Phenotypic prevalence of obesity and metabolic syndrome among an underdiagnosed and underscreened population of over 50 million children and adults

Unsupervised clustering of longitudinal clinical measurements in electronic health records

Longitudinal electronic health records (EHR) can be utilized to identify patterns of disease development and progression in real-world settings. Unsupervised temporal matching algorithms are being repurposed to EHR from signal processing- and protein-sequence alignment tasks where they have shown immense promise for gaining insight into disease. The robustness of these algorithms for classifying EHR clinical data remains to be determined. Timeseries compiled from clinical measurements, such as blood pressure, have far more irregularity in sampling and missingness than the data for which these algorithms were developed, necessitating a systematic evaluation of these methods. We applied 30 state-of-the-art unsupervised machine learning algorithms to 6,912 systematically generated simulated clinical datasets across five parameters. These algorithms included eight temporal matching algorithms with fourteen partitional and eight fuzzy clustering methods. Nemenyi tests were used to determine differences in accuracy using the Adjusted Rand Index (ARI). Dynamic time warping and its lower-bound variants had the highest accuracies across all cohorts (median ARI>0.70). All 30 methods were better at discriminating classes with differences in magnitude compared to differences in trajectory shapes. Missingness impacted accuracies only when classes were different by trajectory shape. The method with the highest ARI was then used to cluster a large pediatric metabolic syndrome (MetS) cohort (N = 43,426). We identified three unique childhood BMI patterns with high average cluster consensus (>70%). The algorithm identified a cluster with consistently high BMI which had the greatest risk of MetS, consistent with prior literature (OR = 4.87, 95% CI: 3.93-6.12). While these algorithms have been shown to have similar accuracies for regular timeseries, their accuracies in clinical applications vary substantially in discriminating differences in shape and especially with moderate to high missingness (>10%). This systematic assessment also shows that the most robust algorithms tested here can derive meaningful insights from longitudinal clinical data.

Socioeconomic Disparities in Glaucoma Severity at Initial Diagnosis: A Nationwide Electronic Health Record Cohort Analysis

PURPOSE

To assess disparities in initial disease severity among open-angle glaucoma (OAG) patients.

DESIGN

Cross-sectional study.

METHODS

In this analysis of Epic Cosmos, an aggregated electronic health record dataset encompassing >213 million patients, OAG patients examined in ophthalmology or optometry clinics between January 1, 2013, and June 1, 2023, were evaluated. OAG severity at presentation was classified as mild, moderate, or severe using International Classification of Disease-10 codes. Demographics, social vulnerability index (SVI) scores, and rural-urban commuting area codes were evaluated as predictors of disease stage using ordinal logistic regression. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated.

RESULTS

Of 245,669 patients, 38.1% had mild, 32.5% moderate, and 29.3% severe disease at presentation. In multivariable analyses, significant determinants of worse severity included older age (OR: 1.23 per decade, 95% CI: 1.22-1.23), male sex (OR: 1.37, 95% CI: 1.35-1.39), Black race (OR: 1.61, 95% CI: 1.58-1.65), Hispanic ethnicity (OR: 1.15, 95% CI: 1.11-1.18), non-commercial insurance or uninsured status (OR: 2.53, 95% CI: 2.33-2.74), secondary OAGs (eg, pseudoexfoliative glaucoma - OR: 1.65, 95% CI: 1.58-1.72), and higher socioeconomic SVI scores (OR: 1.25 for highest versus lowest quartile, 95% CI: 1.22-1.28). Black and Hispanic patients were diagnosed at younger ages compared to White patients (mean ages: 67.8 ± 12.3 and 68.1 ± 12.8 vs 73.3 ± 11.8 years respectively, P < .001).

CONCLUSIONS

Worse OAG at presentation was associated with older age, male sex, Black race, Hispanic ethnicity, non-commercial insurance or uninsured status, secondary OAGs, and greater socioeconomic vulnerability in this nationwide cohort. These findings can help tailor screening programs towards vulnerable populations.

Autoimmune, Autoinflammatory Disease and Cutaneous Malignancy Associations with Hidradenitis Suppurativa: A Cross-Sectional Study

BACKGROUND

Hidradenitis suppurativa (HS) is a debilitating cutaneous disease characterized by severe painful inflammatory nodules/abscesses. At present, data regarding the epidemiology and pathophysiology of this disease are limited.

OBJECTIVE

To define the prevalence and comorbidity associations of HS.

METHODS

This was a cross-sectional study of EPICTM Cosmos© examining over 180 million US patients. Prevalences were calculated by demographic and odds ratios (OR) and identified comorbidity correlations.

RESULTS

All examined metabolism-related, psychological, and autoimmune/autoinflammatory (AI) diseases correlated with HS. The strongest associations were with pyoderma gangrenosum [OR 26.56; confidence interval (CI): 24.98-28.23], Down syndrome (OR 11.31; CI 10.93-11.70), and polycystic ovarian syndrome (OR 11.24; CI 11.09-11.38). Novel AI associations were found between HS and lupus (OR 6.60; CI 6.26-6.94) and multiple sclerosis (MS; OR 2.38; CI 2.29-2.48). Cutaneous malignancies were largely not associated in the unsegmented cohort; however, among Black patients, novel associations with melanoma (OR 2.39; CI 1.86-3.08) and basal cell carcinoma (OR 2.69; CI 2.15-3.36) were identified.

LIMITATIONS

International Classification of Diseases (ICD)-based disease identification relies on coding fidelity and diagnostic accuracy.

CONCLUSION

This is the first study to identify correlations between HS with melanoma and basal cell carcinoma (BCC) among Black patients as well as MS and lupus in all patients with HS.

A comprehensive analysis concerning eating behavior associated with chronic diseases among Romanian community nurses

Introduction

Lifestyle factors, including inadequate eating patterns, emerge as a critical determinant of chronic disease. Apart from caring for patients, nurses should also take an active role in monitoring and managing their own health. Understanding the intricate relationship between nurses' eating behavior and managing their own health is crucial for fostering a holistic approach to healthcare, therefore our study aimed to evaluate eating behavior and demographic factors influencing chronic disease prevalence in a sample of community nurses from Romania.

Methods

Between October-November 2023, 1920 community nurses were invited to answer an online survey, using an advertisement in their professional network. Of them, 788 responded. In the survey, which included a semi-quantitative food frequency questionnaire with 53 food items, the Intuitive Eating Survey 2 (IES-2), and demographic items were used.

Results

A multivariate model was built for the prediction of the association between eating behavior and other factors associated with chronic diseases. The majority of participants were females (95.1%), with the largest age group falling between 40 and 49.9 years (48.2%). Regarding the EFSA criteria for adequate carbohydrate and fat intake, 20.2% of the group have a high intake of carbohydrates, respectively, 43.4% of the group have a high intake of fat. Analysis of chronic diseases indicated that 24.9% of individuals reported at least one diagnosis by a physician. The presence of chronic disease was associated with a low level of perceived health status, with an OR = 3.388, 95%CI (1.684-6.814), compared to those reporting excellent or very good perceived health status. High stress had an OR = 1.483, 95%CI (1.033-2.129). BMI had an OR = 1.069, 95%CI (1.032-1.108), while low carbohydrate diet score had an OR = 0.956, 95%CI (0.920-0.992). Gender and IES-2 did not significantly contribute to the model, but their effect was controlled.

Discussion

By unraveling the intricate interplay between nutrition, lifestyle, and health outcomes in this healthcare cohort, our findings contribute valuable insights for the development of targeted interventions and support programs tailored to enhance the well-being of community nurses and, by extension, the patients they support.

Chronic and postprandial effect of blueberries on cognitive function, alertness, and mood in participants with metabolic syndrome - results from a six-month, double-blind, randomized controlled trial

BACKGROUND

Anthocyanin and blueberry intakes positively associated with cognitive function in population-based studies and cognitive benefits in randomized controlled trials of adults with self-perceived or clinical cognitive dysfunction. To date, adults with metabolic syndrome (MetS) but without cognitive dysfunction are understudied.

OBJECTIVES

Cognitive function, mood, alertness, and sleep quality were assessed as secondary end points in MetS participants, postprandially (>24 h) and following 6-mo blueberry intake.

METHODS

A double-blind, randomized controlled trial was conducted, assessing the primary effect of consuming freeze-dried blueberry powder, compared against an isocaloric placebo, on cardiometabolic health >6 mo and a 24 h postprandial period (at baseline). In this secondary analysis of the main study, data from those completing mood, alertness, cognition, and sleep assessments are presented (i.e., n = 115 in the 6 mo study, n = 33 in the postprandial study), using the following: 1) Bond-Lader self-rated scores, 2) electronic cognitive battery (i.e., testing attention, working memory, episodic memory, speed of memory retrieval, executive function, and picture recognition), and 3) the Leeds Sleep Evaluation Questionnaire. Urinary and serum anthocyanin metabolites were quantified, and apolipoprotein E genotype status was determined.

RESULTS

Postprandial self-rated calmness significantly improved after 1 cup of blueberries (P = 0.01; q = 0.04; with an 11.6% improvement compared with baseline between 0 and 24 h for the 1 cup group), but all other mood, sleep, and cognitive function parameters were unaffected after postprandial and 6-mo blueberries. Across the ½ and 1 cup groups, microbial metabolites of anthocyanins and chlorogenic acid (i.e., hydroxycinnamic acids, benzoic acids, phenylalanine derivatives, and hippuric acids) and catechin were associated with favorable chronic and postprandial memory, attention, executive function, and calmness.

CONCLUSIONS

Although self-rated calmness improved postprandially, and significant cognition-metabolite associations were identified, our data did not support strong cognitive, mood, alertness, or sleep quality improvements in MetS participants after blueberry intervention. This trial was registered at clinicaltrials.gov as NCT02035592.

Comparison of Diagnostic Models to Estimate the Risk of Metabolic Syndrome in a Chilean Pediatric Population: A Cross-Sectional Study

The pediatric population has various criteria for measuring metabolic syndrome (MetS). The diversity of consensus for diagnosis has led to different non-comparable reported prevalence. Given the increase in its prevalence in pediatric ages, it is necessary to develop efficient methods to encourage early detection. Consequently, early screening for the risk of MetS could favor timely action in preventing associated comorbidities in adulthood. This study aimed to establish the diagnostic capacity of models that use non-invasive (anthropometric) and invasive (serum biomarkers) variables for the early detection of MetS in Chilean children. A cross-sectional study was carried out on 220 children aged 6 to 11. Multivariate logistic regressions and discriminant analyses were applied to determine the diagnostic capacity of invasive and non-invasive variables. Based on these results, four diagnostic models were created and compared: (i) anthropometric, (ii) hormonal (insulin, leptin, and adiponectin), (iii) Lipid A (high-density cholesterol lipoprotein [HDL-c] and triglycerides [TG]) and (iv) Lipid B (TG/HDL-c). The prevalence of MetS was 26.8%. Lipid biomarkers (HDL-c and TG) and their ratio (TG/HDL-c) presented higher diagnostic capacity, above 80%, followed by body mass index (BMI, 0.71-0.88) and waist-to-height ratio (WHtR, 0.70-0.87). The lipid model A was the most accurate (sensitivity [S] = 62.7%, specificity [E] = 96.9%, validity index 87.7%), followed by the anthropometric model (S = 69.5%, E = 88.8% and validity index = 83.6%). In conclusion, detecting MetS was possible through invasive and non-invasive methods tested in overweight and obese children. The proposed models based on anthropometric variables, or serum biomarkers of the lipid model A, presented acceptable validity indices. Moreover, they were higher than those that measured adipokines, leptin, and adiponectin. The anthropometric model was the most cost-effective and easy to apply in different environments.

The association between body mass index groups and metabolic comorbidities with healthcare and medication costs: a nationwide biobank and registry study in Finland

BACKGROUND

The increasing prevalence of obesity imposes a significant cost burden on individuals and societies worldwide.

OBJECTIVE

In this nationally representative study, the association between body mass index (BMI) groups and the number of metabolic comorbidities (MetC) with total direct costs was investigated in the Finnish population.

STUDY DESIGN, SETTING, AND PARTICIPANTS

The study cohort included 5,587 adults with BMI ≥18.5 kg/m² who participated in the cross-sectional FinHealth 2017 health examination survey conducted by the Finnish Institute for Health and Welfare. Data on healthcare resource utilization (HCRU) and drug purchases were collected from national healthcare and drug registers.

MAIN OUTCOME MEASURE

The primary outcome was total direct costs (costs of primary and secondary HCRU and prescription medications).

RESULTS

Class I (BMI 30.0-34.9 kg/m²) and class II - III (BMI ≥35.0 kg/m²) obesity were associated with 43% and 40% higher age- and sex-adjusted direct costs, respectively, compared with normal weight, mainly driven by a steeply increased comorbidity in the higher BMI groups. In all BMI groups combined, individuals with ≥2 MetCs comprised 39% of the total study population and 60% of the total costs.

CONCLUSION

To manage the cost burden of obesity, treatment should be given equal consideration as other chronic diseases, and BMIs ≥30.0 kg/m² should be considered in treatment decisions.