Race- and sex-specific associations of parental education with insulin resistance in middle-aged participants: the CARDIA study

Using optimal subset regression to identify factors associated with insulin resistance and construct predictive models in the US adult population

BACKGROUND

In recent decades, with the development of the global economy and the improvement of living standards, insulin resistance (IR) has become a common phenomenon. Current studies have shown that IR varies between races. Therefore, it is necessary to develop individual prediction models for each country. The purpose of this study was to develop a predictive model of IR applicable to the US population.

METHOD

In total, 11 cycles of data from the NHANES database were selected for this study. Of these, participants from 1999 to 2010 (n = 14931) were used to establish the model, and participants from 2011 to 2020 (n = 13,646) were used to validate the model. Univariate and multivariable logistic regression was used to analyze the factors associated with IR. Optimal subset regression was used to filter the best modeling variables. ROC curves, calibration curves, and decision curve analysis were used to determine the strengths and weaknesses of the model.

RESULTS

After screening the variables by optimal subset regression, variables with covariance were excluded, and a total of seven factors (including HDL, LDL, ALB, GLB, GLU, BMI, and waist) were finally included to establish the prediction model. The AUCs were 0.851 and 0.857 in the training and validation sets, respectively, and the Brier value of the calibration curve was 0.153.

CONCLUSION

The optimal subset predictive model proposed in this study has a great performance in predicting IR, and the decision curve analysis shows that it has a high net clinical benefit, which can help clinicians and epidemiologists easily detect IR and take appropriate interventions as early as possible.

Pioglitazone is an effective treatment for patients with post-stroke depression combined with type 2 diabetes mellitus

The antidepressive effects of the antidiabetic medicine, pioglitazone, were recently reported in several studies. These effects may ameliorate the depressive symptoms of patients with post-stroke depression (PSD). The present study aimed to evaluate the antidepressive effect of pioglitazone in patients with PSD combined with type 2 diabetes. A total of 118 consecutive patients with stroke who had depression were studied for an average of 3 months. The Diagnostic and Statistical Manual of Mental Disorders (fourth edition) was used to assess whether a patient was depressed or not. The severity of depression was evaluated by the Hamilton depression rating scale (HAMD). In accordance with their HAMD scores, the 118 patients were divided into a severe depression group (n=40) and a mild and moderate (MM) depression group (n=78). These subjects were then divided into pioglitazone [30 mg once daily (qd)] and metformin (0.5 g twice daily) subgroups. All patients were given fluoxetine (20 mg qd). Follow-up evaluations, which included HAMD scores, activities of daily living (ADL) scores, fasting blood glucose (FBG) levels and fasting insulin (FINS) levels, were conducted on the first and third month following the beginning of the treatment. In the MM depression group, the HAMD score in the pioglitazone subgroup was lower than that in the metformin subgroup following treatment for 1 or 3 months. In the severe depression group, the HAMD score in the pioglitazone subgroup was lower than that in the metformin subgroup following 3 months of treatment. The FINS levels of the pioglitazone subgroup gradually decreased in the 3 months of treatment. No noticeable improvement was observed in the ADL scores and FBG values. In conclusion, the results of the current study demonstrate that pioglitazone effectively decreased HAMD scores and FINS values in patients with PSD, suggesting that pioglitazone may be useful for the treatment of patients with PSD combined with type 2 diabetes.

Early origins of chronic obstructive lung diseases across the life course

Chronic obstructive lung diseases, like asthma and chronic obstructive pulmonary disease, have high prevalences and are a major public health concern. Chronic obstructive lung diseases have at least part of their origins in early life. Exposure to an adverse environment during critical periods in early life might lead to permanent developmental adaptations which results in impaired lung growth with smaller airways and lower lung volume, altered immunological responses and related inflammation, and subsequently to increased risks of chronic obstructive lung diseases throughout the life course. Various pathways leading from early life factors to respiratory health outcomes in later life have been studied, including fetal and early infant growth patterns, preterm birth, maternal obesity, diet and smoking, children's diet, allergen exposure and respiratory tract infections, and genetic susceptibility. Data on potential adverse factors in the embryonic and preconception period and respiratory health outcomes are scarce. Also, the underlying mechanisms how specific adverse exposures in the fetal and early postnatal period lead to chronic obstructive lung diseases in later life are not yet fully understood. Current studies suggest that interactions between early environmental exposures and genetic factors such as changes in DNA-methylation and RNA expression patterns may explain the early development of chronic obstructive lung diseases. New well-designed epidemiological studies are needed to identify specific critical periods and to elucidate the mechanisms underlying the development of chronic obstructive lung disease throughout the life course.

The Generation R Study: Biobank update 2015

The Generation R Study is a population-based prospective cohort study from fetal life until adulthood. The study is designed to identify early environmental and genetic causes and causal pathways leading to normal and abnormal growth, development and health from fetal life, childhood and young adulthood. In total, 9,778 mothers were enrolled in the study. Data collection in children and their parents include questionnaires, interviews, detailed physical and ultrasound examinations, behavioural observations, Magnetic Resonance Imaging and biological samples. Efforts have been conducted for collecting biological samples including blood, hair, faeces, nasal swabs, saliva and urine samples and generating genomics data on DNA, RNA and microbiome. In this paper, we give an update of the collection, processing and storage of these biological samples and available measures. Together with detailed phenotype measurements, these biological samples provide a unique resource for epidemiological studies focused on environmental exposures, genetic and genomic determinants and their interactions in relation to growth, health and development from fetal life onwards.

The Rotterdam Study: 2014 objectives and design update

The Rotterdam Study is a prospective cohort study ongoing since 1990 in the city of Rotterdam in The Netherlands. The study targets cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, oncological, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. The findings of the Rotterdam Study have been presented in over a 1,000 research articles and reports (see www.erasmus-epidemiology.nl/rotterdamstudy ). This article gives the rationale of the study and its design. It also presents a summary of the major findings and an update of the objectives and methods.