BACKGROUND: The role of inflammation in the development of gestational diabetes mellitus (GDM) has recently become a focus of research. The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI), novel indices, reflect the body's chronic immune-inflammatory state. This study aimed to investigate the associations between the SII or SIRI and GDM. METHODS: A prospective birth cohort study was conducted at Beijing Obstetrics and Gynecology Hospital from February 2018 to December 2020, recruiting participants in their first trimester of pregnancy. Baseline SII and SIRI values were derived from routine clinical blood results, calculated as follows: SII = neutrophil (Neut) count × platelet (PLT) count/lymphocyte (Lymph) count, SIRI = Neut count × monocyte (Mono) count/Lymph count, with participants being grouped by quartiles of their SII or SIRI values. Participants were followed up for GDM with a 75-g, 2-h oral glucose tolerance test (OGTT) at 24-28 weeks of gestation using the glucose thresholds of the International Association of Diabetes and Pregnancy Study Groups (IADPSG). Logistic regression was used to analyze the odds ratios (ORs) (95% confidence intervals [CIs]) for the the associations between SII, SIRI, and the risk of GDM. RESULTS: Among the 28,124 women included in the study, the average age was 31.8 ± 3.8 years, and 15.76% (4432/28,124) developed GDM. Higher SII and SIRI quartiles were correlated with increased GDM rates, with rates ranging from 12.26% (862/7031) in the lowest quartile to 20.10% (1413/7031) in the highest quartile for the SII ( Ptrend <0.001) and 11.92-19.31% for the SIRI ( Ptrend <0.001). The ORs (95% CIs) of the second, third, and fourth SII quartiles were 1.09 (0.98-1.21), 1.21 (1.09-1.34), and 1.39 (1.26-1.54), respectively. The SIRI findings paralleled the SII outcomes. For the second through fourth quartiles, the ORs (95% CIs) were 1.24 (1.12-1.38), 1.41 (1.27-1.57), and 1.64 (1.48-1.82), respectively. These associations were maintained in subgroup and sensitivity analyses. CONCLUSION The SII and SIRI are potential independent risk factors contributing to the onset of GDM.
Humans ; Female ; Pregnancy ; Diabetes, Gestational/immunology* ; Prospective Studies ; Adult ; Inflammation/immunology* ; Glucose Tolerance Test ; Birth Cohort

Objective:To investigate the influence of frailty on post-treatment outcomes in elderly heart failure patients with reduced ejection fraction treated with Sacubitril/Valsartan.Methods:The 231 heart failure patients aged 60 years or over with reduced ejection fraction were enrolled from October 2017 to October 2018 in Department of Geriatric Medicine, Henan Provincial People's Hospital.Patients were divided into the frailty group(n=116)and the control group(n=115). Frailty diagnosis was made by five indexes suggested by LP Fried.Both groups were treated with sacubitril/valsartan(49/51 mg)for 1 year.The left ventricular ejection(LVEF), estimated glomerular filtration rate(eGFR), N-terminal pro B-type natriuretic peptide(NT-proBNP)and other clinical and laboratory indexes were detected before and after treatment and compared between the frailty group and the control group.Results:16 subjects in the frailty group and 11 subjects in the control group dropped out of the study.The frailty group versus the control group showed a higher mortality rate of cardiovascular causes(13.0% or 13/100 vs.6.7% or 7/104, χ2=6.437, P=0.027), a higher first re-hospitalization rate(18.0% or 18/100 vs.11.5% or 12/104, χ2=4.458, P=0.043)and a higher all-cause mortality(16.0% or 16/100 vs.8.6% or 9/104, χ2=3.875, P=0.039). In the frailty group, levels of serum NT-proBNP and creatinine were higher and eGFR was lower after treatment than before treatment[(2 253±144) ng/L vs.(2 094±136) ng/L, (137±24) μmol/L vs.(125±23) μmol/L, (49.2±5.9) ml·min -1·1.73 m -2vs.(56.7±6.3) ml·min -1·1.73 m -2, t=3.674, 2.893 and 2.068, P=0.017, 0.026 and 0.029]. In the control group, serum NT-proBNP levels were lower after treatment than before treatment[(1 828±123) ng/L vs.(1 945±128) ng/L, t=1.896, P=0.043], while serum creatinine levels[(120±22) μmol/L vs.(117±19) μmol/L, t=2.099, P=0.650]and eGFR[(59.8±6.5) ml·min -1·1.73 m -2vs.(61.6±6.8) ml·min -1·1.73 m -2, t=2.444, P=0.173]had no significant difference between post-treatment and pre-treatment. Conclusions:Frailty has adverse affects on the mortality, re-hospitalization rate and renal function in elderly heart failure patients with reduced ejection fraction treated with Sacubitril/Valsartan.

With the development of mass spectrometry technologies and bioinformatics analysis algorithms, disease research-driven human proteome project (HPP) is advancing rapidly. Protein biomarkers play critical roles in clinical applications and the biomarker discovery strategies and methods have become one of research hotspots. Feature selection and machine learning methods have good effects on solving the "dimensionality" and "sparsity" problems of proteomics data, which have been widely used in the discovery of protein biomarkers. Here, we systematically review the strategy of protein biomarker discovery and the frequently-used machine learning methods. Also, the review illustrates the prospects and limitations of deep learning in this field. It is aimed at providing a valuable reference for corresponding researchers.
Algorithms ; Biomarkers ; Humans ; Machine Learning ; Mass Spectrometry ; Proteomics