Machine learning (ML) could have advantages over traditional statistical models in identifying risk factors. Using ML algorithms, our objective was to identify the most important variables associated with mortality after dementia diagnosis in the Swedish Registry for Cognitive/Dementia Disorders (SveDem). From SveDem, a longitudinal cohort of 28,023 dementia-diagnosed patients was selected for this study. Sixty variables were considered as potential predictors of mortality risk, such as age at dementia diagnosis, dementia type, sex, body mass index (BMI), mini-mental state examination (MMSE) score, time from referral to initiation of work-up, time from initiation of work-up to diagnosis, dementia medications, comorbidities, and some specific medications for chronic comorbidities . We applied sparsity-inducing penalties for three ML algorithms and identified twenty important variables for the binary classification task in mortality risk prediction and fifteen variables to predict time to death. Area-under-ROC curve (AUC) measure was used to evaluate the classification algorithms. Then, an unsupervised clustering algorithm was applied on the set of twenty-selected variables to find two main clusters which accurately matched surviving and dead patient clusters. A support-vector-machines with an appropriate sparsity penalty provided the classification of mortality risk with accuracy = 0.7077, AUROC = 0.7375, sensitivity = 0.6436, and specificity = 0.740. Across three ML algorithms, the majority of the identified twenty variables were compatible with literature and with our previous studies on SveDem. We also found new variables which were not previously reported in literature as associated with mortality in dementia. Performance of basic dementia diagnostic work-up, time from referral to initiation of work-up, and time from initiation of work-up to diagnosis were found to be elements of the diagnostic process identified by the ML algorithms. The median follow-up time was 1053 (IQR = 516–1771) days in surviving and 1125 (IQR = 605–1770) days in dead patients. For prediction of time to death, the CoxBoost model identified 15 variables and classified them in order of importance. These highly important variables were age at diagnosis, MMSE score, sex, BMI, and Charlson Comorbidity Index with selection scores of 23%, 15%, 14%, 12% and 10%, respectively. This study demonstrates the potential of sparsity-inducing ML algorithms in improving our understanding of mortality risk factors in dementia patients and their application in clinical settings. Moreover, ML methods can be used as a complement to traditional statistical methods. Mortality risk is higher in patients with dementia (PWD) compared to dementia-free subjects3. Risk factors such as age, Body Mass Index (BMI), sex, Mini-Mental State Examination (MMSE) score, and comorbidities are significantly associated with mortality risk in dementia patients9. These patients have common comorbidities for which treatment could be a critical determinant of survival11. Despite decades of research, there may be factors related to mortality risk in dementia which remain undiscovered to date. The identification of these additional risk factors related to death in dementia and understanding of their prognostic role is essential for life and health-care planning and patient care12.Dementia is a major and growing public health concern with a substantial increase in prevalence projected in the future13. ML algorithms could be applied for prediction of mortality risk and time to death and contribute to our understanding of risk factors and their interactions during dementia progression14. ML studies can give more accurate results than traditional statistical models since they offer more flexible alternatives in handling large-scale and heterogeneous data15. From a clinical viewpoint, achieving a high prediction accuracy in and of itself is not the primary goal. Rather, discovering the most important risk factors is often the primary clinical question. There are several ML strategies to develop time to death models. A recent study showed that Boosted Cox regression outperformed Cox proportional hazards and random forest algorithms for early detection and tracking of Alzheimer's disease16.Regularized Machine learning (ML) can handle large-scale data and, if properly trained, can give accurate results, especially in a sparse model18. A limitation to such traditional statistical methods is that they do not identify the most important variables among all relevant variables, relying rather on a pre-existing suspicion of linear associations or hypothesis and testing only those associations. The ML algorithms can select a sub-set of the variables, rank them in order of importance  and then identify associations that were not suspected according to a priori hypothesis. The aim of this study was to identify variables associated with mortality risk in PWD and rank them in order of importance using sparsity-inducing ML classifiers. The aim was also to develop multivariable models using these variables of mortality risk and time to death. We also compared the predictive power of the classifiers to find the best model for predicting mortality risk in dementia.Our previous studies using traditional statistical methods  on the Swedish Registry for Cognitive/Dementia Disorders (SveDem) showed that age, sex, residency, population density, comorbidity burden, BMI, MMSE score, number of medications used and certain specific medications were significantly associated with time to deathAmong 28,023 patients, 16,273 (58.07%) were women, and the mean age and BMI at the time of dementia diagnosis were 78.6 (SD = 7.85) years and 24.8 (SD = 4.39), respectively. The most common dementia type was Alzheimer's disease (AD) 14,464 . The median MMSE score was 22.0 (Interquartile range (IQR) = 18–25) at the time of the diagnosis. The median time from referral to initiation of work-up and from initiation of work-up to diagnosis were 29 (IQR = 14–56) days and 57 (IQR = 26–100) days, respectively. Cardiovascular disease, cancer, and depression were the most frequent comorbidities, present in 69%, 34.1%, and 28.42%, respectively. The median number of medications taken by the patients at the time of the diagnosis was 2 (IQR = 1–2). Additionally, cholinesterase inhibitors, statins, diuretics, and antidepressants were prescribed in 47.51%, 34.11%, 28.71%, and 27.1% of the included patients, respectively. A total of 66.29%  patients had died by December 31, 2018. The median follow-up time was 1053 (IQR = 516–1771) days in surviving and 1125 (IQR = 605–1770) days in dead patients , each classifier was combined with the three sparsity-inducing penalties . Then, we tested the combinations of the three sparsity-inducing penalties and three standard classifiers which resulted in nine different combinations to find the best performing models. The heatmap of the area under receiver operating characteristic curve (AUROC) values for each combination of the feature selection methods and the classifiers is shown in Fig. P-value = 0.249, SVM vs. NN; P-value = 0.498, LR vs. NN; P-value = 0.816, LR vs. SVM).Three subsets of the selected variables were used for binary classification of the mortality risk by LR, SVM, and NN with 100-times repetition in the testing set. Based on the classification metrics used for evaluating the predictive performance, the classifiers showed an approximately similar overall performance Table . Accuracp-values did not reach statistical significance. A higher age at diagnosis significantly increased the hazard rate of mortality (hazard ratio (HR) = 1.059, 95% CI 1.056–1.063). In contrast, higher BMI, and MMSE score significantly decreased the hazard rate of mortality in patients . Taking diuretics  and cholinesterase inhibitors (HR = 0.724 95% CI 0.689–0.761) significantly decreased the hazard rate of mortality in these patients  days and the censoring  rate was 33.71%. Among the twenty selected variables by all three algorithms, 15 variables were identified as highly important variables to predict time from diagnosis to death using the multivariable CoxBoost model. Among these variables, age at diagnosis (23%), MMSE score (15%), sex 14%), BMI (12%), and Charlson Comorbidity Index (CCI) (10%) were the most frequently selected by the CoxBoost model. All fifteen variables had significant effects on survival time among the patients, except atorvastatin and statin prescription in the year preceding dementia diagnosis where the %, BMI . There was no significant difference between both clusters of surviving patients based on the identified variables. The optimal cut-point of dendrograms to find the number of clusters was done by maximizing the variability of the observations between clusters. The comparison of the dendrograms between dead and surviving patients showed that there was no similarity or correlation between dead and surviving patients based on the twenty selected variables (Cophenetic correlation coefficient = −0.00018). Therefore, the results of the clustering confirmed that these selected variables could discriminate between dead and surviving patients overall.  are consistent with previous cohort studies on large-scale populations and our previous work on SveDem. Previous studies on SveDem showed that age, sex, residency, population density, comorbidity burden, MMSE score, BMI, number of medications used, and certain specific medications were significantly associated with time to death using Cox-Proportional Hazards model which C-index was between 0.65 and 0.7218. According to the comparison of the dendrograms between dead and surviving patients, there was no correlation between dead and surviving clusters based on the similarities of the selected variables (Cophenetic correlation coefficient = −0.00018). This means that these variables  were significantly different between dead and surviving patients. This unsupervised clustering algorithm confirmed the discrimination power, validated the findings of the classification algorithms, and strengthened the results.In this large national cohort study, three standard classification algorithms were applied and evaluated. These algorithms used different sparsity-inducing penalties to identify the most important variables associated with mortality risk in PWD. The study aimed to identify previously unsuspected variables which were associated with mortality risk in these patients, to rank them in order of importance, and to develop models to predict mortality risk. We found that the diagnostic model generated by SCAD-SVM achieved a greater predictive performance but the differences were not significant between SCAD-SVM with MCP-NN and Elastic-net LR. The twenty selected variables were the same in all three algorithms. Both SVM and NN are margin-based classifiers and can model non-linear decision boundaries. LR had a similar classification performance in our study. A previous study found LR to have similar classification performance to NN and SVM in predicting diabetes risk22. The NN algorithm is based on a more powerful and adaptive nonlinear equation form and can learn complex functional associations between the input and output data23. As a classifier, LR is much more popular than SVM and NN classifiers because it is easier to interpret. However, achieving a high prediction performance is not the primary goal, rather, identifying the most relevant variables is often the primary computational question. Therefore, variable selection methods  could be of great help by automatically connecting with many classification algorithms to avoid overfitting25. Variable selection methods can achieve the best subset of the most relevant variables for prediction and classification. As an important phase of classification and prediction, variable selection also improves predictive power while avoiding overfitting. Wrapper methods evaluate subsets of variables by training and testing the model on different combinations of variables. Wrapper methods are computationally expensive because they involve training and testing the model multiple times for different variable combinations. They are often used when the number of variables is relatively small. On the other hand, embedded methods are a group of variable selection methods carrying out variable selection within learning classifiers to achieve better computational efficiency and performance compared to wrapper methods. In other words, embedded methods are less computationally expensive and less prone to overfitting than the wrapper methods26. Regularization methods are effective embedded variable selection methods that provide an automatic variable selection within learning classifiers (e.g. LR and SVM)28. With different penalties, several sparsity variable selection methods can be applied. LASSO as the L1-norm penalty is considered as one of the most popular procedures in the class of sparse penalties. However, the result from this penalty is inconsistent. To overcome this limitation, Elastic-net penalty, as a convex combination of the LASSO and ridge penalty, can be helpful. Experiment and simulation studies have demonstrated that the Elastic-net often outperforms the LASSO for variable selection in classification task29. The MCP is very similar to the SCAD penalty. Both MCP and SCAD are non-convex or concave and enjoy the oracle property and unbiased estimates30. MCP performs well when there are many rather sparse groups of predictors. The main limitation of MCP and SCAD is when the non-zero coefficients are clustered into tight groups; as they tend to select too few groups and make insufficient use of the grouping information31.Classification and prediction models play significant roles in data analysis to build a diagnostic or prognostic model. There are many algorithms for classification and prediction tasks in the machine learning field. Among them, SVM and NN are two standard algorithms for classification in many situations 6. This “obesity paradox” or reverse epidemiology has frequently been described in dementia and other conditions32. Preceding studies also showed that living situation was associated with mortality risk of PWD18. Unsurprisingly, higher MMSE score was significantly associated with lower mortality risk, as consistently shown in prior studies on SveDem and other cohorts34. In a previous study on SveDem, MMSE score was a significant predictor of mortality with HR = 0.964 (95% CI 0.962–0.967) per point of MMSE (≈4% risk decrease) among PWD registered in primary care and HR = 0.952 (95% CI 0.949–0.955) among PWD registered in a memory clinic18. This effect size is the same as the one calculated here with the Boosted Cox model . Comorbidities which were significantly associated with time to death in this study included diabetes and cancer. PWD with higher CCI also had significantly higher mortality risk with an effect size similar to the one reported by traditional multivariable Cox-proportional hazard regression performed on this same cohort18. However, previous studies found that mortality risk of PWD was higher among PWD suffering stroke37, which was identified as an important predictor for mortality by the Elastic-net LR algorithm. Despite some of these variables being familiar from previous research, the order of importance was sometimes surprising, for example, the high importance of BMI relative to other predictors.Using different machine learning algorithms, we found that sociodemographics, cognition as measured by MMSE, comorbidities and drug utilization were the most important predictors of mortality risk. It is perhaps simpler to compare the results from the survival algorithm  which most closely resemble the published literature using Cox-proportional hazards regression. We observed that high age, male sex, low BMI and MMSE predicted mortality risk of PWD. This result was in line with previous studies using data from SveDem, in which higher BMI was significantly associated with lower mortality risk38. That study was propensity-score matched and included a somewhat older cohort which might explain the discrepancy. The use of diuretics might reflect comorbidities  which could explain the association. We also found that consuming renin-angiotensin system inhibitors two or more years prior to dementia diagnosis significantly increased mortality risk of PWD, which is at odds with our expectations. It is possible that in this patient selection, the chronic use of renin-angiotensin inhibitors was confounded by the indication for treatment. Different types of drug utilization previously associated with mortality risk of PWD include glucose-lowering drugs, cholinesterase inhibitors, antipsychotics, anticholinergics, atrial fibrillation medications, and antidepressants44. The total number of medications at time of diagnosis and number of dementia medications were identified by all three algorithms matched our previous studies17.Regarding drug utilization among PWD, we observed that the use of diuretics or rosuvastatin  was significantly associated with lower mortality risk. In another recent study using SveDem data, incident users of statins had a significantly lower risk of all-cause death  compared to non-users18.Dementia type was identified by the Elastic net-LR and MCP-NN as a significant and important predictor of mortality risk and was also a strong predictor of mortality risk and survival time in our previous studies18. The clinical utility of this study lies in identifying several new predictors associated with mortality risk and which are potentially modifiable, since they are related to waiting lists. Also interesting is the ranking of predictors in order of importance, which can potentially help prioritize interventions and identify patients at risk. This may be a starting step to developing an individual model for each patient, as part of personalized medicine.What is most interesting is that the ML algorithm detected variables which we had not thought to explore in our cohort (factors without an “a priori” or pre-specified hypothesis). This was the case for the time between referral and initiation of diagnostic work-up and time from diagnostic work-up to diagnosis. These variables were identified by the algorithm with 3% and 2% of selection frequencies, respectively, and warrant further examination in future studies since they suggest a deleterious effect of long waiting lists on survival. The C-index for the CoxBoost model was 0.69 showing acceptable calibration in the testing set. However, a prior study from SveDem using forward selection of covariates arrived at a C-index of 0.705 including age, MMSE score, CCI score, dementia type, sex, living situation, and drugs in a Cox-proportional hazards model46. In addition, the Swedish National Patient Register (NPR) was also employed which covered all inpatient and specialist medical diagnoses. Furthermore, the data on dementia subtypes from SveDem was a unique feature of this study. Using different linear and non-linear ML algorithms, reducing omitted-variable bias by application of three different sparsity-inducing penalties and confirmation by an unsupervised clustering algorithm are other advantages of this study. However, there are some limitations that should not be neglected. Missing data is a weakness of this register-based study. Due to the high number of included predictors, only 28,023 patients  had complete data on all sixty potentially associated variables. We conducted a sensitivity analysis with different methods of imputation. We chose to keep the complete-case analysis as the main finding of the study because of the high percentage of imputed values and because the assumption for the imputation were not met, which could introduce bias. The NPR includes all inpatient medical diagnoses and outpatient care in Sweden but does not cover diagnoses in primary care. Thus, the prevalence of diseases, as well as the influence of Charlson Comorbidity Index on the algorithms might have been underestimated here. Moreover, the Swedish Prescribed Drug Registry (PDR) covers all prescription drugs sold in pharmacies in Sweden but does not include over-the-counter drugs or those administered during hospitalization.The most notable strength of this study was the large size of studied cohort and linkage of national registers. SveDem is the largest clinical dementia register in the worldIn this national dementia cohort study , we applied different standard ML classifiers with three sparsity-inducing penalties to consistently identify important variables associated with mortality risk. The ML algorithms not only replicated some of the previously known findings but also ranked variables by importance, showing that higher age, male sex, low MMSE and low BMI were the most important predictors of death. They also identified new important variables such as performance of basic dementia diagnostic work-up, time of referral to initiation of work-up, time of initiation of work-up to diagnosis, and the use of diuretics. This study highlights the value of employing ML algorithms as a valuable addition to our analytical arsenal. ML can complement traditional statistical methods, particularly when dealing with large-scale, sparse, and heterogeneous data. Overall, this study demonstrates the potential of ML algorithms in improving our understanding of mortality risk factors in patients with dementia and their potential application in clinical settings.48. SveDem can be merged with other registries using the Swedish unique personal identification number. This study included 60 variables potentially related to mortality status from SveDem and other registries and selected from the literature and our clinical knowledge and understanding of the registries: information on the patient’s demographics, living arrangements, date of diagnosis, co-morbidities, and medications taken at the time of the dementia diagnosis (baseline). Medication usage history was obtained from the Swedish Prescribed Drug Registry (PDR). The PDR was established in July 2005 and contains data on all prescribed drugs dispensed at pharmacies in Sweden49. Comorbidities were obtained from the Swedish National Patient Registry (NPR) which covers data on health care episodes in inpatient and outpatient specialist care and includes four different groups of data; demographic/patient data, geographical data, administrative, and medical data50. The date of death was ascertained from the Swedish Cause of Death Registry until December 31, 2018. From 80,004 patients registered in SveDem between 2007 and 2018, we included 28,023 persons diagnosed with no missing data on any of the sixty potentially predictors for a complete case analysis (CCA) in the ML algorithms. To avoid selection bias, missing at random was checked . The TRIPOD statement was reported for good reporting of the developing and validating multivariable prediction models in this study (The Swedish Registry for Cognitive/Dementia Disorders Registry (SveDem) is a national quality-registry established in 2007 with the aim to register all patients with dementia in Sweden at the time of diagnosis and conduct follow-ups to improve dementia diagnostics and careis study .51 and includes a structured clinical interview, an evaluation of the physical and psychological situation of the patient, an interview with a knowledgeable informer, MMSE, clock test, blood analyses, and neuroimaging. The last four of these are included as variables in SveDem and combined into the variable “basic dementia work-up” to be followed as a quality indicator for care. The study outcome was all-cause death. Patients were followed from the dementia diagnosis date to death or the end of follow up (31 December 2018).Based on the literature review, recommendations from the clinicians (SGP and ME), and omitting poor quality/bad implementation variables, sixty variables were considered as potential predictors of mortality in the ML algorithms. These variables included age at dementia diagnosis, sex, dementia types, BMI, MMSE score, living situation , residency (at home vs nursing home), performance of the basic dementia diagnostic work-up, types of diagnostic units , time from referral to initiation of work-up, time from initiation of work-up to diagnosis, dementia medications , total number of medications taken at time of dementia diagnosis, Charlson Comorbidity Index (CCI), comorbidities, and some specific medications for chronic comorbidities . The basic dementia work-up is defined by the Swedish Board of Health and Welfare52. To avoid omitted-variable bias (OVB) , regularization methods as effective embedded variable selection methods were applied by three different penalties: Elastic-Net, Smoothly Clipped Absolute Deviation (SCAD), and minimax concave penalty (MCP). All these overcome the limitations of traditional variable selection methods; for example, stepwise logistic regression requires large sample sizes and is more computationally expensive than these methods. Elastic-net linearly combines L1 and L2 penalties, uniting the strengths of both Least Absolute Shrinkage and Selection Operator (LASSO) (L1) and ridge (L2)28. This is important because LASSO penalty is suitable for variable selection but not for group selection and it tends to give biased estimations. We suspected that our exposure variables were correlated and LASSO tends to select only one among correlated variables. So, group selection methods  were important in our study28. Elastic-net penalty is suitable for multi-collinearity and grouped selection situations  and it has good performance for simultaneous estimation and variable selection 28. Elastic-net penalty has a strictly convex loss function and, therefore, a unique solution/global optimum and parameter estimation (oracle properties)53. On the other hand, SCAD and MCP penalties are non-convex optimizations which means that there are more than one local optimum and they are computationally harder than LASSO or Elastic-net. Additionally, we can only obtain a local optimum with these penalties and not the global optimum. MCP, SCAD and Elastic-net all assign zero-coefficients to non-identified variables. SCAD and MCP have less biased estimates than Elastic-net for the non-zero coefficients, i.e. the selected variables54. Moreover, MCP’s advantage over SCAD is giving less biased coefficients in sparse models55. Both MCP and SCAD penalties outperform Elastic-net based on their less biased estimation of the coefficients while Elastic-net has the advantage of giving a unique parameter estimation. MCP and SCAD penalties suffer when the identified variables are clustered into tight groups as they tend to select too few groups and make insufficient use of the grouping information31. All these penalties have some tuning parameters. Estimation of the best value for tuning parameters is important to decide how many variables are to be selected. We applied 100-times repeated 10-fold cross-validation technique to estimate the tuning parameters and establish consistency in the variable selection processing in the training set53.For the variable selection process, different sparsity-inducing penalties were used to remove irrelevant or redundant variables. There are generally three main categories of variable selection methods: wrapper methods, filter methods, and embedded methods. Wrapper methods evaluate subsets of variables by training and testing the model on different combinations of variables. The wrapper methods are often used when the number of variables is relatively small due to being computationally expensive. Filter methods assess variables independently of the model and consider their correlations with the outcome variable. The main disadvantage of filter methods is that they ignore variable dependencies. Embedded methods incorporate the variable selection process into the model building algorithm itself. These methods typically use regularization techniques to select the importance of certain variables 56. To overcome this issue, regularization methods  could be helpful57. Finally, we used sigmoid kernel for the SVM and Softmax activation function with one hidden layer and 10 hidden neurons for the NN algorithm in this study. Each classifier was combined with all three penalties/regularization methods to perform variable selection and binary classification simultaneously. The importance values in each model were calculated based on the Gini index with normalization.For the binary classification of mortality risk, we used three standard classifiers including LR, SVM, and NN. LR is one of the most common classifiers used in epidemiological studies and is based on a linear decision boundary. When non-linear relationships exist, a nonlinear decision boundary may result in better overall performance. SVM and NN are designed to generate more complex decision boundaries. In other words, both classifiers can detect nonlinear relationships between outcome and predictors. SVM  has the advantage of taking non-linear associations and mapping them into linear boundaries improving interpretability, whereas NN has several hidden layers and, hence, interpretation of its classification decision is difficult. NN requires more complex computations to train the algorithm compared with LR and SVM. SVM can include varying degrees of non-linearity and flexibility by using different kernel functions. Unlike LR and NN, classification results of SVM are purely dichotomous whereas LR and NN give a probability of class membership. Overfitting is less of an issue in LR because LR is less sensitive to training samples compared to NN and SVM algorithms. In contrast, NN is more complex and, thus, more susceptible to overfitting than LR and SVM58. All statistical analyses were performed by “glmnet”, “penalizedSVM”, “neuralnet”, “ncvreg”, and “pROC” R packages61.The final step in the mortality classification was to check for overfitting. This was done using the holdout method where all samples in the dataset were randomly divided into 66.6%  and 33.4% (9341 samples) as training and testing sets, respectively. Accuracy (ACC), balanced error rate (BER), area-under-curve measure associated with receiver-operating-curve (AUROC), sensitivity and specificity were reported for the test set as the classification metrics of the performance on the testing samples. Statistical comparison of AUROCs among the different classifiers was performed by the DeLong test to identify the best algorithmic combinations of binary classifiers and sparsity-inducing penalties for the mortality risk prediction63. Previous studies have shown that CoxBoost has a high goodness of fit compared to a Cox proportional hazard model where there are many predictors; since it allows mandatory covariates with unpenalized parameter estimates64. Boosting is a popular iterative technique used in survival analysis with a high flexibility for the selection of the candidate variables and ease of interpretation. Boosting is also applicable in many situations where the assumption of proportional hazard (PH) does not exactly hold65. In our case, we used “CoxBoost” R package66. The model was trained by 2/3 samples  and tested on 1/3 samples . The concordance index (C-index), as an evaluation metric of survival models, is a weighted average of the area under time-specific ROC curves (time-dependent AUC)67. The C-index and Gonen and Heller's Concordance Index (GHCI) were reported to assess the performance of the survival model in the testing set68.CoxBoost was used to develop a robust survival model based on the selected variables in all combinations of classifiers and sparsity-inducing penalties . This survival model can be applied to fit the sparse survival models and this enables us to consider some mandatory covariates in the model based on the likelihood-based boosting69. For clustering of the patients in surviving and dead groups, the data were divided into two datasets of surviving and dead patients. Then, the agglomerative clustering algorithm was run separately on each dataset to identify clusters of the patients based on the similarities in the identified variables. The clustering results for the surviving and dead patient groups were compared to confirm the presence of considerable differences based on the identified variables between dead and surviving patients. More technically, this hierarchical clustering algorithm was performed by “binary” distance measure and the “ward.D2” method. We compared dendrograms in dead and surviving clusters by the Cophenetic correlation coefficient and permutation test/10-times70. The “cluster”, “dendextend”, and “factoextra” R packages were applied for clustering, comparison of dendrograms, and visualization, respectively71.To validate the identified variables by an unsupervised clustering algorithm, agglomerative hierarchical clustering and Rand index were applied to assess discrimination power of the classifiers that match well with surviving and dead patient clustersAll statistical analyses were performed using R software version 4.1.1 . The significant level was considered at a level of 0.05. Figure This project was approved by the Swedish Ethical Review Authority with the reference number (#2021–0043) and was performed based on the Declaration of Helsinki guidelines. Patients were informed about registration in SveDem at the time of their dementia diagnosis and gave informed consent to obtain information on their registration any time and could withdraw consent later. Data were de-identified by Swedish authorities before delivery to the research team.Supplementary Information.


Observational studies have reported that dietary renal acid load has an important role in insulin resistance and metabolic factors. The aim of the present study was to assess the effect of a low renal acid load diet (LRALD) on blood pressure, lipid profile, and blood glucose indices in patients with type 2 diabetes.n = 40) or control (n = 40) groups, for 12 weeks. Both groups received a balanced diet and a list of nutritional recommendations based on healthy eating behaviors. In the LRALD group, food items with low renal acid load were prescribed. Primary outcomes including: fasting blood glucose (FBG), hemoglobin A1c (HbA1c), fasting serum insulin, quantitative insulin sensitivity check index (QUICKI), homeostatic model assessment for insulin resistance (HOMA) and secondary outcomes including: weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL), and high-density lipoprotein (HDL). were measured at baseline and end of the study. The present trial was registered at IRCT.ir (IRCT20130903014551N5).In this parallel randomized clinical trial, 80 patients with type 2 diabetes were randomly assigned to the LRALD (n = 35 in control group and n = 36 in LRALD). Weight (P < 0.001), body mass index (P < 0.001), FBG (P < 0.001), HbA1c (P < 0.001), SBP (P = 0.004), and TG (P = 0.049) were reduced and HDL (P = 0.002) was increased in both groups, compared with baseline. After adjusting for baseline values, DBP (P = 0.047) was reduced in the LRALD group compared with control group. Results had no changes after using intention to treat analysis.Seventy subjects completed the study .The online version contains supplementary material available at 10.1186/s12937-023-00849-6. Type 2 diabetes is one of the most prevalent non-communicable metabolic disorders, that results in a high rate of morbidity and mortality, worldwide , and, byDietary intake is a determinant of acid production and may influence on acid–base balance in the body ; indeed,One of the main indicators used to estimate the acid renal load is potential renal acid load (PRAL), which refers to the intestinal absorption of five nutrients, including protein, potassium, calcium, phosphorus, and magnesium. A positive PRAL indicates acid-inducing and a negative score indicates alkali-inducing properties . Net endSeveral observational studies have been conducted to investigate the relationship between dietary acid load and glycemic control and metabolic indices. A prospective cohort study conducted on middle-aged subjects reported that a high acid-load diet was associated with a higher risk of type 2 diabetes . A crossAlthough the number of observational studies that assessed the association between dietary acid load and glycemic control and metabolic factors are informative, no well-designed interventional study has been performed to detect the effect of dietary renal acid load on metabolic factors in type 2 diabetic patients. Therefore, in the present study, we aimed to evaluate the effects of a low renal acid load diet (LRALD) on, blood glucose, and insulin resistance as primary outcomes and anthropometric variables, blood pressure and lipid profiles as secondary outcomes in patients with type 2 diabetes.2). Subjects who started using a new blood glucose-lowering drug, changed the dose of medications, or had a diabetic ketoacidosis attack during the study were excluded. The number of subjects was calculated based on fasting blood glucose (FBG) by using following formula: n = 2 [(Z (1-α/2) + Z(1-β))2 × S2] / Δ2, where α = 0.05 (type I error), β = 0.20 (type II error), Δ = 15.63 mg/dl and S = 32.91 mg/d [n = 35 in each group). Accordingly, we recruited 40 subjects in each group at baseline, to account for loss to attrition. The aims and details of the study were individually explained for each volunteer. All participants signed an informed written consent forms before study commencement. This study was approved by the Research Council and Ethical Committee of the School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran and the Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. Present trial was registered at IRCT.ir (IRCT20130903014551N5).This parallel randomized clinical trial was conducted from June to September 2020. Adults with type 2 diabetes were recruited from a governmental type 2 diabetes center. Individuals were included if they were: 1) between 20 to 65 years old, 2) type 2 diabetic patients not on insulin therapy, 3) not pregnant or lactating, 4) not on glucocorticoids and 5) not underweight (body mass index (BMI) > 18.5 kg/m.91 mg/d . TherefoBoth groups were recommended a balanced diet modified for type 2 diabetic patients (such as carbohydrate counting) Energy intake was calculated for normal weight and overweight/obese subjects using current body weight and adjusted ideal body weight, respectively. Energy requirement was calculated using the Mifflin St Jeor Equation . The dieg/d),and \documentPotential confounding variables in this study were physical activity and food intake.Six, one-day (4 weekdays and 2 weekends), food diaries were completed by participants at baseline and during the study. The validity and precision of the dietary record is high and it is often considered as a reference method in validation studies. Nevertheless, we checked all dietary records to complete unclear and incomplete reports by each participant. Also, we excluded dietary records that reported < 800 or > 4200 kcal energy intake per day. We guided participants to how complete a food record by several images regarding portions sizes. Also, a completed food record was provided for each participant as a sample. We checked all dietary records to complete unclear and incomplete reports by each participant. Food diaries were converted to the macro/micronutrients by Nutritionist IV using the USDA database , 19.Participants were asked to complete 4 daily physical activity records during the study. Physical activity was calculated using Metabolic Equivalent per hour per day (MET.hd) . Patientth week, where subjects wore lightweight clothes and were unshod, using a standard scale, to the nearest 0.1 kg. Height was measured at baseline, using a wall-mounted stadiometer, with participants standing upright and unshod. Seated blood pressure was measured using a mercury sphygmomanometer, after 10 min of rest. The systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded by the first sound and the fade of the sound, respectively.Body weight was measured at the baseline and on the 12In the early morning, a fasting (12 h) blood sample was drawn. Serum was separated by centrifuging at 3,000 × g for 10 min. Enzymatic colorimetric method was performed to measure FBG, serum concentration of triglyceride (TG) and total cholesterol (TC) . Similarly, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) were measured after blocking other cholesterol containing components by photometric methods. Glycosylated hemoglobin (HbA1c) was assessed using ion exchange chromatography method. Fasting serum insulin was measured by enzyme-linked immunosorbent assay (ELISA) . To estimate insulin resistance, Quantitative insulin sensitivity check index (QUICKI) and homeostatic Model Assessment for Insulin Resistance (HOMA) were calculated by following formulas :\documenWe considered age, sex, body mass index and physical activity as potential confounding variables. Previous studies revealed that age was related to the lipid profile  and blooa priori alpha level of 0.05.Normality of the data was checked using the Kolmogorov–Smirnov test and Q-Q plot, and results showed that the distribution of HOMA-IR, QUICKI, and TG was not normal. Therefore, we used the log transformed version of these variables. Chi-square tests were used to compare qualitative variables between the LRALD and control groups. Quantitative variables were reported as percentages. To compare baseline and endpoint values within each group, Paired T test analysis was used. Quantitative variables were compared between two groups using Independent Student t-test. To adjust for confounding variables , analysis of covariance (ANCOVA) was applied. To report primary and secondary outcomes, both per-protocol and intention to treat (ITT) analysis were used. Missed data were treated according to linear regression method. Continuous data were reported as mean ± standard deviation. The log-transformed variables were reported as geometric mean ± standard deviation. All data analyses were conducted using SPSS version 21 statistical software, with an n = 3), taking a new drug (n = 1), and not following the prescribed diet correctly based on the patient's own confessions (n = 1). Four subjects were excluded from LRALD group due to positive COVID-19 test (n = 2), heart attack (n = 1), and low adherence to the prescribed diet based on the patient's own confessions (n = 1). Finally, data of 71 patients (n = 35 in control group and n = 36 in LRALD) were statistically analyzed. The number of the subjects who completed the study was greater than minimum required sample size reported in method section.A flow diagram of the study procedure is illustrated in Fig. General characteristics of the subjects are reported in Table Comparison of dietary intake during the study between LRALD and control groups is shown in Table As shown in Table Blood glucose indices showed that the changes in FBG and HbA1C were significantly changedwithin both groups. Insulin, HOMA-IR, QUICKI and LDL were significantly decreased compared with baseline in the control group. Analysis were repeated after using ITT method Table . NeverthWeight, BMI,, SBP, HDL and TG were significantly changed within both groups. In the LRALD group, DBP was significantly decreased after intervention. LDL was significantly decreased compared with baseline in the control group. After adjusting for baseline measurements, the comparison of the final values in the two groups showed that a LRALD marginally decreased DBP compared with the control diet Table . SimilarIn the present study, two dietary interventions were compared in type 2 diabetic patients. The most important innovation of the present study was the utilization of dietary acid load as a nutritional intervention. a finding of the present study was a significant reduction in DBP after adherence to a LRALD compared with control intervention. Also, SBP was significantly reduced after following a LRALD compared with the beginning of the study. While, in the control group, only a decrease in SBP was observed. Indeed, these findings indicated that a LRALD had more beneficial effects than a usual diabetic diet on blood pressure in patients with type 2 diabetes. The prevalence of hypertension in patients with type 2 diabetes is notably prevalent compared with healthy individuals ; indeed,A possible mechanism of the decreasing impact of a LRALD on blood pressure is the presence of abundant phenolic compounds in plants found in high amounts in this diet . The hypPrevious studies have reported findings similar to results of the present study regarding the effect of a LRALD on blood glucose indices. A cross-sectional study in Japan found that there was no correlation between dietary acid load, FBG and HBA1c . Indeed,The findings of the current study regarding the effect of an LRALD on blood pressure has been confirmed by previous studies. A cross-sectional study reported that PRAL and NEAP were positively associated with DBP in men and SBP in women , whilst The present study showed a no significant effect of LRALD on lipid profile, similar to a previous study which found no relationship between NEAP and HDL, LDL, and TC . It shouThree dietary patterns, including LRALD, DASH, and Mediterranean diet, have numerous similarities; for example, high-fat cheeses, red meat, and egg yolks are limited in all three diets. Instead of red meat, the consumption of legumes and chicken and fish in DASH and LRALD is recommended, also, olive oil is recommended in these three dietary patterns. Sodium restriction is also encouraged in all three diets . TherefoAlthough we have provided a novel addition to literature, which may be a practical relevance to prescribing clinicians and patients, there are limitations that should be acknowledged. One of the limitations of this study is the lack of examining of individuals' compliance with the intervention via biomarker. Assessment of compliance with the LRALD requires measuring urine pH over 24 h, however, collecting 24-h urine sample is difficult and it may affect the reliability of the results . TherefoThis study showed that a LRALD had beneficial effects on blood pressure in type 2 diabetic patients compared with the usual diabetic diet, but its effect on blood glucose control factors and lipid profile was not significant.Additional file 1: Supplementary 1. Dietary recommendations for control and LRAL group.Additional file 2: Supplementary 2. Potential renal acid load score of foods.


It is essential to understand the underlying pathophysiological mechanisms of preeclampsia cerebral complications. This study aimed to compare the cerebral hemodynamic effects of magnesium sulfate (MgSO4) and labetalol in pre-eclampsia patients with severe features.Singleton pregnant women who suffered from late onset preeclampsia with severe features were enrolled and subjected to baseline Transcranial doppler (TCD) evaluation and then randomly assigned to either the magnesium sulfate group or labetalol group. TCD to measure middle cerebral artery (MCA) blood flow indices including mean flow velocity (cm/s), mean end-diastolic velocity (DIAS), and pulsatility index (PI) and to estimate CPP and MCA velocity were performed as basal measurements before study drug administration and at post-treatment one and six hours after administration. The occurrence of seizures and any adverse effects were recorded for each group.Sixty preeclampsia patients with severe features were included and randomly allocated into two equal groups. In group M the PI was 0.77 ± 0.04 at baseline versus 0.66 ± 0.05 at 1hour and 0.66 ± 0.05 at 6 hours after MgSO4 administration  also the calculated CPP was significantly decreased from 103.3 ± 12.7mmHg to 87.8 ± 10.6mmHg and 89.8 ± 10.9mmHg  at 1 and 6 hours respectively. Similarly, in group L the PI was significantly decreased from 0.77 ± 0.05 at baseline to 0.67 ± 0.05 and 0.67 ± 0.06 at 1 and 6 hours  after labetalol administration. Moreover, the calculated CPP was significantly decreased from 103.6 ± 12.6 mmHg to 86.2 ± 13.02mmHg at 1 hour and to 83.7 ± 14.6mmHg at 6 hours . In terms of changes in blood pressure and the heart rate, they were significantly lower in the labetalol group.Both magnesium sulfate and labetalol reduce CPP while maintaining cerebral blood flow (CBF) in preeclampsia patients with severe features.The institutional review board of the Faculty of Medicine, Zagazig University approved this study with the reference number (ZU-IRB#: 6353-23-3-2020) and it was registered at clinicaltrials.gov (NCT04539379). Preeclampsia is a multi-system disorder of pregnancy characterized by widespread vascular dysfunction. One of the most prominent organs to be affected is the brain. Indeed, central nervous system (CNS) manifestations such as headache, visual disturbances, alterations in consciousness, and seizures are considered severe features of the disorder and significantly contribute to maternal morbidity and mortality –3. Many Magnesium sulfate (MgSO4) has been used for seizures prevention in severe preeclampsia for many years with demonstrated efficacy in eclampsia prevention and treatment –10. Its Labetalol is a nonselective, competitive β-adrenergic blocker and a selective, competitive α1-adrenergic blocker (ratio of blockade 7:1 respectively). It produces a rapid dose-dependent decrease in blood pressure without causing reflex tachycardia or a significant reduction in heart rate. Furthermore, labetalol has many important non-antihypertensive effects that may be beneficial in preeclampsia. It has an antiplatelet aggregation action, a thromboxane-reducing effect, and a fetal lung maturation-accelerating influence , and curSeveral previous TCD studies reported an increase in CPP, and growing evidence supports cerebral over perfusion theory and subsequent cerebral edema as a leading theory for cerebral injuries in preeclampsia patients with severe features , 14–16, 2. Informed written consent was obtained before study enrollment from the parturients or their legal guardian. The institutional review board of the Faculty of Medicine, Zagazig University approved this trial with the reference number (ZU-IRB#: 6353-23-3-2020) and it was registered under clinicaltrials.gov (NCT04539379).This prospective randomized comparative clinical study was conducted in the surgical intensive care unit; Zagazig University Hospitals between October 2020 to February 2022 on singleton pregnant women who suffered from late-onset preeclampsia with severe features and aged 21–45 years old with a body mass index ≤ of 35 kg/mParturients who were diagnosed to have mild preeclampsia and those with a history of preexisting heart disease, atrial fibrillation or any rhythm abnormality, pulmonary disorders, allergy, or contraindications to either magnesium sulfate or labetalol and exposure to any of these study medications within 24 hours before the study enrollment as well as those with inadequate temporal window were excluded from this study.Before study enrollment, all parturients were subjected to history taking, clinical examination, and investigated by complete blood picture, coagulation profile, liver functions test, and kidney functions test with the full examination by an obstetrician not involved in this study to diagnose those with severe preeclampsia who were immediately admitted and managed in the surgical intensive care unit. On physical examination, special attention was given to document vital signs ; cardiac and chest condition and exclude contraindications to the study medications. The goal and endpoints of the study were discussed, to clarify the advantages and possible side effects of the study medications.*109/L, liver enzymes elevation to twice the upper limit normal concentration with severe persistent right upper quadrant or epigastric pain not attributed to another diagnosis and not responding to medications, doubling of the serum creatinine concentration or its increase more than 1.1mg/dl in the absence of other renal disease, visual disturbances, and/or headache unresponsive to medications [Preeclampsia was diagnosed according to the American College of Obstetricians and Gynecologists (ACOG) criteria defining preeclampsia as persistent elevation of systolic blood pressure (SBP) > 140mmHg and/or diastolic blood pressure (DBP) > 90mmHg after 20 weeks of gestation with significant proteinuria and preeclampsia with severe features was diagnosed depending on the presence of one or more of the following: Systolic blood pressure elevation ≥ 160mmHg or diastolic blood pressure ≥ 110mmHg measured on two occasions at least 4 hours apart, presence of pulmonary edema, decrease in the platelet count less than 100,000Following ICU admission and preeclampsia severity features confirmation, all parturients fulfilling the inclusion criteria were subjected to baseline TCD evaluation and then randomly allocated into 2 equal groups to receive one of the study medications.https://www.randomizer.org/).Randomization was performed by sealed opaque envelopes containing random numbers generated by an online application  available in 20mL ampoules containing 100mg labetalol (5mg/mL). Starting the infusion with 20mg/h and then titrate to obtain and stabilize the targeted blood pressure by adjusting the infusion as required every 15–30min to a maximum dose of 160mg/hr. When clinically appropriate after stabilization of blood pressure it was discontinued by weaning over 1–2 hours.The target blood pressure in the two groups was SBP between 110 and 140mmHg, and DBP between 60 and 90mmHg maintained for at least 4 readings within 60 min.Patients in both groups with persistent hypertension were treated with continuous intravenous infusion of 5 µg/min nitroglycerine with increasing the dose of 5 µg/min every 5 min until reaching the therapeutic goal.Patients in the labetalol group who convulse were treated with MgSO4 with a loading dose of 4gm intravenously over 20min then MgSO4 intravenous infusion was continued at a rate of 1gm/h for 24h and if convulsion occurs in the MgSO4 group, Patients were continued to receive MgSO4. Other management of all patients were according to the standard protocol in the ICU.Measured TCD parameters including mean flow velocity (cm/s), mean end-diastolic velocity (DIAS), and pulsatility index (PI) as well as the calculated cerebral perfusion pressure (CPP) and MCA velocity were recorded as basal measurements before administration of the study drugs then at post-treatment one and six hours after drug administration.Transcranial doppler assessment was performed between the time of documented severe preeclampsia and study medication administration as a baseline measurement and then it was repeated post-treatment at one and six hours after drug administration.All subjects were rest in left lateral position  then after contact gel application, the 1–5 MHz ultrasound probe of Siemens Acuson X300 machine was placed between the ear and lateral orbital margin above the zygomatic bone on the temporal squama  to identify the middle cerebral artery (MCA). Measurements were performed bilaterally whenever possible, and the average value was calculated and recorded. If only one vessel is insonated, this value was used. All the measurements were taken by the same physician who is experienced in neurosonology and blinded to the study purpose.The middle cerebral artery TCD measurements were the mean velocity (MV), the mean end-diastolic velocity (DIAS), and the pulsatility index (PI). The cerebral perfusion pressure (CPP) was estimated using the equation described by Aaslid et al which was validated in pregnant females . CPP = .Middle cerebral artery velocity (MCA velocity) which is the middle cerebral blood flow component of the CPP formula (mean velocity / (mean velocity – diastolic velocity) was extracted and recorded to examine the impact of cerebral blood flow versus peripheral blood pressure on CPP changes.The following data were collected for each patient: Age, Body mass index (BMI), and gestational age. Basal blood pressure and heart rate were assessed then monitored continuously and it was recorded at the time of each TCD measurement.TCD of MCA parameters including the mean velocity (MV), the mean end-diastolic velocity (DIAS), and the pulsatility index (PI) as well as the calculated cerebral perfusion pressure (CPP) and MCA velocity were recorded as basal measurements before administration of the study drugs then at post-treatment one and six hours after drug administration.The need for other antihypertensive drugs e.g., nifedipine, nitroglycerine, hydralazine was recorded for each group.The occurrence of seizures in each group was monitored and managed. Also, any adverse effects of the study drugs such as hypotension (A systolic blood pressure of less than 90mmHg or diastolic of less than 60 mmHg), bradycardia (resting heart rate of under 60 beats per minute), persistent hypertension (Patients with persistent blood pressure above the target blood pressure), nausea and vomiting were noted and recorded.Using the (open Epi) program, the sample size was calculated to be 60 cases divided into 2 equal groups, (30 subjects) in each group. This was done based on assuming that the middle cerebral artery means velocity before and after labetalol administration in preeclampsia patients was 65 ± 10 versus 64 ± 12cm/s  at a conThe Collected data were statistically analyzed using Statistical Package for Social, Science software . Continuous variables with a normal distribution were reported as mean ± SD and range. Categorical variables were summarized as frequencies and percentages. Numerical data were evaluated using independent T-test, repeated measures analysis of variance (ANOVA), and Least Significant Difference (LSD) was used to detect a significant difference between every 2 separate groups, while qualitative data were evaluated by Chi-square test (χ2). P values < 0.05 and < 0.001 were considered statistically significant and highly statistically significant respectively.Sixty-five pregnant females suffered from preeclampsia were eligible for enrollment during our study period. From them 5 patients were excluded . Thus, sixty consented singleton pregnant females who suffered preeclampsia with severe features were included and randomly allocated into two equal groups Fig. . The patThe middle cerebral artery TCD measurements including the MV, DIAS, PI as well as the calculated CPP and MCA velocity showed no statistically significant difference between both groups at all times of measurements Table . While cThere was a statistically significant difference between the two studied groups regarding systolic, diastolic blood pressure, and heart rate after 1 hour of drug administration as it was found to be significantly higher among group M compared to group L with no statistically significant difference observed at other times of measurements between the two groups Fig. , 3, 4. HThe persistence of hypertension and the need to add another antihypertensive drug with the studied medication was statistically significantly higher in the magnesium group compared to the labetalol group with the other adverse effects showed no statistically significant difference in both groups. No statistically significant difference was found between both groups regarding seizures occurrence, seizures occurred in 7 cases in the labetalol group versus 4 cases in magnesium groups  Table .The most common cause of maternal mortality in preeclampsia is neurological complications. Hence, it is essential to understand the underlying pathophysiological mechanisms of these cerebral complications. Alteration of cerebral autoregulation and CPP in preeclampsia is a leading theory . SeveralOur study evaluated magnesium sulfate and labetalol medications, which are commonly used in our preeclampsia patients, with the CPP and CBFV endpoints by using the TCD.In the current study, both MgSO4 and labetalol were found to decrease CPP significantly at 1 and 6h after administration, with no significant change in the middle cerebral artery blood flow indices  indicating that the CPP reduction is due to the decrease in the blood pressure and not due to change in CBFV. Additionally, PI was found to be significantly decreased at 1 and 6h after administration of either MgSO4 or labetalol. However, the extent of CPP reduction and the blood flow indices did not differ significantly when labetalol was compared to MgSO4.Currently, severe preeclampsia and eclampsia are treated with magnesium sulfate  and it iHowever, our findings of no significant change in MCA blood flow indices after magnesium sulfate administration question its role as a cerebral vasodilator. In accordance with our findings, Hatab et al. used a velocity-encoded phase-contrast magnetic resonance imaging technique and found no change in cerebral blood flow or the diameters of the middle and posterior cerebral arteries after 6-gram magnesium sulfate loading dose infusion in preeclampsia patients . In addiThe pulsatility index has been recognized to have a linear relationship with the intracranial pressure (ICP) and it is considered as an indirect estimate of ICP. Also, PI was reported to represent intracranial compliance and distal cerebral vascular resistance . In the Believing that CPP reduction might have a key role in the management of preeclampsia patients, our study findings could support this theory. Since MgSO4 administration decreased CPP while maintaining CBF in preeclampsia patients with severe features and elevated baseline CPP in our study,This discovery may also be a proposed mechanism of action for MgSO4 in seizure prevention.MgSO4 is well-known to obviously prevent eclamptic seizures and has been used for decades; it is currently recommended as the drug of choice for eclampsia prophylaxis .The same decrease in CPP with no effect on CBF in severe preeclampsia patients was also found after labetalol administration in the current study. The previous studies evaluating the effects of antihypertensive drugs on maternal cerebral hemodynamics of preeclampsia patients are scarce , 26, 27.The findings of our study showed that the mean values of SBP, DBP, and HR were significantly lower in the labetalol group after 1 hour of drug administration compared to the MgSO4 group and that parturients in the MgSO4 group were more likely to require an additional antihypertensive drug to achieve blood pressure target. Regarding the occurrence of seizures, there were no statistical difference between the two groups, seizures in the labetalol group was reported in 7 cases (23.3%) versus 4 cases (13.3%) in the magnesium group. 2 patients in the labetalol group was found to have renal impairment and they were adequately managed with no seizures occurred and no need to add MgSO4 which is known to require frequent monitoring of its serum levels in renal impairment patients. Rates of other adverse effects did not differ significantly between labetalol and MgSO4 groups.MgSO4 has been shown in previous studies to have an antihypertensive effect in patients with severe pregnancy-induced hypertension , 30. HowGiven the recognized role of cerebral autoregulation impairment and the hypertensive encephalopathy pathophysiological mechanism, and the fact that the challenge in severe preeclampsia patients is to reduce blood pressure and CPP without jeopardising CBF, our findings support the evidence of MgSO4’s ability to reduce CPP while maintaining CBF as a potential mechanism of action in severe preeclampsia patients with elevated baseline CPP.This CPP normalizing effect of MgSO4 was comparable with that of labetalol, which was found to reduce CPP with no effect on CBF in our enrolled patients. Therefore, labetalol, which has been used for many years in preeclampsia patients, is both effective and safe for controlling blood pressure and reducing CPP with no effect on CBF in these patients.Using labetalol with no magnesium sulfate was supported by several previous studies. Walker in his experience report demonstrated that labetalol was the only antihypertensive drug used in over 500 preeclampsia patients with no MgSO4 administration and only one of these patients had a seizure that may be due to inadequate blood pressure control . Also, WOne of the main strengths of our study is that its prospective randomized comparative design comparing the cerebral hemodynamic effects of the two most commonly used drugs in preeclampsia patients. We suppose that the presented cerebral hemodynamic data add to the increasing evidence supporting the cerebral over perfusion pathophysiologic mechanism of cerebral injuries in preeclampsia with severe features.It is interesting to note that our study has several limitations including the small sample size that clearly is insufficient to address the safety issues. However, we believe that labetalol which has been used for many years is both effective and safe for controlling hypertension in preeclampsia. Secondly, the difficulty of accurately predicting preeclampsia patients with elevated CPP based on blood pressure alone. Although the CPP and blood pressure are obviously linked, they are not directly correlated in preeclampsia as abnormal cerebral autoregulation plays a vital role . MoreoveIn conclusion, in preeclampsia patients with severe features both magnesium sulfate and labetalol reduce CPP while maintaining the CBF and this labetalol CPP reducing effect is as effective as magnesium sulfate. It is worth mentioning that our findings should be considered preliminary and further large multicenter studies are required to verify these results.


Hypertension increases the risk of cardiovascular disease. Uncontrolled nocturnal blood pressure is prevalent in patients taking antihypertensive medication, with an incidence rate of 30–60%. Although chronotherapy with antihypertensive agents may provide a new direction for effective control of nocturnal blood pressure, the clinical evidence base remains controversial. This research is presently underway to compare the effects of morning and bedtime administration of antihypertensive medication on nocturnal reduction and circadian rhythm of blood pressure in patients with hypertension.This study is being performed as a randomized, multicenter, open-label, parallel-group, clinical trial in which 720 participants are to undergo 24-h ambulatory blood pressure measurement (ABPM) and office blood pressure measurement (OBPM) at baseline before being randomly assigned to a morning (6–10 am) or a bedtime (6–10 pm) administration group. Each participant receives one 20/5-mg tablet of olmesartan/amlodipine (OA) daily for 4 weeks and is then followed up at 4-week intervals for a total of 12 weeks. During follow-up, the OA dosage is adjusted according to the ABPM and OBPM results. Patients with uncontrolled hypertension at the first follow-up visit will receive an increase in OA dosage to 1.5 tablets/day. For patients with blood pressure that is still uncontrolled after a further 4 weeks, the dosage of OA can be increased to 2 tablets/day. The primary objective is the reduction in mean nocturnal systolic blood pressure between baseline and week 12. The secondary objectives are the reduction in ambulatory blood pressure at weeks 4 and 12 and the blood pressure control rate at weeks 4, 8, and 12.Antihypertensive chronotherapy remains controversial. A superiority test hypothesis design has been adopted for this trial, in which all participants will be taking the same antihypertensive medication. We anticipate that our findings will determine if nocturnal blood pressure control in Chinese patients with essential hypertension varies according to whether antihypertensive medication is taken in the morning or at bedtime. This study may provide scientific evidence for the application of chronotherapy in clinical practice.ChiCTR2200059719. Registered on 10 May 2022http://www.chictr.org.cn/edit.aspx?pid=169782&htm=4) {2a,2b}(The online version contains supplementary material available at 10.1186/s13063-023-07726-x. Hypertension is the most common chronic non-communicable disease and the most important risk factor for cardiovascular disease worldwide. Moreover, hypertension significantly increases the risks of atherosclerotic heart disease, stroke, chronic kidney disease, and heart failure. According to the recent China Hypertension Survey, the age-adjusted prevalence of hypertension in Chinese adults aged >18 years is 23.2%, with the estimated number of affected patients exceeding 250 million; however, the blood pressure (BP) control rate is only 16.8%, which is significantly lower than that in the developed countries of Europe and the USA  depending on the type of the original data. Qualitative variables will be presented as the absolute and relative frequency. Quantitative variables will be compared between the two study groups using theAll analyses will be based on the intention-to-treat (ITT) and the per-protocol (PP) populations. The primary objective of the study will be analyzed using an analysis of covariance (ANCOVA) model with appropriate baseline adjustments using the following covariates: baseline BP, age, sex, body mass index, alcohol consumption status, smoking status, fasting glucose, total cholesterol, low-density lipoprotein cholesterol, and the course of hypertension. Only covariates for which the results are statistically significant will be selected as explanatory variables for inclusion in the final model. The ANCOVA model will also be used to evaluate the other secondary objectives. Mean differences or odds ratios will be determined across groups along with their 95% confidence intervals. In the final analysis, we also aim to determine the antihypertensive dosage titration ratio for the two groups and then separately analyze the BP reduction further in subgroups whether or not the antihypertensive dosage was titrated. All statistical analyses will be performed using SPSS version 26.0 . The significance level will be set to 5% for all statistical tests.The process for handling missing data is generally divided into the deletion of cases with missing data and imputation of missing data. The commonly used methods include mean imputation, last observation carried forward, and multiple imputation. We will deal with the missing data depending on the circumstances under the guidance of the Department of Clinical Research Management at West China Hospital.Thus far, neither the European Society of Cardiology nor the American Heart Association has established guidelines regarding the best time of day for patients to take their antihypertensive medication. Therefore, antihypertensive chronotherapy is a hot topic in the hypertension field across the world. The Hygia Chronotherapy Trial  reportedAlthough OA is a new oral SPC with a relatively long half-time, the findings of this study may not be generalizable to other antihypertensive SPCs. Olmesartan was found to antagonize the vasoconstrictor and pressor responses induced with angiotensin II in a dose-dependent manner in animal studies, with an elimination half-life of 10-16h, it allows once-daily administration . HoweverThis study will have some limitations. First, it only includes Chinese individuals. Therefore, our findings may not be applicable to other ethnic populations. Second, ABPM is being performed over 24 h, which may not be as accurate as 48 h of monitoring. Third, one will exhibit a different BP response to a similar dose administered at bedtime or in the morning. Therefore, a randomized crossover trial will be required to examine the antihypertensive effectiveness of morning versus bedtime dosing. Fourth, patients in this trial are being followed for 3 months only, so it will not be possible to assess cardiovascular outcomes in the longer term.This trial is ongoing. This protocol is the third version and was revised on March 2022. Recruitment commenced in June 2022 and follow-up of the last patient recruited is estimated to be completed in September 2023.This prospective, multicenter, randomized controlled clinical study of antihypertensive chronotherapy in Asian patients aims to bridge the present knowledge gap in the field of antihypertensive chronotherapy and to provide clinical evidence on its use in hypertensive patients in China. Based on our research, we hypothesize that taking antihypertensive medication before bedtime can improve both nocturnal BP and the circadian rhythm of BP, thereby minimizing the risk of cardiovascular events.The results of this study will be actively disseminated through scientific publications and conference presentations.All participants will continue to be followed up in hypertension outpatient clinics. We would also provide compensation in the event of an unforeseen accident.Additional file 1. Reporting checklist for protocol of a clinical trial.


CAVI is also significant in CKD stage 1 with AOR = 0.16, 95% CI 0.03–0.77, p = 0.0223, stage 2 with AOR = 0.18, 95% CI 0.04–0.74, p = 0.0180, and stage 3a AOR = 0.31, 95% CI 0.10–0.93, p = 0.0375. Conclusion: TBI has a better yield of detection of PAD compared to ABI among Taiwanese patients with DKD. CAVI may play a role in the early stage of DKD.Background: Ankle-brachial index (ABI) is a simple method for diagnosing peripheral artery disease (PAD) but has limited reliability in patients with diabetes mellitus (DM) because of medial artery calcification. Our study aims to investigate whether the toe brachial index (TBI) or the cardio-ankle vascular index (CAVI) has a better detection over the ABI for diagnosing PAD in diabetic kidney disease (DKD). Materials and Methods: A cohort of 368 patients  with type 2 DM underwent ABI, TBI, and CAVI measurements at our outpatient clinic. Results: Of all enrolled patients, the TBI is significant in evaluating PAD, especially in patients whose chronic kidney disease (CKD) stage 3a with adjusted odds ratio (AOR) = 6.50, 95% confidence interval (CI) 1.63–25.97,  In patients with type 2 diabetes, cardiovascular disease (CVD) is the most common cause of mortality and morbidity . DiabeteThe ABI is a simple and noninvasive method to assess PAD , and an Patients with type 2 DM who visited the Endocrinology and Metabolism outpatient department of the Southern Taiwan Medical Center between February 2022 and May 2022 were included in the study. We excluded patients with type 1 DM , or the continuous requirement of insulin in the year succeeding diagnosis). Finally, 368 patients  were included in this study.The study protocol was approved by the institutional review board of the Kaohsiung Medical University Hospital (KMUHIRB-E(I)-20210313). Informed consent was obtained in written form from patients and all clinical investigation was conducted according to the principles expressed in the Declaration of Helsinki. The patients gave consent for the publication of the clinical details.The values of ABI, TBI, and CAVI were determined from the measurements by VS-2000 , which automatically and simultaneously measured blood pressures and pulse volumes in both arms and ankles or toe using an oscillometric method ,12,13. A2, where ρ is the blood density and PWV is the heart–ankle PWV (haPWV) measured between the origin of the aorta and the tibial artery at the ankle. The mean coefficient of variation of CAVI is <5%, which is small enough for clinical use. The CAVI values, according to age and gender, are classified as normal (CAVI < 8), borderline (8 ≤ CAVI < 9), and abnormal (CAVI ≥ 9) ).Comparisons of baseline characteristics between the patients with (n = 200) and without (n = 168) a normal CAVI are shown in p = 0.0080; AOR 7.47, 95% CI: 1.52–36.81, p = 0.0135; and AOR 20.13, 95% CI: 1.96–206.92, p= 0.0116, respectively. The AOR of TBI shows a value consistently above 1.0 beginning at CKD 3a, implying that this measure is associated with the true CKD stages. Interestingly, CKD stages 1, 2, and 3a were strongly associated with CAVI . Older age and longer DM duration were strongly associated with the CAVI level . p = 0.03120 and AOR 1.06, 95% CI: 1.00–1.12, p = 0.0388, respectively). Lower BMI was strongly associated with CAVI . Higher SBP was strongly associated with ABI . CVD was associated with both TBI and ABI . Meglitinide users were associated with both TBI and CAVI . DPP4 inhibitor users were associated with ABI . Diuretic users were strongly associated with TBI . Alpha-blocker was associated with CAVI .Increased triglycerides and low-density lipoprotein levels were associated with CAVI  stage 3a with adjusted odds ratio (AOR) = 6.50, 95% confidence interval (CI) 1.63–25.97, In our study, the first important finding is that TBI was strongly associated with CKD stages 3 to 5. Considering the medial artery calcification of the type 2 diabetes population with chronic kidney disease, the TBI is an alternative measure to the ABI to evaluate for PAD . Another2, especially with higher ratios in CKD stage 3 to 5 patients. [2 compared with those with eGFR ≥ 60 mL/min/1.73m2 and frequently leads to lower-limb complications [With respect to PAD, several studies have demonstrated that patients with CKD have a high risk of developing PAD . In addiatients. . In an oications . In 2012ications . This meications . VariousIn America, using a TBI cut-off of 0.7, a study has detected PAD  in dialysis patients characterized by an ABI > 0.9 . In addiPrevious studies also indicated that an increase in the CAVI is closely associated with a decreased eGFR or increased albuminuria . The preThe presence of metabolic syndrome has been related to high CAVI as a factor of arterial stiffness . The risThere are also some parameters, such as BMI, triglycerides, and LDL levels, that are strongly associated with CAVI in our study. A previous study proved that high triglycerides were strongly associated with high CAVI independent of multiple cardiometabolic risk factors. This can be explained by the statistics that show that the triglycerides were positively associated with CAVI. There was also a report showing that TG was found to be associated with a risk of higher CAVI . In our There is a linear association between CAVI and age ; as patiAlpha-blocker, which mainly acts on α-adrenergic receptors to produce vasodilation, reduces systemic vascular resistance, and achieves an antihypertensive effect, has the function of dilating blood vessels and lowering blood pressure; however, the CAVI is a parameter of vascular stiffness that does not depend on blood pressure , so it iDiuretics are mainly used in renal disease to facilitate extracellular fluid volume control, reduce the chance of developing hyperkalemia, and lower blood pressure. Furthermore, based on the results of the Antihypertensive and Lipid-Lowering Treatment To Prevent Heart Attack Trial (ALLHAT) and Joint National Committee (JNC) 7, diuretics are recommended as preferred agents in the general population with hypertension to lower their blood pressure and reduce CVD risk . AccordiIn our study, patients who used non-sulfonylureas meglitinides were associated with CAVI and TBI. When patients use sulfonylureas as medicine to control their type 2 DM, the risk of hypoglycemia needs to be considered, mainly because of the decreased clearance of insulin and oral hypoglycemic drugs and impaired renal gluconeogenesis . The patThe strength of our study is that this is the first and only study that compares the three measures of TBI, ABI, and CAVI together among patients with DKD. In the previous studies of Japan and the United States, only two measures with TBI and ABI were compared. The limitation of this study is that the gold standard for diagnosing PAD like angiography was not conducted in our study, because of the risk of progressing to hemodialysis after the procedures, especially for the advanced stages of CKD. The other limitation was that we were not able to include the smoking history of the study population.In conclusion, TBI has a higher yield of PAD compared to ABI in this sample of Taiwanese patients with diabetic kidney disease, especially in advanced stages of CKD. CAVI may play a role in the early stage of DKD.


Transl Psychiatry 2022; 12(1) 1-11).One of the great challenges still to be achieved in schizophrenia is the development of a staging model that reflects the progression of the disorder. The previous models suggested have been developed from a theoretical point of view and do not include objective variables such as biomarkers, physical comorbidities, or self-reported subjective variables ; psychopathology: PANSS, CDS, OSQ, CGI-S; functioning: PSP; cognition: MATRICS; laboratory tests: C-Reactive Protein (CRP), IL-1RA, IL-6, Platelets/Lymphocytes (PLR), Neutrophils/Lymphocytes (NLR), and Monocytes/Lymphocytes (MLR) ratios. Statistical analysis: Variables selection was performed with an ad hoc algorithm developed for this research. The referred algorithm makes use of genetic algorithms (GA) to select those variables that show the best performance for the patients classification according to their global CGI-S. The objective function of the GA maximizes the individuals correct classification of a support vector machines (SVM) model that employs as input variables those given by the GA  654). Models performance was assessed with the help of 3-fold cross-validation and these process was repeated 10,000 times for each one of the models assessed.Naturalistic, cross-sectional study. Sample: 212 stable patients with Schizophrenia (F20). Assessments: Mean age(SD): 39.5(13.54); men: 63.5%; secondary education: 59.50%. Most patients in our sample had never been married (74.10%), and more than a third received disability benefits due to schizophrenia (37.70%). The mean length of the disease was 11.98(12.02) years. The best SVM model included the following variables: 1)Clinical: number of hospitalizations, positive, negative, depressive symptoms and general psychopathology; 2)Cognition: speed of processing, visual learning and social cognition; 3)Functioning: PSP total score; 4)Biomarkers: PLR, NLR and MLR. This model was executed again 100,000 times applying again 3-fold cross-validation. In 95% of the algorithm executions more than a 53.52% of the patients were classfied in the right CGI-S category. On average the right classification was of 61.93%. About specificity and sensitivity the average values obtained were of 0.85 and 0.64 respectively.Our staging model is a robust method that appropriately distributes patients according to the severity of the disorder. Highlights the importance of clinical, functional and cognitive factors to classify patients. Finally, the inflammatory parameters PLR, NLR and MLR have also emerged as potential biomarkers for staging schizophrenia.None Declared