Introduction: This study aimed to examine the risk of common diseases among people with pre-diabetes and explored the relationship between pre-diabetes and multimorbidity (in this case, two or more comorbid diseases). Methods: An observational multicohort study using data from the UK Biobank database and the National Inpatient Sample (NIS) database (2016–2018) was conducted. We analysed 461 535 participants and 17 548 442 patients aged 18 years or older from both databases, of whom 14.0% and 0.7% were diagnosed with pre-diabetes, respectively. A total of 76 common diseases of various body systems were selected as adverse health outcomes for analysis. Results: Among 64 523 individuals with pre-diabetes in the UK Biobank, the mean age was 60 years, 35 304 (54.7%) were female. There were 24 non-overlapping diseases associated with pre-diabetes with significant multiple test results in both databases, and most of them are circulatory system diseases. Compared with normoglycaemia, the confounder-adjusted HR in the UK Biobank for pre-diabetes was 1.46 (95% CI 1.43 to 1.49) for accompanying complex multimorbidity (ie, four or more pre-diabetes-related diseases), the corresponding confounder-adjusted OR in the NIS study was 10.03 (95% CI 9.66 to 10.40). Conclusion: Pre-diabetes was associated with a significantly higher risk of multimorbidity. Pre-diabetes, thus, might represent an important target for multimorbidity prevention, and stronger emphasis on its management seems necessary to reduce the risk of the development of multiple comorbidities, especially before the onset of overt diabetes.

(1) Background: As the introduction of “positive” diagnostic criteria for metabolic dysfunction-associated fatty liver disease (MAFLD) does not exclude alcohol consumption, some patients originally diagnosed with alcoholic fatty liver disease (AFLD) may be diagnosed with dual- etiology fatty liver disease (AFLD&MAFLD), which requires us to urgently explore the impact of the changes in this classification of AFLD on clinical manifestations. (2) Methods: Utilizing data from the Nationwide Inpatient Sample database 2016–2018, a total of 9269 participants with AFLD were selected. With the definition of MAFLD, these patients were further categorized into two groups: single AFLD and AFLD&MAFLD. The primary outcome was the risk of comorbidities and organ failures. The secondary outcomes were the length of stay, total charges, and in-hospital all-cause mortality. (3) Results: The patients with AFLD&MAFLD were older, were predominantly male, and had more comorbidities and organ failures compared to the patients with AFLD. These comorbidities included coronary atherosclerosis, myocardial infarction, cerebrovascular disease, arrhythmia, asthma, chronic obstructive pulmonary disease, and chronic kidney disease (all p values < 0.05). The patients with AFLD&MAFLD were more likely to develop acute and chronic heart and/or kidney failures than those with single AFLD (all p < 0.05). The length of stay and total charges of the patients in the AFLD&MAFLD group were greater than the single AFLD group (p = 0.029 and p < 0.001, respectively). No significant difference in all-cause mortality was observed. (4) Conclusions: The patients with AFLD&MAFLD have more comorbidities and organ failures, longer hospital stays, and higher hospitalization costs than the patients with single AFLD. Hence, patients with dual-etiology fatty liver disease deserve more attention from clinical staff during treatment.

Background: The present definition of obesity based on body mass index (BMI) is not accurate and effective enough to identify hospitalized patients with a heavier burden, especially for postmenopausal hospitalized patients concomitant with osteoporosis. The link between common concomitant disorders of major chronic diseases such as osteoporosis, obesity, and metabolic syndrome (MS) remains unclear. Here, we aim to evaluate the impact of different metabolic obesity phenotypes on the burden of postmenopausal hospitalized patients concomitant with osteoporosis in view of unplanned readmissions. Methods: Data was acquired from the National Readmission Database 2018. The study population was classified into metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO) patients. We estimated the associations between metabolic obesity phenotypes and 30- and 90-day unplanned readmissions. A multivariate Cox Proportional Hazards (PH) model was used to assess the effect of factors on endpoints, with results expressed as HR and 95% CI. Results: The 30-day and 90-day readmission rates for the MUNO and MUO phenotypes were higher than that of the MHNO group (all p < 0.05), whereas no significant difference was found between the MHNO and MHO groups. For 30-day readmissions, MUNO raised the risk mildly (hazard ratio [HR] = 1.110, p < 0.001), MHO had a higher risk (HR = 1.145, p = 0.002), and MUO further elevated this risk (HR = 1.238, p < 0.001). As for 90-day readmissions, both MUNO and MHO raised the risk slightly (HR = 1.134, p < 0.001; HR = 1.093, p = 0.014, respectively), and MUO had the highest risk (HR = 1.263, p < 0.001). Conclusions: Metabolic abnormalities were associated with elevated rates and risks of 30- or 90-day readmission among postmenopausal hospitalized women complicated with osteoporosis, whereas obesity did not seem to be innocent, and the combination of these factors led to an additional burden on healthcare systems and individuals. These findings indicate that clinicians and researchers should focus not only on weight management but also metabolism intervention among patients with postmenopausal osteoporosis.

Objective: The aim of this study was to evaluate the admission indicators and characteristics of individuals diagnosed with type 1 diabetes (T1D) to ascertain potential impact on the choice of glucose control therapy after discharge.Methods: A total of 398 eligible T1D patients were selected. We conducted multivariable logistic regression analysis to determine the independent influence of predictors on the selection of glucose control therapy after discharge. To explore the influencing factors of different subgroups, we additionally performed subgroup analyses based on gender and age.Results: Our study revealed that body mass index (BMI) was noteworthy influence factor for prescription of insulin and non-insulin antidiabetic drug (NIAD prescription) in T1D patients of general population [OR = 1.109 (1.033-1.195), p = 0.006], male [OR = 1.166 (1.040−1.318), p = 0.011] and individuals below the age of 30 years [OR = 1.146 (1.020−1.301), p = 0.028]. Diastolic blood pressure (DBP) was a protective factor for NIAD prescription in the general population [OR = 0.971 (0.949-0.992), p = 0.008] and women [OR = 0.955 (0.923−0.988), p = 0.008]. The other risk factor of NIAD prescription in men was dyslipidemia [OR = 4.824 (1.442−22.246), p = 0.020]. Pulse pressure [OR = 1.036 (1.007–1.068), p = 0.016] constituted an additional risk factor of NIAD prescription among individuals below the age of 30 years. The risk factors of NIAD prescription for people aged 30 to 50 years were length of stay [OR = 1.097 (1.014–1.196), p = 0.026] and initial blood glucose [OR = 1.078 (1.007–1.168), p = 0.047]. In the case of individuals aged above 50 years, physicians exhibited a higher tendency to prescribe supplementary non-insulin medications to men [OR = 9.385 (1.501–87.789), p = 0.029].Conclusions: We identified notable factors that influence discharge prescriptions in patients with T1D. In order to enhance the treatment outcome for the patient, clinicians ought to have a special focus on these indicators or factors.

BACKGROUND: Large prospective population-based cohort study is rare for confirming the relationship between body mass index (BMI) and Esophageal Squamous Cell Carcinoma (ESCC). METHODS: We analyzed two prospective cohorts: a Chinese screening cohort and the UK Biobank. BMI was examined continuously per 5 kg/m2 and categorically using Asian-specific cutoffs in the Chinese cohort and World Health Organization cutoffs in the UK Biobank. Multivariable Cox models, restricted cubic splines, subgroup analyses, lag analyses, age-as-time-scale models, complete-case analyses, and Fine–Gray models with death as a competing event were used to analysis the relationship between BMI and ESCC. Exploratory circulating-proteomic analyses were performed, with categorical BMI associations and incident ESCC associations evaluated in the UK Biobank. RESULTS: The Chinese cohort included 228,906 participants and 1,142 ESCC cases during 2,021,295 person-years while the UK Biobank included 466,796 participants and 274 cases during 7,102,858 person-years. In the fully adjusted model, the HR for ESCC per 5 kg/m2 higher BMI was 0.730 (95% CI, 0.659–0.808) in the Chinese cohort and 0.673 (95% CI, 0.580–0.781) in the UK Biobank. Compared with normal BMI, underweight was associated with higher ESCC risk in the Chinese cohort (HR, 1.480; 95% CI, 1.093–2.006) and the UK Biobank (HR, 3.597; 95% CI, 1.659–7.798). The association for continuous BMI remained directionally consistent after 5-year lagging (HR, 0.767 and 0.718, respectively). Age-as-time-scale and competing-risk analyses yielded similar estimates. In the exploratory proteomic analysis, six of seven prespecified proteins were measurable in the UK Biobank; four showed concordant directions across categorical BMI and incident ESCC analyses. CONCLUSIONS: Lower BMI was consistently associated with higher ESCC risk in two large prospective cohorts. The persistence of the association after lag analyses supports low BMI as a robust epidemiologic risk marker and motivates prospective evaluation of BMI in ESCC risk-stratification strategies.