Intakes of omega-3 fatty acids and risks of all-cause and cause-specific mortality in people with diabetes: a cohort study based on NHANES 1999-2014.

AIMS: Intake of omega-3 fatty acids is associated with several health benefits, but the specific benefits in populations with diabetes have yet to be elucidated. Therefore, this study aimed to explore the relationship between intake of omega-3 fatty acids and mortality in people with diabetes. METHODS: This was a prospective cohort study and included 4854 participants with diabetes (mean age, 57.92 years; 50.9% male) from the National Health and Nutrition Examination Survey (1999-2014). Eicosapentaenoic (EPA) and docosahexaenoic (DHA) acid intake were used as alternative markers of omega-3 fatty acids intake and calculated based on the sum of the 24-h dietary recall interviews and dietary supplements. Mortality data were ascertained by linkage to National Death Index records by December 31, 2015. Cox proportional hazard models and restricted cubic spline were used to assess the relationship between EPA and DHA intake and all-cause and cause-specific mortality. Statistical analyses were performed using R 4.2.0 software. RESULTS: Compared with participants with a lower EPA + DHA intake, participants who had a higher EPA + DHA intake tended to be Non-Hispanic Black; were more likely to be obese; and had higher daily energy intake and family income. During 34,386 person-years of follow-up, 1102 deaths were documented, including 266 cardiovascular disease deaths and 152 cancer deaths. In multivariable regression analyses with adjustment of confounding factors, higher EPA + DHA intake was significantly and linearly related to lower all-cause mortality: there was a 25% reduced risk of all-cause mortality. CONCLUSIONS: Higher omega-3 fatty acid intake was independently related to lower all-cause mortality in individuals with diabetes, suggesting an adequate intake of omega-3 fatty acids may prevent premature death among the population with diabetes.

Analysis of treatment pathways for three chronic diseases using OMOP CDM.

The present study examined treatment pathways (the ordered sequence of medications that a patient is prescribed) for three chronic diseases (hypertension, type 2 diabetes, and depression), compared the pathways with recommendations from guidelines, discussed differences and standardization of medications in different medical institutions, explored population diversification and changes of clinical treatment, and provided clinical big data analysis-based data support for the development and study of drugs in China. In order to run the "Treatment Pathways in Chronic Disease" protocol in Chinese data sources,we have built a large data research and analysis platform for Chinese clinical medical data. Data sourced from the Clinical Data Repository (CDR) of the First Affiliated Hospital of Nanjing Medical University was extracted, transformed, and loaded into an observational medical outcomes partnership common data model (OMOP CDM) Ver. 5.0. Diagnosis and medication information for patients with hypertension, type 2 diabetes, and depression from 2005 to 2015 were extracted for observational research to obtain treatment pathways for the three diseases. The most common medications used to treat diabetes and hypertension were metformin and acarbose, respectively, at 28.5 and 20.9% as first-line medication. New drugs were emerging for depression; therefore, the favorite medication changed accordingly. Most patients with these three diseases had different treatment pathways from other patients with the same diseases. The proportions of monotherapy increased for the three diseases, especially in recent years. The recommendations presented in guidelines show some predominance. High-quality, effective guidelines incorporating domestic facts should be established to further guide medication and improve therapy at local hospitals. Medical institutions at all levels could improve the quality of medical services, and further standardize medications in the future. This research is the first application of the CDM model and OHDSI software in China, which were used to study, treatment pathways for three chronic diseases (hypertension, type 2 diabetes and depression), compare the pathways with recommendations from guidelines, discuss differences and standardization of medications in different medical institutions, demonstrate the urgent need for quality national guidelines, explores population diversification and changes of clinical treatment, and provide clinical big data analysis-based data support for the development and study of drugs in China.