The systemic immunity-inflammation index (SII) is a novel inflammatory marker, and aberrant blood lipid levels are linked to inflammation. This study aimed to look at the probable link between SII and hyperlipidemia. The current cross-sectional investigation was carried out among people with complete SII and hyperlipidemia data from the 2015–2020 National Health and Nutrition Examination Survey (NHANES). SII was computed by dividing the platelet count × the neutrophil count/the lymphocyte count. The National Cholesterol Education Program standards were used to define hyperlipidemia. The nonlinear association between SII and hyperlipidemia was described using fitted smoothing curves and threshold effect analyses. A total of 6117 US adults were included in our study. A substantial positive correlation between SII and hyperlipidemia was found [1.03 (1.01, 1.05)] in a multivariate linear regression analysis. Age, sex, body mass index, smoking status, hypertension, and diabetes were not significantly correlated with this positive connection, according to subgroup analysis and interaction testing (p for interaction > 0.05). Additionally, we discovered a non-linear association between SII and hyperlipidemia with an inflection point of 479.15 using a two-segment linear regression model. Our findings suggest a significant association between SII levels and hyperlipidemia. More large-scale prospective studies are needed to investigate the role of SII in hyperlipidemia.

Previous studies have suggested a possible association among dietary zinc and vitamin B6 intake and CVD mortality and all-cause mortality. However, evidence on the association of dietary zinc and vitamin B6 intake and their interactions with CVD mortality and all-cause mortality remains unclear. This prospective study utilized data from the US National Health and Nutrition Examination Survey (NHANES) from 1999 to 2016. After a median follow-up of 10.4 years, 4757 deaths were recorded among 36,081 participants. Higher dietary zinc intake levels (≥9.87 mg/day) were associated with lower CVD mortality (hazard ratio (HR) = 0.85, 95% confidence interval (CI): 0.83–0.87). Vitamin B6 intake levels (≥1.73 mg/day) were associated with lower CVD mortality (HR = 0.91, 95% CI: 0.86–0.96) and all-cause mortality (HR = 0.91, 95% CI: 0.90–0.93). Higher dietary zinc intake and higher vitamin B6 intake were associated with a lower risk of CVD mortality, with an interaction between dietary zinc intake levels and vitamin B intake levels (LZLV group (HR, CI): 1.21,1.12–1.29; LZHV group (HR, CI): 1.42, 1.34–1.50; LZHV group (HR, CI): 1.28, 1.14–1.45; HZHV group (HR, CI): ref). There was also a J-type association (p for nonlinear < 0.001) between the dietary zinc–vitamin B6 ratio and CVD mortality, with a high dietary zinc–vitamin B6 ratio increasing the risk of CVD mortality (HR = 1.27, 95% CI: 1.19–1.35), whereas a moderate dietary zinc–vitamin B6 ratio appeared to be beneficial for CVD mortality. These results suggest that increasing the appropriate proportion of dietary zinc and vitamin B6 intake is associated with a lower risk of CVD mortality. Furthermore, precise and representative studies are needed to verify our findings.

(1) Background: Poor adherence to management behaviors in Chinese Type 2 diabetes mellitus (T2DM) patients leads to an uncontrolled prognosis of diabetes, which results in significant economic costs for China. It is imperative to quickly locate vulnerability factors in the management behavior of patients with T2DM. (2) Methods: In this study, a thematic analysis of the collected interview materials was conducted to construct the themes of T2DM management vulnerability. We explored the applicability of the pre-trained models based on the evaluation metrics in text classification. (3) Results: We constructed 12 themes of vulnerability related to the health and well-being of people with T2DM in Tianjin. We considered that Bidirectional Encoder Representation from Transformers (BERT) performed better in this Natural Language Processing (NLP) task with a shorter completion time. With the splitting ratio of 6:3:1 and batch size of 64 for BERT, the test accuracy was 97.71%, the completion time was 10 min 24 s, and the macro-F1 score was 0.9752. (4) Conclusions: Our results proved the applicability of NLP techniques in this specific Chinese-language medical environment. We filled the knowledge gap in the application of NLP technologies in diabetes management. Our study provided strong support for using NLP techniques to rapidly locate vulnerability factors in T2DM management.

Background: Diabetes has become a global public health priority resulting in significant workforce losses and health care expenditures. Therefore, research on diabetes vulnerability has become imperative. Current methods for studying disease vulnerability mainly use qualitative research methods represented by Thematic Analysis (TCA), which has the disadvantage of being staff-intensive for long periods of time. Natural Language Processing (NLP) could achieve efficient results in information mining tasks, but we didn't find many studies talking about NLP in non-infectious chronic diseases.Methods: In this study, hyperparameters were adjusted to obtain more cost-effective model applicable to The Cities Changing Diabetes’ vulnerability data by comparing Bidirectional Encoder Representation from Transformers (BERT) and Enhanced Language Representation with Informative Entities (ERNIE) in terms of test accuracy, completion time and evaluation metrics on classification.Results: The results showed that BERT took less time for the same hyperparameter cases, and the test accuracy of ERNIE was slightly better than that of BERT. We further adjusted the Batch size of ERNIE as we found that ERNIE with the splitting ratio of 8:1:1 and Batch size of 64 had the better efficiency with the test accuracy was 97.67%, the completion time was 12min36s and Macro-F₁ score was 0.9734.Conclusions: In this study, BERT overwhelmed ERNIE in terms of completion speed with the same hyperparameters. ERNIE showed higher accuracy, especially the ideal performance at the split ratio of 8:1:1 after enhancing the Batch size. From the point of view, we pursue a model with high accuracy and fast processing speed, which means that we can obtain the highest accuracy in the shortest time. It could be selected according to the actual situation in the application process.