Association of cystatin C levels with metabolic syndrome incidence: a nested case-control study with propensity score matching

Metabolic Syndrome and Risk of Amyotrophic Lateral Sclerosis: Insights from a Large-Scale Prospective Study

OBJECTIVE

Although metabolic abnormalities are implicated in the etiology of neurodegenerative diseases, their role in the development of amyotrophic lateral sclerosis (ALS) remains a subject of controversy. We aimed to identify the association between metabolic syndrome (MetS) and the risk of ALS.

METHODS

This study included 395,987 participants from the UK Biobank to investigate the relationship between MetS and ALS. Cox regression model was used to estimate hazard ratios (HR). Stratified analyses were performed based on gender, body mass index (BMI), smoking status, and education level. Mediation analysis was conducted to explore potential mechanisms.

RESULTS

In this study, a total of 539 cases of ALS were recorded after a median follow-up of 13.7 years. Patients with MetS (defined harmonized) had a higher risk of developing ALS after adjusting for confounding factors (HR: 1.50, 95% CI: 1.19-1.89). Specifically, hypertension and high triglycerides were linked to a higher risk of ALS (HR: 1.53, 95% CI: 1.19-1.95; HR: 1.31, 95% CI: 1.06-1.61, respectively). Moreover, the quantity of metabolic abnormalities showed significant results. Stratified analysis revealed that these associations are particularly significant in individuals with a BMI <25. These findings remained stable after sensitivity analysis. Notably, mediation analysis identified potential metabolites and metabolomic mediators, including alkaline phosphatase, cystatin C, γ-glutamyl transferase, saturated fatty acids to total fatty acids percentage, and omega-6 fatty acids to omega-3 fatty acids ratio.

INTERPRETATION

MetS exhibits a robust association with an increased susceptibility to ALS, particularly in individuals with a lower BMI. Furthermore, metabolites and metabolomics, as potential mediators, provide invaluable insights into the intricate biological mechanisms. ANN NEUROL 2024.

The association between maternal diabetes and neonatal seizures: a nested case-Control study

Aim

We aimed to evaluate the association of pregestational diabetes mellitus (PGDM) and gestational diabetes mellitus (GDM) with neonatal seizures during neonatal hospitalization.

Methods

In this nested case-control study, all data were collected from the data files of the National Vital Statistics System (NVSS) 2016-2021. Considering the effect of confounders, we used the propensity-score matching (PSM; case:control = 1:4) method to select the study population. The outcome was considered the occurrence of neonatal seizures. Univariate and multivariate logistic regression analyses were adopted to assess the association of PGDM and GDM with neonatal seizures. We also conducted stratified analyses according to gestational age, birthweight, 5 min Apgar score, and maternal age to explore the potential disparities.

Results

After using the PSM method, a total of 6,674 cases of neonatal seizures and 26,696 controls were included. After adjusting for covariates, PGDM was associated with an increased risk of neonatal seizures [odds ratio (OR) = 1.51, 95% confidence interval (CI): 1.15-1.98], whereas the association between GDM and neonatal seizures is not statistically significant. In addition, the correlation between PGDM and increased risk of neonatal seizures was observed in neonates with a gestational age of 37-42 weeks and ≥42 weeks, with a 5 min Apgar score of ≥7, and with a maternal age of ≤40 years.

Conclusion

PGDM was found to be closely associated with an increased risk of neonatal seizures. The findings of our study indicated that neonatologists should consider monitoring the incidence of neonatal seizures in neonates born to mothers with PGDM.

Association between metabolic syndrome and uric acid: a systematic review and meta-analysis

This systematic review and meta-analysis aim to provide the best evidence on the association between metabolic syndrome (MetS) and uric acid (UA) by determining the size of the effect of this biomarker on MetS. The review protocol is registered with PROSPERO (CRD42021231124). The search covered the PubMed and Scopus databases. Methodological quality was assessed with the STROBE tool, overall risk of bias with RevMan (Cochrane Collaboration) and quality of evidence with Grade Pro. Initially, 1582 articles were identified. Then, after excluding duplicates and reviewing titles and abstracts, 1529 articles were excluded from applying the eligibility criteria. We included 43 papers (56 groups) comparing UA concentrations between subjects 91,845 with MetS and 259,931 controls. Subjects with MetS had a higher mean UA of 0.57 mg/dl (95% CI 0.54-0.61) (p < 0.00001). Given the heterogeneity of the included studies, the researchers decided to perform subgroups analysis. Men with MetS have a higher UA concentration mg/dl 0.53 (95% CI 0.45-0.62, p < 0.00001) and women with MetS 0.57 (95% CI 0.48-0.66, p < 0.00001) compared to subjects without MetS. Assessment of UA concentration could provide a new avenue for early diagnosis of MetS, as a new biomarker and the possibility of new therapeutic targets.

Diagnostic Power of Circulatory Metabolic Biomarkers as Metabolic Syndrome Risk Predictors in Community-Dwelling Older Adults in Northwest of England (A Feasibility Study)

Background: Metabolic Syndrome (MetS) is a cluster of risk factors for diabetes and cardiovascular diseases with pathophysiology strongly linked to aging. A range of circulatory metabolic biomarkers such as inflammatory adipokines have been associated with MetS; however, the diagnostic power of these markers as MetS risk correlates in elderly has yet to be elucidated. This cross-sectional study investigated the diagnostic power of circulatory metabolic biomarkers as MetS risk correlates in older adults. Methods: Hundred community dwelling older adults (mean age: 68.7 years) were recruited in a study, where their blood pressure, body composition and Pulse Wave Velocity (PWV) were measured; and their fasting capillary and venous blood were collected. The components of the MetS; and the serum concentrations of Interleukin-6 (IL-6), Tumor Necrosis Factor-α (TNF-α), Plasminogen Activator Inhibitor-I (PAI-I), Leptin, Adiponectin, Resistin, Cystatin-C, C-Reactive Protein (CRP), insulin and ferritin were measured within the laboratory, and the HOMA1-IR and Atherogenic Index of Plasma (AIP) were calculated. Results: Apart from other markers which were related with some cardiometabolic (CM) risk, after Bonferroni correction insulin had significant association with all components of Mets and AIP. These associations also remained significant in multivariate regression. The multivariate odds ratio (OR with 95% confidence interval (CI)) showed a statistically significant association between IL-6 (OR: 1.32 (1.06–1.64)), TNF-α (OR: 1.37 (1.02–1.84)), Resistin (OR: 1.27 (1.04–1.54)) and CRP (OR: 1.29 (1.09–1.54)) with MetS risk; however, these associations were not found when the model was adjusted for age, dietary intake and adiposity. In unadjusted models, insulin was consistently statistically associated with at least two CM risk factors (OR: 1.33 (1.16–1.53)) and MetS risk (OR: 1.24 (1.12–1.37)) and in adjusted models it was found to be associated with at least two CM risk factors and MetS risk (OR: 1.87 (1.24–2.83) and OR: 1.25 (1.09–1.43)) respectively. Area under curve (AUC) for receiver operating characteristics (ROC) demonstrated a good discriminatory diagnostics power of insulin with AUC: 0.775 (0.683–0.866) and 0.785 by cross validation and bootstrapping samples for at least two CM risk factors and AUC: 0.773 (0.653–0.893) and 0.783 by cross validation and bootstrapping samples for MetS risk. This was superior to all other AUC reported from the ROC analysis of other biomarkers. Area under precision-recall curve for insulin was also superior to all other markers (0.839 and 0.586 for at least two CM risk factors and MetS, respectively). Conclusion: Fasting serum insulin concentration was statistically linked with MetS and its risk, and this link is stronger than all other biomarkers. Our ROC analysis confirmed the discriminatory diagnostic power of insulin as CM and MetS risk correlate in older adults.