Clinical Network for Big Data and Personalized Health: Study Protocol and Preliminary Results

The use of secondary hospital-based clinical data and electronical health records (EHR) represent a cost-efficient alternative to investigate chronic conditions. We present the Clinical Network Big Data and Personalised Health project, which collects EHRs for patients accessing hospitals in Central-Southern Italy, through an integrated digital platform to create a digital hub for the collection, management and analysis of personal, clinical and environmental information for patients, associated with a biobank to perform multi-omic analyses. A total of 12,864 participants (61.7% women, mean age 52.6 ± 17.6 years) signed a written informed consent to allow access to their EHRs. The majority of hospital access was in obstetrics and gynaecology (36.3%), while the main reason for hospitalization was represented by diseases of the circulatory system (21.2%). Participants had a secondary education (63.5%), were mostly retired (25.45%), reported low levels of physical activity (59.6%), had low adherence to the Mediterranean diet and were smokers (30.2%). A large percentage (35.8%) were overweight and the prevalence of hypertension, diabetes and hyperlipidemia was 36.4%, 11.1% and 19.6%, respectively. Blood samples were retrieved for 8686 patients (67.5%). This project is aimed at creating a digital hub for the collection, management and analysis of personal, clinical, diagnostic and environmental information for patients, and is associated with a biobank to perform multi-omic analyses

Prevalence and cardiovascular risk profile of chronic kidney disease in Italy: Results of the 2008-12 National Health Examination Survey

Background National surveys in countries outside Europe have reported a high prevalence (11-13%) of chronic kidney disease (CKD). Studies in Europe have provided a variable prevalence likely due to differences in study design, including age and extent of geographic areas, equation used to evaluate estimated glomerular filtration rate (eGFR) and CKD stages examined. Methods The 2008-12 National Health Examination Survey in Italy randomly extracted samples from the general population aged 35-79 years, stratified by age and gender, from the resident list of each Italian region (440 persons/1.5 million of residents). We estimated the prevalence of CKD by means of urinary albumin: creatinine ratio and eGFR (CKD-EPI equation-enzymatic assay of serum creatinine). Cardiovascular (CV) risk profile was also evaluated. Results Three thousand eight hundred and forty-eight men and 3704 women were examined. In the whole population, mean age was 57 ± 12 and 56 ± 12 years in men and women, respectively; hypertension was prevalent in men and women, respectively (56 and 43%) and the same held true for overweight (48 and 33%), obesity (26 and 27%), diabetes (14 and 9%) and smoking (21 and 18%), whereas CV disease was less frequent (9 and 6%). Overall, the prevalence of CKD (95% confidence interval) was 7.05% (6.48-7.65). Early stages constituted 59% of the CKD population [Stage G1-2 A2-3: 4.16% (3.71-4.61) and Stage G3-5: 2.89% (2.51-3.26)]. At multivariate regression analysis, age, obesity, hypertension, diabetes, CV disease and smoking were all independent correlates of CKD. Conclusions CKD has a relatively lower prevalence in Italy, in particular for advanced stages, when compared with similar national surveys outside Europe. This occurs despite older age and unfavourable CV risk profile of the whole population

The association of high-sensitivity c-reactive protein and other biomarkers with cardiovascular disease in patients treated for HIV: a nested case–control study

Background: Elevated high-sensitivity C-reactive protein (hsCRP) increases the risk of cardiovascular disease (CVD) in the general population, but its role as a predictive marker in HIV-positive patients remains unclear. Aim of the study was to evaluate whether hsCRP or other biomarkers are independent predictors of CVD risk in HIV-infected patients.Methods: Retrospective, nested case-control study. HIV-positive men and women (35-69 years of age) receiving combination antiretroviral therapy (cART) were included. Cases (n = 35) had a major CVD event. Controls (n = 74) free from CVD events for at least 5 years from starting ART were matched on diabetes and smoking. HsCRP, D-dimer, P-selectin, interleukin-6 (IL-6), tissue plasminogen activator, plasminogen activator inhibitor-1 levels were measured.Results: High hsCRP was associated with CVD risk, independently of traditional cardiovascular risk factors, HIV replication and the type of ART received at the time of sampling (adjusted odds ratio 8.00 [1.23-51.94] comparing >3.3 mg/L with <0.9 mg/L; P = 0.03). Higher IL-6 and P-selectin levels were also independently associated with increased CVD risk, although the association was weaker than for hsCRP. Higher total cholesterol and lower HDL cholesterol increased CVD risk, independent of hsCRP.Conclusion: hsCRP may be a useful additional biomarker to predict CVD risk in HIV-infected patients receiving cART

Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

PurposeRadiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity. Methods and materialsOne hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (I-M) and erythema (I-E) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of &gt;= 2. The patient's dataset was randomly split into a training and testing set used for model development/validation and testing (75%/25% split). A 5-times repeated holdout cross-validation was performed. Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin toxicity prediction purposes. ResultsThirty-four (26.4%) patients presented with adverse skin effects (RTOG &gt;= 2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (I-M,I-T0 and I-E,I-T0), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p&lt;0.05) in univariate analysis. The diagnostic performances measured by the area-under-curve (AUC) were 0.816, 0.734, 0.714, 0.691 and 0.664 for IM, IE, PTV2, PTV1 and BMI, respectively. Classification performances reported precision, recall and F1-values greater than 0.8 for all models. The SVM classifier using the RBF kernel had the best performance, with accuracy, precision, recall and F-score equal to 89.8%, 88.7%, 98.6% and 93.3%, respectively. CART analysis classified patients with I-M,I-T0 &gt;= 99 to be associated with RTOG &gt;= 2 toxicity; subsequently, PTV1 and PTV2 played a significant role in increasing the classification rate. The CART model provided a very high diagnostic performance of AUC=0.959. ConclusionsSpectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG &gt;= 2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life

Association of nutritional glycaemic indices with global DNA methylation patterns: results from the Moli-sani cohort

Background: High dietary glycaemic index (GI) and load (GL) have been associated with increased risk of various cardiometabolic conditions. Among the molecular potential mechanisms underlying this relationship, DNA methylation has been studied, but a direct link between high GI and/or GL of diet and global DNA methylation levels has not been proved yet. We analyzed the associations between GI and GL and global DNA methylation patterns within an Italian population. Results: Genomic DNA methylation (5mC) and hydroxymethylation (5hmC) levels were measured in 1080 buffy coat samples from participants of the Moli-sani study (mean(SD) = 54.9(11.5) years; 52% women) via ELISA. A 188-item Food Frequency Questionnaire was used to assess food intake and dietary GI and GL for each participant were calculated. Multiple linear regressions were used to investigate the associations between dietary GI and GL and global 5mC and 5hmC levels, as well as the proportion of effect explained by metabolic and inflammatory markers. We found negative associations of GI with both 5mC (β (SE) = - 0.073 (0.027), p = 0.007) and 5hmC (- 0.084 (0.030), p = 0.006), and of GL with 5mC (- 0.14 (0.060), p = 0.014). Circulating biomarkers did not explain the above-mentioned associations. Gender interaction analyses revealed a significant association of the gender-x-GL interaction with 5mC levels, with men showing an inverse association three times as negative as in women (interaction β (SE) = - 0.16 (0.06), p = 0.005). Conclusions: Our findings suggest that global DNA methylation and hydroxymethylation patterns represent a biomarker of carbohydrate intake. Based on the differential association of GL with 5mC between men and women, further gender-based separate approaches are warranted

Plasma fibrinogen levels and all-cause and cause-specific mortality in an Italian adult population: results from the Moli-sani study

Epidemiological data on the association between fibrinogen levels and mortality are scarse and controversial. Longitudinal analyses were performed, separately by sex, on 17,689 individuals from the Moli-sani study [53% women, ≥35 years, free from cardiovascular disease (CVD) or cancer at enrolment], to evaluate the association between plasma fibrinogen and all-cause and cause-specific mortality. Over a median follow-up of 11.2 years, 1,058 deaths (34.7% CVD, 36.3% cancer) were ascertained. Both in the lowest (1.12-2.64 g/L) and highest (≥3.62 g/L) fibrinogen quintiles, women had an increased all-cause mortality hazard, when compared with third quintile (2.97-3.23 g/L). Dose-response analyses showed a U-shaped relationship in women (P overall <0.0001; P non-linear association <0.0001), but a positive linear association for all-cause mortality in men (P overall 0.0038; P non-linear association 0.76). Similar trends for a U-shaped association were observed for CVD mortality, while no association was observed with cancer deaths. A U-shaped association of fibrinogen levels with other-cause mortality was also found in both sexes. This study shows that not only higher but also lower fibrinogen levels represent hazard for mortality when compared to normal levels; U-shaped curves being prevalently observed in women

Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

PurposeRadiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity.Methods and materialsOne hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (IM) and erythema (IE) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of ≥ 2. The patient’s dataset was randomly split into a training and testing set used for model development/validation and testing (75%/25% split). A 5-times repeated holdout cross-validation was performed. Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin toxicity prediction purposes.ResultsThirty-four (26.4%) patients presented with adverse skin effects (RTOG ≥2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (IM,T0 and IE,T0), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p&lt;0.05) in univariate analysis. The diagnostic performances measured by the area-under-curve (AUC) were 0.816, 0.734, 0.714, 0.691 and 0.664 for IM, IE, PTV2, PTV1 and BMI, respectively. Classification performances reported precision, recall and F1-values greater than 0.8 for all models. The SVM classifier using the RBF kernel had the best performance, with accuracy, precision, recall and F-score equal to 89.8%, 88.7%, 98.6% and 93.3%, respectively. CART analysis classified patients with IM,T0 ≥ 99 to be associated with RTOG ≥ 2 toxicity; subsequently, PTV1 and PTV2 played a significant role in increasing the classification rate. The CART model provided a very high diagnostic performance of AUC=0.959.ConclusionsSpectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG ≥2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

Background Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories. Methods We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5–19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI &lt;18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For school&#x2;aged children and adolescents, we report thinness (BMI &lt;2 SD below the median of the WHO growth reference) and obesity (BMI &gt;2 SD above the median). Findings From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness. Interpretation The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesit

Rising rural body-mass index is the main driver of the global obesity epidemic in adults

Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities(.)(1,2) This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity(3-6). Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017-and more than 80% in some low- and middle-income regions-was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing-and in some countries reversal-of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.Peer reviewe

Global variation in diabetes diagnosis and prevalence based on fasting glucose and hemoglobin A1c

Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) are both used to diagnose diabetes, but these measurements can identify different people as having diabetes. We used data from 117 population-based studies and quantified, in different world regions, the prevalence of diagnosed diabetes, and whether those who were previously undiagnosed and detected as having diabetes in survey screening, had elevated FPG, HbA1c or both. We developed prediction equations for estimating the probability that a person without previously diagnosed diabetes, and at a specific level of FPG, had elevated HbA1c, and vice versa. The age-standardized proportion of diabetes that was previously undiagnosed and detected in survey screening ranged from 30% in the high-income western region to 66% in south Asia. Among those with screen-detected diabetes with either test, the age-standardized proportion who had elevated levels of both FPG and HbA1c was 29-39% across regions; the remainder had discordant elevation of FPG or HbA1c. In most low- and middle-income regions, isolated elevated HbA1c was more common than isolated elevated FPG. In these regions, the use of FPG alone may delay diabetes diagnosis and underestimate diabetes prevalence. Our prediction equations help allocate finite resources for measuring HbA1c to reduce the global shortfall in diabetes diagnosis and surveillance