raman spectroscopy. An identification model was also established by using the multidimensional multivariate method of orthogonal partial least squares discriminant analysis (OPLS-DA) model for statistical analysis of the data, key genes associated with CKD were identified using the Gene Expression Omnibus (GEO) database, and key biological pathways associated with renal function damage in CKD patients with HUA were analyzed.Results: The Raman spectra showed significant differences in the levels of uric acid (640 cm−1), urea, creatinine , proteins/amino acids , and ketone body  (p < 0.05). The top 10 differentially expressed genes (DEGs) associated with CKD  were identified. Compared with the differential peak positions analyzed by the OPLS-DA model, it was found that the peak positions of glutathione, tryptophan and tyrosine may be important markers for the diagnosis and progression of CKD.Conclusion: The progression of CKD was related to the expression of the ALB, MYC, IL10, PLG, REN, and FGA genes. Patients with HUA may have abnormalities in glutathione, tryptophan, tyrosine, and energy metabolism. The application of Raman spectroscopy to analyze urine samples and interpret the heterogeneity of the internal environment of asymptomatic HUA patients can be combined with the OPLS-DA model to mine the massive clinical and biochemical examination information on HUA patients. The results can also provide a reference for identifying the right time for intervention for uric acid as well as assist the early detection of changes in the internal environment of the body. Finally, this approach provides a useful technical supplement for exploring a low-cost, rapid evaluation and improving the timeliness of screening. Precise intervention of abnormal signal levels of internal environment and energy metabolism may be a potential way to delay renal injury in patients with HUA. Uric acid, an end product of purine, is generally produced from nucleic acids and purine compounds after a series of metabolic processes in the liver, and it is eventually discharged from the body through the kidneys. However, the process of uric acid excretion is complex as, after its filtration through the glomeruli, a series of reabsorption and re-secretion processes occur in the proximal renal tubules, the mechanisms of which are still not fully understood. Excessive concentrations of uric acid deposited in the kidneys and joints can not only lead to kidney stones and gout, but also cause cardiovascular diseases such as hypertension and atherosclerosis . ConsequHyperuricemia (HUA) is a metabolic syndrome caused by abnormal purine metabolism, and its diagnostic standard is internationally defined in terms of blood uric acid levels , female >357 μmol/L (6 mg/dL)) , The incIn recent years, an increasing number of researchers have been using Raman spectroscopy (RS) technology to distinguish the biomarkers of healthy subjects from those of patients at different stages of CKD . This tein vivo microenvironment. Then, combined with gene chip data technology, the Bioinformation Analysis microarray data about CKD were downloaded and sorted from the GEO (Gene Expression Omnibus) database to find the key genes involved in the development of CKD. The combination of the relevant biological peak positions obtained by Raman spectroscopy with the molecular targets suggested by key genes opened a new way for analyzing information on the urine environment of CKD patients with HUA. It is expected that the results would assist in the clarification of the pathological mechanism of CKD caused by HUA  of the General Hospital of Tianjin Medical University, China. Based on the results of the physical examination, 10 subjects with normal levels of serum uric acid were randomly selected as the control group, and 31 with asymptomatic hyperuricemia were selected as the test group. Of these, 35 were males and six were females, with their ages varying between 35 and 97 years. The serum uric acid levels were 133–415 μmol/L and 424–595 μmol/L for the control and test groups, respectively. For the test group, the improved estimated glomerular filtration rate (eGFR) suitable for the Chinese population was calculated according to the CKD-EPI two-level race equation .a: if female a = 144; if male a = 141b: if female b = 0.7; if male b = 0.9c: if female and Cr ≤ 0.7 mg/dl, c = −0.329, if female and Cr > 0.7 mg/dl, c = −1.209.The specific formula is as follows:If male and Cr ≤ 0.7 mg/dl, c = −0.411, if male and Cr > 0.7 mg/dl, c = −1.209.2), as it has higher specificity and is more useful for the evaluation of patients with normal or near-normal renal function.The CKD-EPI formula is currently recognized as an effective measure of CKD, especially for patients with GRF <60 mL/(min·1.73 m2), while 10 subjects were considered to be in the stage 2 CKD, with an eGFR between 60–89 (ml/min/1.73 m2), and 6 cases with an eGFR less than 60 (ml/min/1.73 m2) as assigned to the stage 3 CKD subgroup.In addition, the subjects in the test group were diagnosed and staged according to the clinical practice guidelines of the kidney disease outcome quality initiatives (K/DOQI) . As a rePatients were excluded from the study if they were taking drugs that affected uric acid metabolism (such as aspirin), had a previous history of primary kidney disease (such as glomerulonephritis), had complications arising from acute kidney injury, had received any renal replacement therapy or had urinary tract stones, gouty arthritis, severe edema, pleural effusion, or ascites as well as ketoacidosis. For this study, approval was obtained from the ethics committee of the General Hospital of Tianjin Medical University (IRB2022-WZ-132). All participating healthy individuals and patients provided written informed consent.The heights and weights of all subjects were measured, and blood was collected after 10 h of fasting. The blood samples were centrifuged to obtain the serum. Clinical and biochemical indices in the serum were measured with an automatic biochemical analyzer, while fresh urine samples were taken to determine the levels of the relevant urinary indices, including the urinary microalbumin creatinine ratio (ACR), urinary creatinine (UCR), urinary microalbumin , urine pH, and urine specific gravity (SG). Similarly, indices related to renal function such as uric acid (uric), urea (urea), and creatinine (crea) levels, were measured, together with other biochemical indicators, including low-density and high-density lipoprotein cholesterol (LDL-C and HDL-C), direct bilirubin (DBIL), total bilirubin (TBIL), triglycerides (TG), total protein (TP), globulin (GLO), glycosylated hemoglobin (HbA1c), alpha fetoprotein (AFP), alanine transaminase (ALT), carcinoembryonic antigen (CEA), albumin (ALB), glucose (Glu), and total cholesterol (TC). Routine blood indicators, including the mean corpuscular volume (MCV), red blood cell SD (RBC-SD), red blood cell CV (RBC-CV), red blood cell (RBC), hemoglobin (HGB), mean red blood cell hemoglobin content (MCH), mean red blood cell hemoglobin concentration (MCHC), hematocrit (HCT), lymphocyte absolute value (LYM#), lymphocyte percentage (LYMPH%), neutrophil absolute value (NEU#), neutrophil granulocyte (NEU), white blood cell (WBC), monocytes percentage (MON), monocytes absolute value (MON#), eosinophilic granulocyte percentage (EOS), eosinophilic granulocyte absolute value (EOS#), basophils percentage (BAS), basophils absolute value (BAS#), platelet large cell ratio (P-LCR), platelet crit (PCT), mean platelet volume (FL), platelet distribution width (PDW), and platelet count (PLT) were also measured.−1. For each group, measurements were taken at 5–10 different sites, with a resolution of 1 cm−1 and at the same time, the background noise was assessed by acquisition of the Raman spectrum of the quartz slide. Lastly, data processing, including smoothing, background removal, and baseline correction was performed with Labspec6 software. All spectra were normalized with reference to the 1,650 cm−1 Raman peak as the internal standard.Urine samples were uniformly collected and stored at −18°C before analysis. Subjects were then selected for Raman spectral analysis according to their blood uric acid levels. The frozen urine samples were thawed at room temperature, and after adding 5 μL to a glass slide, measurements were taken with a confocal Raman spectrometer (XploRA Raman microscope). In this case, the objective lens was 40 times, a 40-mW output power was applied, and a 785-nm laser was selected as the excitation light. Samples were also fixed to the XYZ three-dimensional platform for shooting using a 40 × 0.75 NA Nikon lens, while the laser was received applied to a spot size of about 2 × 2 μm. The other selected parameters included a single integration time of 250 s and measurements in the range of 600–1,800 cmR2 and Q2 were then used to evaluate the performance of the OPLS model. Under the zero hypothesis, the model was verified by randomly resampling 200 times with a random change of the Y matrix. In addition, potential biomarkers were identified from statistically significant Raman peak positions in the classification model using cluster and V + S analyses. Based on a comprehensive consideration of the correlation coefficient, loadings, distance from the center in the V + S diagram and other parameters, Origin software was used to process the relevant data. Peak positions of variable importance (VIP) > 1.0 were considered to have a significant impact on the model.SIMCA14.1 software was used to conduct supervised orthogonal partial least squares discriminant analysis (OPLS-DA) on the Raman spectral data from the urine samples from the control group as well as from patients with hyperuricemia and different CKD stages. The goodness-of-fit parameters http://www.ncbi.nlm.nih.gov/geo/) database were used, specifically, dataset GSE66494 (species: Homo sapiens). The data included data from kidney biopsy samples from 53 CKD patients together with kidney biopsy data from eight healthy controls, all of which included data on gene expression profiles. All samples were obtained using a GPL6480 Agilent-014850 Whole Human Genome Microarray 4 × 44K G4112F platform.Data from the NCBI GEO , Gene Ontology (GO) functional annotation as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out for key genes. The GO functions included three categories, namely molecular function (MF), cellular component (CC), and biological process (BP).Using the online tool DAVID , a protein interaction database, was used to explore the relationships between the proteins encoded by the DEGs using a protein-protein interaction network (PPI). The PPI results were imported and edited in Cytoscape 3.6.1 before analyzing the node scores within the network using network topology property index degree centrality. In this case, higher node scores reflected a greater likelihood of the node being a key node. Genes corresponding to the top 10 proteins (degree. TOP10) of the PPI network nodes were considered as hub genes with high connectivity in the network.STRING 11.0 , and the Kruskal Wallis test was used for comparisons between groups, with statistical significance set at −1. −1), protein, amino acid , ketone bodies , urea and creatinine , respectively. Similarly, In this study, a total of 258 Raman spectra of urine samples were obtained, and of these, 65 were for the control group, 93 were from stage 1 CKD of HUA patients, 62 were from stage 2 CKD of HUA patients and 38 were from stage 3 CKD of HUA patients. These results were presented in However, it was often difficult to distinguish between the urine components of CKD patients and the controls based only on the above spectral patterns and peak positions. Hence, it is necessary to further identify peak positions that could assist in differentiating between the control and CKD subgroups so that, along with the OPLS-DA method and the classification model established by statistical analysis, the peaks can act as potential biomarkers.2 on the y-axis was negative, indicating that the OPLS-DA model was reliable and not an over-fit (Twenty-four characteristic spectra (six in each category) were randomly selected from the four sets of urine Raman spectra, namely, those of the control group, stage 1 CKD with HUA, stage 2 CKD with HUA, and stage 3 CKD with HUA. The sample data were analyzed and compared by applying the supervised OPLS-DA. Permutation analysis showed that the intercept of Qover-fit . Under tover-fit . The cloover-fit .x-axis. On the other hand, the stage 1 CKD with HUA group was located on the negative half of the x-axis. At the same time, while the control group was found on the negative half of the y-axis, the stage 2 CKD with HUA group as well as the stage 3 CKD with HUA group were located on its positive half. These results reflected the differences between the control group and the CKD subgroups, allowing them to be clearly distinguished. Thus, it was established that OPLS-DA could distinguish between the spectral data of urine from the four groups, thereby providing conditions for analyzing their material composition. In the OPLS-DA score map , urea/creatinine , ketone body , and uric acid (640 cm−1), respectively. These characteristic peak positions played a key role in the identification of the four groups of samples.The OPLS-DA loading map  were the most influential and biologically significant peaks in the model and played an important role in the identification of CKD samples, reflecting that the urine ketone bodies and some protein/amino acid contents of non-hyperuricemic people are higher than those of patients with CKD combined with HUA . However, there was a significant difference in the peak at 1,643 cm−1 in the urine of stage 1 and stage 2 CKD patients (p = 0.007), and significant differences were also observed when comparing stage 1 CKD patients with HUA with those in stage 3 CKD (p = 0.001). Lastly, no significant differences between stage 2 CKD patients with HUA and stage 3 CKD patients (p > 0.05) were noted.There were no significant differences in the intensities of the 640, 642, 828, 1,556, 1,585, 1,587, 1,596, 1,603, 1,608, 1,615, and 1,706-cm−1), urea/creatinine , proteins/amino acids , and ketone bodies  in stage 1 CKD patients with HUA were significantly different from those of control patients (p < 0.05). Likewise, the peak intensities of uric acid (640 cm−1), urea/creatinine , proteins/amino acids , and ketone bodies  differed significantly between patients with stage 2 CKD and HUA and the controls (p < 0.05). Compared with the control group, the peak intensities of uric acid (640 cm−1), urea/creatinine , proteins/amino acids , and ketone bodies (828 cm−1) in patients with stage 3 CKD patients and HUA were also statistically significant (p < 0.05). See The peak intensities of uric acid (640 cmp = 0.000).Comparisons of the peripheral blood indices of renal function showed that the blood uric acid level for the control group was 259.0 ± 88.6 μmol L (range 133–415), while that of the stage 1 CKD group with HUA was 486.3 ± 50.3 μmol/L (range 425–557). Similarly, the blood uric acid level in the stage 2 CKD group with HUA was 486.0 ± 45.5 μmol/L (range 429–573), and the blood uric acid level in the stage 3 CKD group with HUA was 484.2 ± 67.4 μmol/L (range 424–595). Compared with the control, the serum uric acid levels were significantly higher in patients in the different CKD stages , and the creatinine levels in the urine were also higher than those in the control group (p = 0.010). The eGFR, blood creatinine, and urine pH level were not significantly different (p > 0.05), although stage 2 CKD patients with HUA had higher serum creatinine levels compared with the control group (p = 0.013). At the same time, there was no significant differences in the eGFR, urea, urine microalbumin creatinine ratio, urine creatinine, urine microalbumin, and urine pH levels (p > 0.05). The levels of eGFR and creatinine in stage 3 CKD patients with HUA were significantly higher than those in the control group , and the urine pH was significantly lower than that in the control group (p = 0.002). There were no significant differences in blood urea, urine microalbumin creatinine ratio, urine creatinine, and urine microalbumin (p > 0.05).The blood urea, urine microalbumin:creatinine ratio, and urine microalbumin levels in stage 1 CKD patients with HUA were lower those for the control (p = 0.000), but no significant differences were observed in terms of the serum uric acid, urea, serum creatinine, urine microalbumin creatinine ratio, urine creatinine, urine microalbumin, and urine pH (p > 0.05). The eGFR of stage 1 CKD patients with HUA was also higher compared with stage 3 CKD (p = 0.000), and in this case, the blood urea, blood creatinine, urine microalbumin creatinine ratio, and urine microalbumin were significantly lower compared with stage 3 CKD , although no significant difference were present in blood uric acid, urine creatinine, and urine pH (p > 0.05). Finally, comparing the eGFR of stage 2 and stage 3 CKD patients with HUA indicated that the eGFR values were higher in the stage 2 patients (p = 0.000). At the same time, the blood urea and urine microalbumin were lower than those of stage 3 CKD patients . There was no significant difference in blood creatinine, urine microalbumin: creatinine ratio, urine creatinine, and urine pH (p > 0.05). See A significantly higher eGFR was noted for stage 1 CKD patients with HUA compared with those in stage 2 (p < 0.05). (See p > 0.05). See When compared with other clinical data, age, sex, total protein, albumin, globulin, total bilirubin, glucose, total cholesterol, low-density lipoprotein, alpha fetoprotein, carcinoembryonic antigen, red blood cell count, hemoglobin, hematocrit, number of variation lines of red blood cell distribution width, standard deviation of red blood cell distribution width, percentage of neutrophils, absolute value of neutrophils, percentage of lymphocytes and percentage of monocytes, significant between-group or within-groups difference in the absolute values of monocytes and eosinophils were observed (05). See . In termA total of 581 DEGs were identified from the GSE66494 chip data, of which 432 were significantly upregulated  and 149 p-values. Combined with the identification of the top 10 genes, the main enriched pathways in biological process (BP) were oxygen transport and protein hydrolysis. In terms of cell components (CC), extracellular space, extracellular region, exosomes, and blood microparticles were mostly enriched, while for molecular functions (MF), the DEGs were mainly involved in functions associated with extracellular matrix structural components and oxygen junctions. The KEGG analysis showed significant enrichment in pathways associated with African trypanosomiasis signaling pathway, renin angiotensin system signaling pathway, and the malaria signaling pathway  and urine clinical data  were unable to distinguish between CKD patients. Kidneys have a strong reserve compensation capacity, and CKD is a slow and gradual process. Although the etiology is different, with increased age, the anatomical features and physiological metabolism of the kidneys undergo degenerative changes to varying degrees. Consequently, the GFR values of elderly patients tend to show a progressive downward trend. Subjects with primary renal diseases were not included in this study, while those who had undergone physical examination in the physical examination center were randomly selected for recruitment. At the same time, patients with basic renal disease were excluded. Therefore, the age of the patients with stage HUA combined with stage 3 CKD was higher, with a median of 80.5 years. Similarly, the median age of patients with HUA and stage 2 CKD was 76.5 years, while for the control, the median age was 90.50 years. There were no significant statistical differences between the three groups. The median age of patients with HUA combined with stage 1 CKD was 45 years old, which was significantly younger than the other three groups.Previous studies on HUA have shown , that th−1 represents the peak position of uric acid, and the significantly lower levels in patients with CKD and HUA suggest that uric acid excretion is abnormal in patients with HUA. The determination of uric acid in the urine by Raman spectroscopy simplifies the determination process of uric acid and provides the possibility for the rapid and simple detection of uric acid.This study suggests that the pH of urine from non-HUA patients was significantly higher than that in the HUA group, with metabolic acidosis associated with the entire CKD process. More specifically, the pH of urine from stage 3 CKD patients with HUA was significantly lower than that of stage 1 and 2 CKD patients or even that of the control group. Although uric acid is more soluble in urine than in blood, the urine pH nevertheless influences the conversion rate of uric acid to urate as well as the solubility of urate in urine. For instance, in an alkaline environment, uric acid will be converted into urate because of its high solubility, thus promoting renal excretion and alleviating potential damage to the kidneys . In the −1 represents the peak position of urea. Urea levels in the urine of patients with stage 1 and stage 2 CKD are lower compared with the control group, while the urea level in stage 3 CKD patients did not differ significantly from that of the control group, which is itself consistent with the levels present in the peripheral blood among the groups. Regarding creatinine, the peak occurs at 1,706 cm−1 with the levels being significantly lower for all CKD subgroups in comparison with the control.In the Raman spectrum of urine, 1,608 cmp < 0.01, see p = 0.000, see −1, which is produced by ketogenic diets. β-Hydroxybutyric acid can not only be used as an energy supply fuel, but also can generate ATP through metabolism, and can also generate cellular signals  in the CKD group was lower than that of the control group. In stage 1, CKD patients with HUA without obvious renal function impairment, have abnormal energy metabolism. At this time the β-hydroxybutyric acid concentrations are high, and the presence HUA in this period indicates that the body is protecting itself. However, CKD progression disrupts the internal microenvironment, reducing the protective and regulatory effects of HUA and thus inducing damage to the kidneys.Hyperuricemia patients often present with complications resulting from dyslipidemia, especially from triglyceride (TG) abnormalities. Even in healthy people, it has been found that the level of blood uric acid is positively correlated with the triglyceride and cholesterol levels. Hence, increases in the levels of TG, TC, and LDL-C are seen as an independent risk factor for hyperuricemia. There is an apolipoprotein E2 allele in patients with hypertriglyceridemia and hyperuricemia, which reduces uric acid excretion by the kidneys, and the increased lipoprotein may lead to the reduction of blood uric acid clearance. It is speculated that TG may be a common factor that influences metabolism in hyperuricemia, and as such, controlling blood lipid metabolism disorders can effectively improve the process of hyperuricemia . High tr signals . This ce signals . Insuffi signals . Ketone ALB, MYC, IL10, PLG, REN, and FGA. Of these, ALB  is a protein synthesized by mammalian liver cells. As the most important protein in human plasma, it has important physiological functions such as the maintenance and stabilization of plasma osmotic pressure and participating in material transport. ALB gene expression is tissue-specific, that is, albumin can only be synthesized by hepatocytes, while the ALB gene in other tissues is not expressed. Albumin may be the main target of oxidative stress, and the anti-inflammatory and nephroprotective effects of dexamethasone are achieved by increasing two enhancers at the transcription start site of the ALB gene (PLG) encodes plasminogen, an enzyme secreted by the liver, which is converted into plasmin by a variety of enzymes when combined with blood clots. PLG plays an important role in tissue remodeling during growth and development, inflammation, body injury, and carcinogenesis. It can help degrade the extracellular matrix and matrix metalloproteinases   in blood vessels, adrenal glands, heart, central nervous system, and other tissues  . As a pr tissues , and pla tissues . REN genpoptosis . In CKD tion see . MYC is loidosis . In CKD osis see .−1 peak position representing glutathione/tryptophan, the 1,556-cm−1 peak position representing tryptophan, the 1,585-cm−1 peak representing C-C bonds, and the 1,587-cm−1 peak representing tyrosine played important roles in the identification of CKD samples. Glutathione is the most important antioxidant in human body. After oxidation of the carbon-carbon double bond, the double bond breaks. Tryptophan and tyrosine can then act as glycogenic and ketogenic amino acids and participate in the body’s energy metabolism. Although there were no significant differences between the CKD subtypes with HUA, the peak positions related to antioxidation and energy metabolism in the urine of stage 3 CKD patients with HUA showed an increase, suggesting that there may be poor energy conversion and weakened antioxidant capacity in stage 3 CKD patients , purine , glutathione/tryptophan (828 cm−1 peak), tryptophan , carbon-carbon bonds (158 cm−1 peak), and tyrosine  is simple and easy to use. It can assess the presence of abnormal energy metabolism in patients with HUA and dynamically determine the degree of renal function damage in patients with HUA, thus providing a reference for finding a reasonable time for uric acid intervention. The limitation of this study is that the sample size was small, and a large number of carefully designed prospective studies are still needed to confirm the time sequence and causal relationship between hyperuricemia and CKD to lay a foundation for exploring the changes in the molecular environment of the urine of CKD patients with HUA. Interventions to modulate the internal environment and energy metabolic signaling levels may to delay renal injury in patients with HUA.HUA is a risk factor for CKD, and it is thus very important to control blood uric acid to delay the progression of kidney disease. There are a large number of asymptomatic HUA patients in the clinic. The cause of HUA is related to purine metabolism. Purine plays an important role in maintaining human physiological function. Excessive uric acid lowering treatment will inevitably lead to abnormal purine metabolism in the body. Therefore, there is no unified view on when to start the intervention treatment of hyperuricemia. In Japan, for patients with stage 3 CKD with glomerular filtration rate (eGFR) < 60 mL minid level . Using uThe innovation of this study lies in that, firstly, compared with traditional methods, Raman spectroscopy technology is simple, fast, and accurate for the capture of biomolecular “fingerprints” in the urine of patients with HUA; a single integration time for a spectrum is only 250 s, no expensive reagents and substrates are needed, and the cost is low. Secondly, the urine sample is easy to obtain and does not harm the patient and can thus be used as a supplement for the clinical detection of renal system diseases. Thirdly, the combination of Raman spectroscopic data and bioinformatics analysis explored the genetic basis relating to oxidative stress, inflammatory reactions, and thrombosis associated with CKD progression. Fourthly, comparing the OPLS-DA model analysis established by massive biochemical data, establishing the identification model of CKD population combined with HUA will help improve the timeliness of renal system disease screening and reduce the economic burden of patients. Fifthly, the results of the combination of Raman spectroscopic technology and clinical data can be developed into home portable instruments to monitor specific indicators as an auxiliary tool for kidney disease inspection, which is of great significance for the early assessment of patient prognosis and the reduction of the economic burden of patients with end-stage kidney disease.Compared with traditional technologies, Raman spectroscopy has the advantages of being non-invasive, having easy access to specimens, and providing real-time reflections of body conditions. It is convenient for repeated sampling to monitor the dynamic development of diseases, and Raman data provide a large amount of information that can quantitatively reflect the concentrations of substances, which is of great clinical significance for the early screening stage of diseases. In the past, Raman spectroscopy has been used to study blood samples, but rarely urine samples. At present, the analysis of urine components of asymptomatic patients with HUA and CKD is still undocumented. At present, few studies have combined the use of Raman spectroscopy with clinical data, nor of Raman spectroscopy with bioinformatics analysis. This study is thus innovative in combining Raman spectroscopy, clinical data, and bioinformatics analysis to more comprehensively analyze the metabolic characteristics and pathogenesis of patients with HUA combined with CKD, for a comprehensive analysis of the characteristics of these patients, allowing an easy means of early detection, intervention, and treatment, thus reducing the economic burden on patients and society.To sum up, it is very important to start treatment before HUA patients have developed gout symptoms and before complications arise due to CKD, especially as HUA patients may have abnormal energy metabolism. Raman spectroscopy was applied to interpret the heterogeneity of the urine environment of HUA patients. At the same time, combined with an OPLS-DA model, clinical and biochemical data of HUA patients was mined to explore the close relationships between the progression of CKD and oxidative stress, inflammatory reactions, and thrombus as well as to explore the peak intensities of β-hydroxybutyric acid, purine, glutathione, tryptophan, and tyrosine can help to determine the degree of renal function damage in CKD patients with HUA and provide a reference for finding a reasonable time for uric acid intervention. The collection of urine samples is simple and easy. Monitoring the changes of urine microenvironment of CKD patients with HUA by Raman spectroscopy can provide help for early detection of changes in human internal environment, provide a new idea for exploring a low-cost and rapid assessment of renal function of patients with HUA and CKD, and also improve the timeliness level of CKD disease screening, reduce the economic burden of patients, It provides a useful technical supplement for the accurate diagnosis of chronic kidney disease. Precise intervention of abnormal signal levels of internal environment and energy metabolism may be a potential way to delay renal injury in patients with HUA.