Chronic kidney disease (CKD) is a public health problem largely caused by diabetes. The epidemiology of diabetes mellitus-related CKD (CKD-DM) could provide specific support to lessen global, regional, and national CKD burden.
 
  Data were derived from the GBD 2019 study, including four measures and age-standardized rates (ASRs). Estimated annual percentage changes and 95% CIs were calculated to evaluate the variation trend of ASRs.
 
  Diabetes caused the majority of new cases and patients with CKD in all regions. All ASRs for type 2 diabetes-related CKD increased over 30 years.  https://www.selleckchem.com/products/vo-ohpic.html  Asia and Middle socio-demographic index (SDI) quintile always carried the heaviest burden of CKD-DM. Diabetes type 2 became the second leading cause of CKD and CKD-related death and the third leading cause of CKD-related DALYs in 2019. Type 2 diabetes-related CKD accounted for most of the CKD-DM disease burden. There were 2.62 million incident cases, 134.58 million patients, 405.99 thousand deaths, and 13.09 million disability-adjusteong regions and countries. Prevention and treatment measures should be strengthened according to CKD-DM epidemiology, especially in middle SDI quintile and Asia.
 Increasing burden of CKD-DM varied among regions and countries. Prevention and treatment measures should be strengthened according to CKD-DM epidemiology, especially in middle SDI quintile and Asia.Diabetes in the elderly increases cognitive impairment, but the underlying mechanisms are still far from fully understood. A non-targeted metabolomics approach based on liquid chromatography-mass spectrometry (LC-MS) was performed to screen out the serum biomarkers of diabetic mild cognitive impairment (DMMCI) in rats. Total 48 SD rats were divided into three groups, Normal control (NC) group, high-fat diet (HFD) fed group and type 2 diabetes mellitus (T2DM) group. The T2DM rat model was induced by intraperitoneal administration of streptozotocin (STZ, 35 mg/kg) after 6 weeks of high-fat diet (HFD) feeding. Then each group was further divided into 4-week and 8-week subgroups, which were calculated from the time point of T2DM rat model establishment. The novel object recognition test (NORT) and the Morris water maze (MWM) method were used to evaluate the cognitive deficits in all groups. Compared to the NC-8w and HFD-8w groups, both NOR and MWM tests indicated significant cognitive dysfunction in the T2DM-8w gid (SP) metabolism, tryptophan (Trp) metabolism, Glycerophospholipid (GP) metabolism, etc. Besides, SP metabolism, Trp metabolism and GP metabolism mainly belonging to the lipid metabolism showed marked perturbations over DMMCI and may contribute to the development of disease. Taken collectively, our results revealed that T2DM could cause cognitive impairment by affecting a variety of metabolic pathways especially lipid metabolism. Besides, serum PE, PC, L-Trp, and S1P may be used as the most critical biomarkers for the early diagnosis of DMMCI.
  Evidence on new-onset endocrine dysfunction and identifying whether the degree of this dysfunction is associated with the severity of disease in patients with COVID-19 is scarce.
 
  Consecutive patients enrolled at PGIMER Chandigarh were stratified on the basis of disease severity as group I (moderate-to-severe disease including oxygen saturation <94% on room air or those with comorbidities) (n= 35) and group II (mild disease, with oxygen saturation >94% and without comorbidities) (n=49). Hypothalamo-pituitary-adrenal, thyroid, gonadal axes, and lactotroph function were evaluated. Inflammatory and cell-injury markers were also analysed.
 
  Patients in group I had higher prevalence of hypocortisolism (38.5 
  6.8%, p=0.012), lower ACTH (16.3 
  32.1pg/ml, p=0.234) and DHEAS (86.29 
  117.8µg/dl, p= 0.086) as compared to group II. Low T3 syndrome was a universal finding, irrespective of disease severity. Sick euthyroid syndrome (apart from low T3 syndrome) (80.9 
  73.1%, p= 0.046) and atypical thyroiditis (low T3, high T4, low or normal TSH) (14.3 
  2.4%, p= 0.046) were more frequent in group I than group II. Male hypogonadism was also more prevalent in group I (75.6% 
  20.6%, p=0.006) than group II, with higher prevalence of both secondary (56.8 
  15.3%, p=0.006) and primary (18.8 
  5.3%, p=0.006) hypogonadism. Hyperprolactinemia was observed in 42.4% of patients without significant difference between both groups.
 
  COVID-19 can involve multiple endocrine organs and axes, with a greater prevalence and degree of endocrine dysfunction in those with more severe disease.
 COVID-19 can involve multiple endocrine organs and axes, with a greater prevalence and degree of endocrine dysfunction in those with more severe disease.
  This study aimed to cluster newly diagnosed patients and patients with long-term diabetes and to explore the clinical characteristics, risk of diabetes complications, and medication treatment related to each cluster.
 
  K-means clustering analysis was performed on 1,060 Chinese patients with type 2 diabetes based on five variables (HbA1c, age at diagnosis, BMI, HOMA2-IR, and HOMA2-B). The clinical features, risk of diabetic complications, and the utilization of elven types of medications agents related to each cluster were evaluated with the chi-square test and the Tukey-Kramer method.
 
  Four replicable clusters were identified, severe insulin-resistant diabetes (SIRD), severe insulin-deficient diabetes (SIDD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). In terms of clinical characteristics, there were significant differences in blood pressure, renal function, and lipids among clusters. Furthermore, individuals in SIRD had the highest prevalence of stages 2 and 3 chronic kidney disease (CKD) (57%) and diabetic peripheral neuropathy (DPN) (67%), while individuals in SIDD had the highest risk of diabetic retinopathy (32%), albuminuria (31%) and lower extremity arterial disease (LEAD) (13%). Additionally, the difference in medication treatment of clusters were observed in metformin (p = 0.012), α-glucosidase inhibitor (AGI) (p = 0.006), dipeptidyl peptidase 4 inhibitor (DPP-4) (p = 0.017), glucagon-like peptide-1 (GLP-1) (p <0.001), insulin (p <0.001), and statins (p = 0.006).
 
  The newly diagnosed patients and patients with long-term diabetes can be consistently clustered into featured clusters. Each cluster had significantly different patient characteristics, risk of diabetic complications, and medication treatment.
 The newly diagnosed patients and patients with long-term diabetes can be consistently clustered into featured clusters. Each cluster had significantly different patient characteristics, risk of diabetic complications, and medication treatment.