Background: Diabetes mellitus is a heterogeneous metabolic disorder that poses substantial challenges in the management of patients with diabetes. Emerging research underscores the potential of unsupervised cluster analysis as a promising methodological approach for unraveling the complex heterogeneity of diabetes mellitus. This systematic review evaluated the effectiveness of unsupervised cluster analysis in identifying diabetes phenotypes, elucidating the risks of diabetes-related complications, and distinguishing treatment responses. Methods: We searched MEDLINE Complete, PubMed, and Web of Science and reviewed forty-one relevant studies. Additionally, we conducted a cross-sectional study using K-means cluster analysis of real-world clinical data from 558 patients with diabetes. Results: A key finding was the consistent reproducibility of the five clusters across diverse populations, encompassing various patient origins and ethnic backgrounds. MOD and MARD were the most prevalent clusters, while SAID was the least prevalent. Subgroup analysis stratified by ethnic group indicated a higher prevalence of SIDD among individuals of Asian descent than among other ethnic groups. These clusters shared similar phenotypic traits and risk profiles for complications, with some variations in their distribution and key clinical variables. Notably, the SIRD subtype was associated with a wide spectrum of kidney-related clinical presentations. Alternative clustering techniques may reveal additional clinically relevant diabetes subtypes. Our cross-sectional study identified five subgroups, each with distinct profiles of glycemic control, lipid metabolism, blood pressure, and renal function. Conclusions: Overall, the results suggest that unsupervised cluster analysis holds promise for revealing clinically meaningful subgroups with distinct characteristics, complication risks, and treatment responses that may remain undetected using conventional approaches.

Epilepsy is one of the major neurological disorders affecting millions of people worldwide, with significant variations in incidence and prevalence across different regions. This study focuses on the epidemiological trends of idiopathic epilepsy in Central Asia, a region characterized by a long process of healthcare system transformation and socioeconomic conditions after the collapse of the Soviet Union. Understanding these trends is crucial for developing effective public health strategies tailored to the unique needs of the population. GBD 2021 data were analyzed for 9 Central Asian countries (Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, Mongolia), identifying trends in incidence, prevalence, mortality, and disability-adjusted life years associated with idiopathic epilepsy from 1990-2021. The average annual percentage change (AAPC) was determined to compare regional and global trends. The data were disaggregated by country, year, age, and sex to identify demographic patterns. In 2021, the highest prevalence of idiopathic epilepsy was in Uzbekistan (473 per 100,000; 95 % UI: 140.1-761.1), while Armenia had the lowest (291.3; 95 % UI: 94.8-486.1). Incidence decreased across most countries, with Tajikistan showing a significant decline (AAPC:0.89 %; 95 % CI:0.98 to -0.8). Mortality was highest in Tajikistan (4.6; 95 % UI: 3.4-6.1) and lowest in Armenia (0.9; 95 % UI: 0.7-1.1). DALYs were highest in Tajikistan (422.7; 95 % UI: 284.1-591.7) and lowest in Armenia (118.8; 95 % UI: 57.4-204.9). Most trends were declining, except for Turkmenistan, which showed increases in prevalence and DALYs. The sex-specific estimates are consistently greater in the male population across all the metrics. Central Asia has demonstrated promising downward trends in the burden of epilepsy; however, challenges persist in some countries. This study underscores the urgent need for local data generation, robust registries, and tailored policies to improve care for people with epilepsy in the region.

An Orthomyxoviridae Alphainfluenza virus Influenza A virus strain, A/Kazakhstan/Flu-H11-6/2022(H1N1 ), was isolated in Karaganda, Central Kazakhstan during a study of acute respiratory infections among hospital inpatients in 2022. Here, we present the complete genome sequence of this strain.

Background and Aims: Pneumonia is an inflammatory condition of the lower respiratory tract, commonly caused by infection and associated with substantial morbidity and healthcare utilization. The burden of pneumonia on healthcare is exacerbated by limited data on pathogenic causes and associated antimicrobial resistance (AMR). Here, we conducted a literature review and synthesized data on the microbial causes and AMR in hospitalized pneumonia patients from Kazakhstan. Methods: We searched PubMed, Medline, Embase, Web of Science, the Cochrane Central Register of Controlled Trials, eLIBRARY, and CyberLeninka for observational studies of hospitalized pneumonia patients reporting microbiologically confirmed pathogen and antimicrobial resistance data from Kazakhstan. Exclusion criteria were unclear pneumonia definitions or absence of pathogen‐specific AMR data. We assessed study quality using the Joanna Briggs Institute checklist and extracted data on microbial prevalence and resistance rates by pathogen‐antibiotic combinations. We synthesized microbial etiology and AMR patterns and performed random‐effects meta‐analysis of prevalence data. Results: Nine studies represented 1534 isolates from 1474 inpatients (2008–2022). The most prevalent pathogens were Streptococcus pneumoniae (17.8%), Streptococcus spp. (17.3%), Haemophilus influenzae (12.6%), and Klebsiella pneumoniae (10.7%). Hospital‐acquired pathogens comprised 48% of isolates. Acinetobacter baumannii had the highest resistance, exceeding 70% across major antibiotic classes. Escherichia coli had high resistance to β‐lactams (> 66%) and fluoroquinolones (63%). Pseudomonas aeruginosa had 60% fluoroquinolone and 27% carbapenem resistance. Fluoroquinolone resistance was widespread across multiple pathogens. K. pneumoniae had relatively lower resistance rates. Pathogen distribution varied by clinical setting and patient population. Conclusion: Data on the etiologic causes and AMR in pneumonia are sparse in Kazakhstan and methodologically heterogeneous. Hospital‐acquired pneumonia pathogens were highly prevalent and associated with high AMR rates. These findings underscore the need for improved pathogen surveillance and antimicrobial stewardship in Kazakhstan to address the high burden of antibiotic‐resistant and hospital‐acquired pneumonia.