Blood glucose fluctuations detected by continuous glucose monitoring system in gout patients with normal glucose tolerance and the effect of urate-lowering therapy

Glycemic variability assessed using continuous glucose monitoring in individuals without diabetes and associations with cardiometabolic risk markers: A systematic review and meta-analysis

BACKGROUND & AIMS

Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes.

METHODS

Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for ≥24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta-analyzes were performed, depending on outcome.

RESULTS

Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease.

CONCLUSION

GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes.

Chinese herbal compound Huangqin Qingrechubi capsule reduces lipid metabolism disorder and inflammatory response in gouty arthritis via the LncRNA H19/APN/PI3K/AKT cascade

CONTEXT

Gouty arthritis (GA) is a characteristically inflammatory disease often associated with lipid metabolism disorder. Huangqin Qingrechubi capsule (HQC) has been used for the treatment of GA.

OBJECTIVE

To explore the mechanism of HQC in the treatment of GA.

MATERIALS AND METHODS

A total of 30 GA patients (GA group) and 30 healthy subjects [normal control (NC) group] were recruited. The GA group was treated with HQC (3.6 g/d) for 10 days. Lipid metabolism and inflammation indexes were detected. Five herbal names of HQC, or 'gouty arthritis', 'hyperlipidemia' and 'inflammation' were used as key words to search related databases for network pharmacological analysis. Subsequently, GA-fibroblast-like synoviocytes (FLSs) were stimulated with GA-peripheral blood mononuclear cells (PBMCs) (3:1) and treated with HQC drug-containing serum (20%). RT-qPCR, Western blot, and ELISA were conducted to further explore the mechanism of HQC in improving GA.

RESULTS

In clinical observation, HQC decreased the expression of lncRNA H19 and IL-1β, and increased the expression of adiponectin (APN) and IL-4 in the GA group (about half). Through network pharmacology, the PI3K/AKT signaling pathway was identified. Cell experiments showed that HQC treatment reduced the viability of GA-FLSs (49.61%), up-regulated the expression of IL-4 (155.18%), IL-10 (165.13%), and APN (31.24%), and down-regulated the expression of lncRNA H19 (33.70%), IL-1β (64.70%), TNF-α (78.32%), p-PI3K (48.80%), and p-AKT (53.48%).

DISCUSSION AND CONCLUSIONS

HQC improved lipid metabolism disorder and inflammatory response of GA by regulating the lncRNA H19/APN/PI3K/AKT. Maintaining the stability of lipid metabolism may be an effective way to alleviate GA.

Use of Continuous Glucose Monitors by People Without Diabetes: An Idea Whose Time Has Come?

BACKGROUND

Continuous glucose monitor (CGM) systems were originally intended only for people with diabetes. Recently, there has been interest in monitoring glucose concentrations in a variety of other situations. As data accumulate to support the use of CGM systems in additional states unrelated to diabetes, the use of CGM systems is likely to increase accordingly.

METHODS

PubMed and Google Scholar were searched for articles about the use of CGM in individuals without diabetes. Relevant articles that included sufficient details were queried to identify what cohorts of individuals were adopting CGM use and to define trends of use.

RESULTS

Four clinical user cases were identified: (1) metabolic diseases related to diabetes with a primary dysregulation of the insulin-glucose axis, (2) metabolic diseases without a primary pathophysiologic derangement of the insulin-glucose axis, (3) health and wellness, and (4) elite athletics. Seven trends in the use of CGM systems in people without diabetes were idenfitied which pertained to both FDA-cleared medical grade products as well as anticipated future products, which may be regulated differently based on intended populations and indications for use.

CONCLUSIONS

Wearing a CGM has been used not only for diabetes, but with a goal of improving glucose patterns to avoid diabetes, improving mental or physical performance, and promoting motivate healthy behavioral changes. We expect that clinicians will become increasingly aware of (1) glycemic patterns from CGM tracings that predict an increased risk of diabetes, (2) specific metabolic glucotypes from CGM tracings that predict an increased risk of diabetes, and (3) new genetic and genomic biomarkers in the future.

The correlation of uric acid levels with biochemical parameters and dietary factors in individuals with asymptomatic hyperuricemia and gouty arthritis

Diet may play an important role in the development of asymptomatic hyperuricemia (ASH) and gouty arthritis (GOUT). However, the association between dietary factors and hyperuricemia remains unclear. Serum uric acid levels are affected by dietary factors. This study aimed to evaluate the correlation of uric acid levels with biochemical parameters and dietary factors in individuals with ASH and GOUT. This study was conducted in 145 individuals with ASH and GOUT. General characteristics of individuals were collected via face-to-face interviews. Food frequency questionnaire was used to obtain energy, macro- and micronutrients intakes. Biochemical parameters were obtained from patient files. The incidence of gout was higher in men comparing to women. Individuals in the GOUT group consumed more alcohol and higher serum levels of vitamin B₁₂, C-reactive protein (CRP), triglyceride, and uric acid. Individuals in the GOUT group had higher intakes of energy, protein, carbohydrate, fat, fructose, vitamin C, and vitamin B₁₂. Triglyceride, uric acid, CRP, vitamin B₁₂, and homeostatic model assessment of insulin resistance were found to be affected by high uric acid levels. Dietary factors can pose a risk for health problems in addition to GOUT and ASH, such as cardiovascular disease, diabetes, and obesity.

Serum Uric Acid and Diabetes: From Pathophysiology to Cardiovascular Disease

Hyperuricemia, has been traditionally related to nephrolithiasis and gout. However, it has also been associated with the development of type 2 diabetes mellitus (T2DM) and cardiometabolic and cardiovascular diseases. Pathophysiologically, elevated serum uric acid (SUA) levels may be associated with abnormal lipid and glucose metabolism. In this narrative review, we consider the associations between hyperuricemia, hyperglycemia, atherosclerosis and thrombosis. Furthermore, we comment on the available evidence linking elevated SUA levels with the incidence and outcomes of coronary heart disease, stroke, peripheral artery disease and non-alcoholic fatty liver in subjects with T2DM. The effects of antidiabetic drugs (e.g. metformin, pioglitazone, sulfonylureas, dipeptidyl peptidase 4 inhibitors, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter 2 inhibitors and insulin) on SUA concentrations are also reviewed.