Effect of obesity, serum lipoproteins, and apolipoprotein E genotypes on mortality in hospitalized elderly patients

Effects of Banana Resistant Starch on the Biochemical Indexes and Intestinal Flora of Obese Rats Induced by a High-Fat Diet and Their Correlation Analysis

The effects of banana resistant starch (BRS) on obesity-related metabolic and intestinal flora were investigated in a high-fat diet-induced obesity model. After 6 weeks of intervention, the glucolipid metabolism index [blood glucose (GLU), total cholesterol (TC), triacylglycerol (TG), low density lipoprotein-cholesterol (LDL-C), and high density lipoprotein-cholesterol (HDL-C)], hormone index [leptin (LEP), insulin (INS), ghrelin, adiponectin (ADP), and thyroxine (T4)], and 16S rRNA sequencing analyses were performed for each group to explore the regulating effect of intestinal flora and the mechanism of weight loss in obese rats. The results showed that (1) BRS intervention significantly reduced the levels of GLU, TG, TC, LDL-C, LEP, and INS (p < 0.01) and increased the contents of ghrelin (p < 0.05) and ADP (p < 0.01). (2) BRS could improve the diversity of intestinal flora and regulate the overall structure of intestinal microorganisms, mainly by upregulating the Bacteroides/Firmicutes ratio and the relative abundance of Cyanobacteria and downregulating the relative abundances of Deferribacteres and Tenericutes (at the phylum level). BRS could inhibit the proliferation of Turicibacter, Romboutsia, and Oligella and increase the abundances of Bacteroides, Ruminococcaceae, and Lachnospiraceae (at the genus level). (3) Some significant correlations were observed between the gut microbiota and biomarkers. Turicibacter, Romboutsia, and Oligella were positively correlated with GLU, TG, TC, LEP, and INS and negatively correlated with ghrelin and ADP. Bacteroides, Parabacteroides, and Akkermansia were negatively correlated with GLU, TG, and TC. Conclusion: BRS had promising effects on weight loss, which could be associated with the improvement in host metabolism by regulating intestinal flora.

Hypoglycemia is independently associated with multidimensional impairment in elderly diabetic patients

Aim. To identify the characteristics associated with multidimensional impairment, evaluated through the Multidimensional Prognostic Index (MPI), a validated predictive tool for mortality derived from a standardized Comprehensive Geriatric Assessment (CGA), in a cohort of elderly diabetic patients treated with oral hypoglycemic drugs. Methods and Results. The study population consisted of 1342 diabetic patients consecutively enrolled in 57 diabetes centers distributed throughout Italy, within the Metabolic Study. Inclusion criteria were diagnosis of type 2 diabetes mellitus (DM), 65 years old or over, and treatment with oral antidiabetic medications. Data concerning DM duration, medications for DM taken during the 3-month period before inclusion in the study, number of hypoglycemic events, and complications of DM were collected. Multidimensional impairment was assessed using the MPI evaluating functional, cognitive, and nutritional status; risk of pressure sores; comorbidity; number of drugs taken; and cohabitation status. The mean age of participants was 73.3 ± 5.5 years, and the mean MPI score was 0.22 ± 0.13. Multivariate analysis showed that advanced age, female gender, hypoglycemic events, and hospitalization for glycemic decompensation were independently associated with a worse MPI score. Conclusion. Stratification of elderly diabetic patients using the MPI might help to identify those patients at highest risk who need better-tailored treatment.

APOE and LRPAP1 gene polymorphism and risk of Parkinson's disease

Epidemiologic findings suggest that lipids and alteration in lipid metabolizing protein/gene may contribute to the development of neurodegenerative disorders. The aim of the current study was to determine the serum lipid levels and genetic variation in two lipid metabolizing genes, low-density lipoprotein receptor-related protein-associated protein (LRPAP1) and apolipoprotein E (APOE) gene in Parkinson's disease (PD). Based on well-defined inclusion and exclusion criteria, this study included 70 patients with PD and 100 age-matched controls. LRPAP1 and APOE gene polymorphism were analyzed by polymerase chain reaction and restriction fragment length polymorphism, respectively. Fasting serum lipid levels were determined using an autoanalyser. The logistic regression analysis showed that high levels of serum cholesterol [odds ratio (OR) = 1.101, 95 % confidence interval (CI95%) = 1.067-1.135], LRPAP1 I allelic variant alone (OR = 2.766, CI95% = 1.137-6.752) and in combination with APOE ε4 allelic variant (OR = 4.187, CI95% = 1.621-10.82) were significantly associated with increase in PD risk. Apart from that, the high levels of LDL cholesterol appears to have a protective role (OR = 0.931, CI95% = 0.897-0.966) against PD. The LRPAP1 I allelic variant may be considered a candidate gene for PD, predominantly in patients having the APOE ε4 allelic variant.

Development and validation of a predicting model of all-cause mortality in patients with type 2 diabetes

OBJECTIVE

To develop and validate a parsimonious model for predicting short-term all-cause mortality in patients with type 2 diabetes mellitus (T2DM).

RESEARCH DESIGN AND METHODS

Two cohorts of patients with T2DM were investigated. The Gargano Mortality Study (GMS, n = 679 patients) was the training set and the Foggia Mortality Study (FMS, n = 936 patients) represented the validation sample. GMS and FMS cohorts were prospectively followed up for 7.40 ± 2.15 and 4.51 ± 1.69 years, respectively, and all-cause mortality was registered. A new forward variable selection within a multivariate Cox regression was implemented. Starting from the empty model, each step selected the predictor that, once included into the multivariate Cox model, yielded the maximum continuous net reclassification improvement (cNRI). The selection procedure stopped when no further statistically significant cNRI increase was detected.

RESULTS

Nine variables (age, BMI, diastolic blood pressure, LDL cholesterol, triglycerides, HDL cholesterol, urine albumin-to-creatinine ratio, and antihypertensive and insulin therapy) were included in the final predictive model with a C statistic of 0.88 (95% CI 0.82-0.94) in the GMS and 0.82 (0.76-0.87) in the FMS. Finally, we used a recursive partition and amalgamation algorithm to identify patients at intermediate and high mortality risk (hazard ratio 7.0 and 24.4, respectively, as compared with those at low risk). A web-based risk calculator was also developed.

CONCLUSIONS

We developed and validated a parsimonious all-cause mortality equation in T2DM, providing also a user-friendly web-based risk calculator. Our model may help prioritize the use of available resources for targeting aggressive preventive and treatment strategies in a subset of very high-risk individuals.

Development and validation of a Multidimensional Prognostic Index for mortality based on a standardized Multidimensional Assessment Schedule (MPI-SVaMA) in community-dwelling older subjects

OBJECTIVES

To develop and validate a Multidimensional Prognostic Index (MPI) for mortality based on information collected by the Multidimensional Assessment Schedule (SVaMA), the recommended standard tool for multidimensional assessment of community-dwelling older subjects in seven Italian regions.

DESIGN

Prospective cohort study.

PARTICIPANTS

Community-dwelling subjects older than 65 years who underwent an SVaMA evaluation from 2004 to 2010 in Padova Health District, Veneto, Italy.

MEASUREMENTS

The MPI-SVaMA was calculated as a weighted (weights were derived from multivariate Cox regressions) linear combination of the following nine domains: age, sex, main diagnosis, and six scores, ie, the Short Portable Mental Status Questionnaire, the Barthel index (contains two domains: activities of daily living and mobility), the Exton-Smith scale, the Nursing Care Needs, and the Social Network Support by a structured interview. Subjects were followed for a median of 2 years; those who had not died were followed for at least 1 year. The MPI-SVaMA score ranged from 0 to 1 and 3 grades of severity of the MPI-SVaMA were calculated on the basis of estimated cutoffs. Discriminatory power and calibration were further assessed.

RESULTS

A total of 12,020 subjects (mean age 81.84 ± 7.97 years) were included. Two random cohorts were selected: (1) a development cohort, ie, 7876 subjects (mean age 81.79 ± 8.05, %females: 63.1) and (2) a validation cohort, ie, 4144 subjects (mean age: 81.95 ± 7.83, %females: 63.7). The discriminatory power for mortality of MPI-SVaMA was 0.828 (95% CI 0.817-0.838) and 0.832 (95% CI 0.818-0.845) at 1 month and 0.791 (95% CI 0.784-0.798) and 0.792 (95% CI 0.783-0.802) at 1 year in development and validation cohorts, respectively. MPI-SVaMA results were well calibrated showing lower than 10% differences between predicted and observed mortality, both in development and validation cohorts.

CONCLUSIONS

The MPI-SVaMA is an accurate and well-calibrated prognostic tool for mortality in community-dwelling older subjects, and can be used in clinical decision making.