Glucose-Lowering Medications and Post-Dementia Survival in Patients with Diabetes and Dementia

The Promising Potency of Sodium-Glucose Cotransporter 2 Inhibitors in the Prevention of and as Treatment for Cognitive Impairment Among Type 2 Diabetes Patients

Type 2 diabetes mellitus (T2DM), accounting for the majority of diabetes mellitus prevalence, is associated with an increased risk of cognition decline and deterioration of cognition function in diabetic patients. The sodium-glucose cotransporter 2 (SGLT2), located in the renal proximal tubule, plays a role in urine glucose reabsorption. SGLT2 inhibitors (SGLT2i), have shown potential benefits beyond cardiac and renal improvement in preventing and treating cognitive impairment (CI), including mild cognitive impairment, Alzheimer's disease and vascular dementia in T2DM patients. Studies suggest that SGLT2i may ameliorate diabetic CI through metabolism pathways, inflammation, oxidative stress, neurotrophic factors and AChE inhibition. Clinical trials and meta-analyses have reported significant and insignificant results. Given their vascular effects, SGLT2i may offer unique protection against vascular CI. This review compiles mechanisms and clinical evidence, emphasizing the need for future analysis, evaluation, trials and meta-analyses to verify and recommend optimal SGLT2i selection and dosage for specific patients.

Impact of Sodium Glucose Cotransporter 2 Inhibitors (SGLT2i) Therapy on Dementia and Cognitive Decline

Dementia is an age-related syndrome characterized by the progressive deterioration of cognition and capacity for independent living. Diabetes is often associated with cognitive decline and shares similar pathophysiological mechanisms with dementia, such as systemic inflammation, oxidative stress, insulin resistance, and advanced glycation end-products formation. Therefore, adequate diabetes management may reduce the risk of cognitive decline, especially in patients with other comorbidities and risk factors. The sodium glucose cotransporter inhibitors (SGLT2i) regulate renal glucose reabsorption by blocking the SGLT2 cotransporters located in the proximal tubules, causing glycosuria and intraglomerular pressure reduction. Their use helps to lower blood pressure by modifying sodium and water homeostasis; these drugs are also commonly used in the treatment of heart failure and chronic kidney disease, while recently, a potential neuroprotective role in the central nervous system has been suggested. The aim of our scoping review is to analyze current evidence about the potential neuroprotective effects of SGLT2i in adult patients. We performed a scoping literature review to evaluate the effect of SGLT2i on dementia, mild cognitive impairment (MCI) and Alzheimer's disease incidence and progression. The screening process was performed through different searches on PubMed and EMBASE, evaluating original works published up to January 2024. In conclusion, the use of SGLT2i could be associated with a neuroprotective effect in patients with diabetes, reducing the incidence or the progression of MCI and dementia. Further prospective studies are needed to validate this hypothesis and to evaluate the effectiveness of this class of drugs in normal glycemic profile patients.

XBP1-mediated transcriptional regulation of SLC5A1 in human epithelial cells in disease conditions

BACKGROUND

Sodium-Glucose cotransporter 1 and 2 (SGLT1/2) belong to the family of glucose transporters, encoded by SLC5A1 and SLC5A2, respectively. SGLT2 is almost exclusively expressed in the renal proximal convoluted tubule cells. SGLT1 is expressed in the kidneys but also in other organs throughout the body. Many SGLT inhibitor drugs have been developed based on the mechanism of blocking glucose (re)absorption mediated by SGLT1/2, and several have gained major regulatory agencies' approval for treating diabetes. Intriguingly these drugs are also effective in treating diseases beyond diabetes, for example heart failure and chronic kidney disease. We recently discovered that SGLT1 is upregulated in the airway epithelial cells derived from patients of cystic fibrosis (CF), a devastating genetic disease affecting greater than 70,000 worldwide.

RESULTS

In the present work, we show that the SGLT1 upregulation is coupled with elevated endoplasmic reticulum (ER) stress response, indicated by activation of the primary ER stress senor inositol-requiring protein 1α (IRE1α) and the ER stress-induced transcription factor X-box binding protein 1 (XBP1), in CF epithelial cells, and in epithelial cells of other stress conditions. Through biochemistry experiments, we demonstrated that the spliced form of XBP1 (XBP1s) acts as a transcription factor for SLC5A1 by directly binding to its promoter region. Targeting this ER stress → SLC5A1 axis by either the ER stress inhibitor Rapamycin or the SGLT1 inhibitor Sotagliflozin was effective in attenuating the ER stress response and reducing the SGLT1 level in these cellular model systems.

CONCLUSIONS

The present work establishes a causal relationship between ER stress and SGLT1 upregulation and provides a mechanistic explanation why SGLT inhibitor drugs benefit diseases beyond diabetes.

GLP-1 receptor agonists, SGLT2 inhibitors and noncardiovascular mortality in type 2 diabetes: Insights from a meta-analysis

OBJECTIVE

Type-2 diabetes (T2D) poses a higher risk of noncardiovascular mortality in addition to the burden of cardiovascular mortality. The well-established cardiovascular benefits of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT2i) could solely explain their apparent effects on all-cause mortality in T2D. The present meta-analysis aims to pool their effects on noncardiovascular mortality in T2D and summarize the recent evidence on plausible pathways mediating these effects.

METHODS

PubMed, Embase, Web of Science, and clinical trial registries were searched for randomized controlled trials (RCTs) with ≥1-year duration in adults with T2D reporting both cardiovascular and all-cause mortality in treatment versus placebo arms (PROSPERO: CRD42022337559). Noncardiovascular mortality was calculated by subtracting cardiovascular mortality events from all-cause mortality and risk ratios (RRs) were calculated. Random-effects meta-analysis was done. GRADE framework was used to assess evidence quality.

RESULTS

We identified 17 eligible RCTs pooling data retrieved from 109,892 patients. Randomization to GLP-1 RA treatment versus placebo was associated with reduced noncardiovascular mortality (RR = 0.90; 95%CI: 0.81-0.99; I2 = 0 %; p < 0.05), consistent with their effects on cardiovascular mortality (RR = 0.88; 95%CI: 0.81-0.95; I2 = 0 %; p < 0.01) in T2D. Compared to placebo, SGLT2i significantly reduced noncardiovascular mortality (RR = 0.90; 95%CI: 0.82-0.99; I2 = 0 %; p < 0.05) along with cardiovascular mortality (RR = 0.84; 95%CI: 0.77-0.92; I2 = 28 %; p < 0.001). Subgroup analysis showed no significant effects of heart failure or renal function on treatment benefits of SGLT2i on noncardiovascular mortality (p value > 0.2 for subgroup differences).

CONCLUSION

The impact of GLP-1RAs and SGLT2i on mortality in people with T2D extends beyond their cardiovascular benefits.

Anti-diabetics and the Prevention of Dementia: A Systematic Review

Type 2 diabetes mellitus (T2DM) is a worldwide epidemic that is only increasing as the years progress, and as of 2019, affecting over 37 million. T2DM is a chronic condition caused by reduced insulin secretion and increased insulin resistance. Due to insulin not operating at optimal conditions, blood glucose rises and remains high, thus disturbing metabolic hemostasis. Many complications can arise from T2DM, such as coronary vascular disease, kidney damage, eye damage, and, quite significantly, dementia. It is theorized that dementia from T2DM stems from the fact that the brain is susceptible to hyperglycemic conditions, which are promoted by the increase in insulin resistance of target cells in the central nervous system. This directly affects cognitive processes and memory, which correlates to decreased temporal and front lobes volume. The risk of diabetic complications can be minimized with therapeutic interventions such as oral-antidiabetic (OAD) agents and insulin. Several OADs are on the market, but the first-line agent is metformin, a biguanide that decreases glucose production and increases insulin sensitivity. This paper aims to determine if currently prescribed OADs can help slow cognitive decline and reduce the risk and incidence of dementia as a complication of T2DM. Studies found that, for the most part, all OADs except sulfonylureas (SU) significantly slowed the decline of cognitive function and reduced the risk and incidence of dementia. SU's were shown to increase the risk of dementia in most studies. Of all the OADs, thiazolidinediones may be the most beneficial drug class for reducing the risk of dementia in T2DM patients. Future research should focus on whether early intervention with specific classes of OADs can not only improve glycemic control, leading to decreased hyperglycemia but also prevent the build-up of damaged brain tissue and help to reduce the risk and incidence of dementia in patients with T2DM.

XBP1-mediated transcriptional regulation of SLC5A1 in human epithelial cells in disease conditions

Background

sodium-dependent glucose cotransporter 1 and 2 (SGLT1/2) belong to the family of glucose transporters, encoded by SLC5A1 and SLC5A2, respectively. SGLT-2 is almost exclusively expressed in the renal proximal convoluted tubule cells. SGLT-1 is expressed in the kidneys but also in other organs throughout the body. Many SGLT inhibitor drugs have been developed based on the mechanism of blocking glucose (re)absorption mediated by SGLT1/2, and several have gained major regulatory agencies' approval for treating diabetes. Intriguingly these drugs are also effective in treating diseases beyond diabetes, for example heart failure and chronic kidney disease. We recently discovered that SGLT-1 is upregulated in the airway epithelial cells derived from patients of cystic fibrosis (CF), a devastating genetic disease affecting greater than 70,000 worldwide.

Results

in the present work, we show that the SGLT-1 upregulation is coupled with elevated endoplasmic reticulum (ER) stress response, indicated by activation of the primary ER stress senor inositol-requiring protein 1a (IRE1a) and the ER stress-induced transcription factor X-box binding protein 1 (XBP1), in CF epithelial cells, and in epithelial cells of other stress conditions. Through biochemistry experiments, we demonstrated that XBP1 acts as a transcription factor for SLC5A1 by directly binding to its promoter region. Targeting this ER stress → SLC5A1 axis by either the ER stress inhibitor Rapamycin or the SGLT-1 inhibitor Sotagliflozin was effective in attenuating the ER stress response and reducing the SGLT-1 levels in these cellular model systems.

Conclusions

the present work establishes a causal relationship between ER stress and SGLT-1 upregulation and provides a mechanistic explanation why SGLT inhibitor drugs benefit diseases beyond diabetes.

Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study

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 (e.g., cardiovascular disease). 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.