Dose-response association of diabetic kidney disease with routine clinical parameters in patients with type 2 diabetes mellitus: a systematic review and meta-analysis.

Background: Diabetic kidney disease (DKD) is a leading cause of end-stage kidney disease and is associated with high mortality rates. The influence of routine clinical parameters on DKD onset in patients with type 2 diabetes mellitus (T2DM) remains uncertain. Methods: In this systematic review and meta-analysis, we searched multiple databases, including PubMed, Embase, Scopus, Web of Science, and Cochrane Library, for studies published from each database inception until January 11, 2024. We included cohort studies examining the association between DKD onset and various clinical parameters, including body mass index (BMI), hemoglobin A1c (HbA1c), systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and serum uric acid (UA). Random-effect dose-response meta-analyses utilizing one-stage and/or cubic spline models, were used to estimate correlation strength. This study is registered in PROSPERO (CRD42022326148). Findings: This analysis of 46 studies involving 317,502 patients found that in patients with T2DM, the risk of DKD onset increased by 3% per 1 kg/m2 increase in BMI (relative risk (RR) = 1.03, confidence interval (CI) [1.01-1.04], I2 = 70.07%; GRADE, moderate); a 12% increased risk of DKD onset for every 1% increase in HbA1c (RR = 1.12, CI [1.07-1.17], I2 = 94.94%; GRADE, moderate); a 6% increased risk of DKD onset for every 5 mmHg increase in SBP (RR = 1.06. CI [1.03-1.09], I2 = 85.41%; GRADE, moderate); a 2% increased risk of DKD onset per 10 mg/dL increase in TG (RR = 1.02, CI [1.01-1.03], I2 = 78.45%; GRADE, low); an 6% decreased risk of DKD onset per 10 mg/dL increase in HDL (RR = 0.94, CI [0.92-0.96], I2 = 0.33%; GRADE, high), and a 11% increased risk for each 1 mg/dL increase in UA (RR = 1.11, CI [1.05-1.17], I2 = 79.46%; GRADE, moderate). Subgroup analysis revealed a likely higher risk association of clinical parameters (BMI, HbA1c, LDL, and UA) in patients with T2DM for less than 10 years. Interpretation: BMI, HbA1c, SBP, TG, HDL and UA are potential predictors of DKD onset in patients with T2DM. Given high heterogeneity between included studies, our findings should be interpreted with caution, but they suggest monitoring of these clinical parameters to identify individuals who may be at risk of developing DKD. Funding: Shenzhen Science and Innovation Fund, the Hong Kong Research Grants Council, and the HKU Seed Funds, and Scientific and technological innovation project of China Academy of Chinese Medical Sciences.

Emergence of cortical network motifs for short-term memory during learning.

Learning of adaptive behaviors requires the refinement of coordinated activity across multiple brain regions. However, how neural communications develop during learning remains poorly understood. Here, using two-photon calcium imaging, we simultaneously recorded the activity of layer 2/3 excitatory neurons in eight regions of the mouse dorsal cortex during learning of a delayed-response task. Across learning, while global functional connectivity became sparser, there emerged a subnetwork comprising of neurons in the anterior lateral motor cortex (ALM) and posterior parietal cortex (PPC). Neurons in this subnetwork shared a similar choice code during action preparation and formed recurrent functional connectivity across learning. Suppression of PPC activity disrupted choice selectivity in ALM and impaired task performance. Recurrent neural networks reconstructed from ALM activity revealed that PPC-ALM interactions rendered choice-related attractor dynamics more stable. Thus, learning constructs cortical network motifs by recruiting specific inter-areal communication channels to promote efficient and robust sensorimotor transformation.

Representation learning in the artificial and biological neural networks underlying sensorimotor integration.

The integration of deep learning and theories of reinforcement learning (RL) is a promising avenue to explore novel hypotheses on reward-based learning and decision-making in humans and other animals. Here, we trained deep RL agents and mice in the same sensorimotor task with high-dimensional state and action space and studied representation learning in their respective neural networks. Evaluation of thousands of neural network models with extensive hyperparameter search revealed that learning-dependent enrichment of state-value and policy representations of the task-performance-optimized deep RL agent closely resembled neural activity of the posterior parietal cortex (PPC). These representations were critical for the task performance in both systems. PPC neurons also exhibited representations of the internally defined subgoal, a feature of deep RL algorithms postulated to improve sample efficiency. Such striking resemblance between the artificial and biological networks and their functional convergence in sensorimotor integration offers new opportunities to better understand respective intelligent systems.