Urine Albumin-Creatinine Ratio Variability in People With Type 2 Diabetes: Clinical and Research Implications.

RATIONALE & OBJECTIVE: Evidence has demonstrated that albuminuria is a key diagnostic and prognostic marker of diabetic chronic kidney disease, but the impact of its day-to-day variability has not been adequately considered. This study quantified within-individual variability of albuminuria in people with type 2 diabetes to inform clinical albuminuria monitoring. STUDY DESIGN: Descriptive cross-sectional analysis. SETTING & PARTICIPANTS: People with type 2 diabetes (n=826, 67.1 [IQR, 60.3-72.4] years, 64.9% male) participating in the Progression of Diabetic Complications (PREDICT) cohort study. EXPOSURE: Four spot urine collections for measurement of urinary albumin-creatinine ratio (UACR) within 4 weeks. OUTCOME: Variability of UACR. ANALYTICAL APPROACH: We characterized within-individual variability (coefficient of variation [CV], 95% limits of random variation, intraclass correlation coefficient), developed a calculator displaying probabilities that any observed difference between a pair of UACR values truly exceeded a 30% difference, and estimated the ranges of diagnostic uncertainty to inform a need for additional UACR collections to exclude or confirm albuminuria. Multiple linear regression examined factors influencing UACR variability. RESULTS: We observed high within-individual variability (CV 48.8%; 95% limits of random variation showed a repeated UACR to be as high/low as 3.78/0.26 times the first). If a single-collection UACR increased from 2 to 5mg/mmol, the probability that UACR actually increased by at least 30% was only 50%, rising to 97% when 2 collections were obtained at each time point. The ranges of diagnostic uncertainty were 2.0-4.0mg/mmol after an initial UACR test, narrowing to 2.4-3.2 and 2.7-2.9mg/mmol for the mean of 2 and 3 collections, respectively. Some factors correlated with higher (female sex; moderately increased albuminuria) or lower (reduced estimated glomerular filtration rate and sodium-glucose cotransporter 2 inhibitor/angiotensin-converting enzyme inhibitor/angiotensin receptor blocker treatment) within-individual UACR variability. LIMITATIONS: Reliance on the mean of 4 UACR collections as the reference standard for albuminuria. CONCLUSIONS: UACR demonstrates a high degree of within-individual variability among individuals with type 2 diabetes. Multiple urine collections for UACR may improve capacity to monitor changes over time in clinical and research settings but may not be necessary for the diagnosis of albuminuria. PLAIN-LANGUAGE SUMMARY: Albuminuria (albumin in urine) is a diagnostic and prognostic marker of diabetic chronic kidney disease. However, albuminuria can vary within an individual from day to day. We compared 4 random spot urinary albumin-creatinine ratio (UACR) samples from 826 participants. We found that a second UACR collection may be as small as a fourth or as large as almost 4 times the first sample's UACR level. This high degree of variability presents a challenge to our ability to interpret changes in albuminuria. Multiple collections have been suggested as a solution. We have constructed tools that may aid clinicians in deciding how many urine collections are required to monitor and diagnose albuminuria. Multiple urine collections may be required for individual monitoring but not necessarily for diagnosis.

Research elevation of bank lending and technological innovation in the excess liquidity countries.

This study examines the intellectual framework research in bank lending and technological innovation relationships in countries with high banking system liquidity. This study employs bibliometrics with R-studio tools and procedures to analyze documents regarding productions, collaborations, keyword occurrences, conceptual structure, and density and centrality occurrence's network. Combining data from the Web of Science and Scopus databases, this study obtained 939 documents from 527 sources with a significant opportunity for further elevation through combination with other themes. The development analysis based on the most related countries indicates that researchers from other countries have also conducted studies identified as having significant banking liquidity. Topic development and thematic evolution show that research on the role of bank lending on technological innovation evolves to environmental issues, with green credit as the most recent and emerging elaboration. For further direction, keywords in investment clusters can help elevate education, commerce, and impact clusters by combining them with research on government taxation, credit provision, sustainable development, and emission control themes.

Excess Risk of Injury in Individuals With Type 1 or Type 2 Diabetes Compared With the General Population.

OBJECTIVE: To estimate the relative risk (RR) and excess hospitalization rate for injury in individuals with diabetes compared with the general population. RESEARCH DESIGN AND METHODS: Data were obtained from the Australian National Diabetes Services Scheme, hospitalization data sets, the Australian Pharmaceutical Benefits Scheme, the National Death Index, and the census spanning from 2011 to 2017. Hospitalizations for injury were coded as head and neck, lower-extremity, upper-extremity, or abdominal and thoracic injury; burns; or other injury. Poisson regression was used to estimate the age- and sex-adjusted RR of hospitalization for injury. RESULTS: The total number of hospitalizations for any injury was 117,705 in people with diabetes and 3,463,173 in the general population. Compared with that in the general population, an elevated adjusted risk of admission was observed for any injury (RR 1.22; 95% CI 1.21, 1.22), head and neck (1.28; 1.26, 1.30), lower extremity (1.24; 1.23, 1.26), abdominal and thoracic (1.29; 1.27, 1.30), upper extremity (1.03; 1.02, 1.05), burns (1.52; 1.44, 1.61), and other injury (1.37; 1.33, 1.40). The adjusted RR of any injury was 1.62 (1.58, 1.66) in individuals with type 1 diabetes, 1.65 (1.63, 1.66) in those with type 2 diabetes who were taking insulin, and 1.07 (1.06, 1.08) in individuals with type 2 diabetes not using insulin. Falls were the primary cause of injury in individuals with diabetes. CONCLUSIONS: Individuals with diabetes, especially those using insulin, had a higher risk of hospitalization for injury compared with the general population.

Impact of type 2 diabetes and its duration on incidence rates of dementia death and medication prescription in the Australian population during 2003-2016.

AIMS: To quantify rates of dementia treatment and death among Australians with type 2 diabetes relative to those without diabetes using linked national registries of Australia. METHODS: The study included 891,418 people with type 2 diabetes registered on the National Diabetes Services Scheme and a randomly sampled, population-based comparison group (n = 1,131,369). Outcomes included dementia death (all-cause dementia, Alzheimer's disease (AD) or vascular dementia), and first prescription of cholinesterase inhibitors or memantine. RESULTS: Excess dementia risk was observed in the diabetes group for the composite outcome of all-cause dementia death or dementia medication prescription but varied with age at diabetes diagnosis and its duration. At age 70, the rate of dementia death/medication prescription was 1.3 (95 % CI 1.2, 1.3) and 1.1 (95 % CI 1.1, 1.2) times higher in people with ten and five years of diabetes duration, respectively. Individual outcomes showed that diabetes was associated with a higher incidence of vascular dementia death, whereas an increased risk of AD death was only observed beyond ∼ 10 years of diabetes duration. Further, the incidence of dementia medication prescription was lower among people with diabetes. CONCLUSIONS: A higher incidence of AD death in the setting of 10 + years of diabetes duration coupled with a lower incidence of AD treatment suggests an under-recognition of this dementia phenotype among people with type 2 diabetes.

Library size confounds biology in spatial transcriptomics data.

Spatial molecular data has transformed the study of disease microenvironments, though, larger datasets pose an analytics challenge prompting the direct adoption of single-cell RNA-sequencing tools including normalization methods. Here, we demonstrate that library size is associated with tissue structure and that normalizing these effects out using commonly applied scRNA-seq normalization methods will negatively affect spatial domain identification. Spatial data should not be specifically corrected for library size prior to analysis, and algorithms designed for scRNA-seq data should be adopted with caution.

A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age.

BACKGROUND: Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health. METHODS: Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status. FINDINGS: Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have cardiovascular disease (OR = 2.13, 95% CI = 1.62-2.83), had a 2.01-fold increased risk of 12-year incident cardiovascular events (HR = 2.01, 95% CI = 1.45-2.57), and a 1.56-fold increased risk of 17-year all-cause mortality (HR = 1.56, 95% CI = 1.34-1.79), relative to the individuals in the bottom quintile group (Q1). Survival analysis further revealed that men in the Q5 group faced the challenge of reaching a median survival rate due to cardiovascular events more than six years earlier and reaching a median survival rate due to all-cause mortality more than four years earlier than men in the Q1 group. INTERPRETATION: Our findings demonstrate that the mAge score captures age-related metabolic changes, predicts health outcomes, and has the potential to identify individuals at increased risk of metabolic diseases. FUNDING: The specific funding of this article is provided in the acknowledgements section.

Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention.

BACKGROUND: Accurate risk stratification is vital for primary prevention of cardiovascular disease (CVD). However, traditional tools such as the Framingham Risk Score (FRS) may underperform within the diverse intermediate-risk group, which includes individuals requiring distinct management strategies. OBJECTIVES: This study aimed to develop a lipidomic-enhanced risk score (LRS), specifically targeting risk prediction and reclassification within the intermediate group, benchmarked against the FRS. METHODS: The LRS was developed via a machine learning workflow using ridge regression on the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab; n = 10,339). It was externally validated with the Busselton Health Study (n = 4,492), and its predictive utility for coronary artery calcium scoring (CACS)-based outcomes was independently validated in the BioHEART cohort (n = 994). RESULTS: LRS significantly improved discrimination metrics for the intermediate-risk group in both AusDiab and Busselton Health Study cohorts (all P < 0.001), increasing the area under the curve for CVD events by 0.114 (95% CI: 0.1123-0.1157) and 0.077 (95% CI: 0.0755-0.0785), with a net reclassification improvement of 0.36 (95% CI: 0.21-0.51) and 0.33 (95% CI: 0.15-0.49), respectively. For CACS-based outcomes in BioHEART, LRS achieved a significant area under the curve improvement of 0.02 over the FRS (0.76 vs 0.74; P < 1.0 × 10-5). A simplified, clinically applicable version of LRS was also created that had comparable performance to the original LRS. CONCLUSIONS: LRS, augmenting the FRS, presents potential to improve intermediate-risk stratification and to predict atherosclerotic markers using a simple blood test, suitable for clinical application. This could facilitate the triage of individuals for noninvasive imaging such as CACS, fostering precision medicine in CVD prevention and management.