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Background: The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate if temporal patterns of healthcare expenditures, can improve the predictive performance for mortality, in spousal bereaved older adults, next to other widely used sociodemographic variables. Methods: This is a population-based cohort study of 48,944 Danish citizens 65 years of age and older suffering bereavement within 2013–2016. Individuals were followed from date of spousal loss until death from all causes or 31st of December 2016, whichever came first. Healthcare expenditures were available on weekly basis for each person during the follow-up and used as predictors for mortality risk in Extreme Gradient Boosting models. The extent to which medical spending trajectories improved mortality predictions compared to models with sociodemographics, was assessed with respect to discrimination (AUC), overall prediction error (Brier score), calibration, and clinical benefit (decision curve analysis). Results: The AUC of age and sex for mortality the year after spousal loss was 70.8% [95% CI 68.8, 72.8]. The addition of sociodemographic variables led to an increase of AUC ranging from 0.9% to 3.1% but did not significantly reduce the overall prediction error. The AUC of the model combining the variables above plus medical spending usage was 80.8% [79.3, 82.4] also exhibiting smaller Brier score and better calibration. Overall, patterns of healthcare expenditures improved mortality predictions the most, also exhibiting the highest clinical benefit among the rest of the models. Conclusion: Temporal patterns of medical spending have the potential to significantly improve our assessment on who is at high risk of dying after suffering spousal loss. The proposed methodology can assist in a more efficient risk profiling and prognosis of bereaved individuals. [ABSTRACT FROM AUTHOR]

Objective To develop a prognostic model of 1-year mortality for individuals aged 65+ presenting at the emergency department (ED) with a fall based on health care spending patterns to guide clinical decision-making. Design Population-based cohort study (n  = 35,997) included with a fall in 2013 and followed 1 year. Methods Health care spending indicators (dynamical indicators of resilience, DIORs) 2 years before admission were evaluated as potential predictors, along with age, sex and other clinical and sociodemographic covariates. Multivariable logistic regression models were developed and internally validated (10-fold cross-validation). Performance was assessed via discrimination (area under the receiver operating characteristic curve, AUC), Brier scores, calibration and decision curve analysis. Results The AUC of age and sex for mortality was 72.5% [95% confidence interval 71.8 to 73.2]. The best model included age, sex, number of medications and health care spending DIORs. It exhibited high discrimination (AUC: 81.1 [80.5 to 81.6]), good calibration and potential clinical benefit for various threshold probabilities. Overall, health care spending patterns improved predictive accuracy the most while also exhibiting superior performance and clinical benefit. Conclusions Patterns of health care spending have the potential to significantly improve assessments on who is at high risk of dying following admission to the ED with a fall. The proposed methodology can assist in predicting the prognosis of fallers, emphasising the added predictive value of longitudinal health-related information next to clinical and sociodemographic predictors. [ABSTRACT FROM AUTHOR]

Summary: The nested case‐control (NCC) design has been widely adopted as a cost‐effective sampling design for biomarker research. Under the NCC design, markers are only measured for the NCC subcohort consisting of all cases and a fraction of the controls selected randomly from the matched risk sets of the cases. Robust methods for evaluating prediction performance of risk models have been derived under the inverse probability weighting framework. The probabilities of samples being included in the NCC cohort can be calculated based on the study design ''a previous study'' or estimated non‐parametrically ''a previous study''. Neither strategy works well due to model mis‐specification and the curse of dimensionality in practical settings where the sampling does not entirely follow the study design or depends on many factors. In this paper, we propose an alternative strategy to estimate the sampling probabilities based on a varying coefficient model, which attains a balance between robustness and the curse of dimensionality. The complex correlation structure induced by repeated finite risk set sampling makes the standard resampling procedure for variance estimation fail. We propose a perturbation resampling procedure that provides valid interval estimation for the proposed estimators. Simulation studies show that the proposed method performs well in finite samples. We apply the proposed method to the Nurses' Health Study II to develop and evaluate prediction models using clinical biomarkers for cardiovascular risk. [ABSTRACT FROM AUTHOR]

The incidence of nonalcoholic fatty liver disease (NAFLD) is rapidly increasing. This study evaluates the referral pattern of patients with NAFLD. A cohort study evaluating all patients with NAFLD referred to a single Gastroenterology Department from January 2017 to June 2020. Electronic patient referral letters were reviewed, and patients with NAFLD were diagnosed using standardized tests as part of a prospective cohort study. Predictors of nonalcoholic steatohepatitis (NASH) with significant (≥F2) fibrosis were evaluated in logistic regression analyses. In total, 323 (18.6%) of 1735 patients referred to the Gastro Unit during the study period were diagnosed with NAFLD. Patients were referred from general practitioners (62.5%) or other hospital departments (37.5%). Most referral letters included information suggesting a possible diagnosis of NAFLD (patient history, blood tests, or diagnostic imaging) or used the nonspecific general diagnosis suspected disease (Z.038). Out of 110 patients referred for a liver biopsy, 71 (22%) had NASH with significant fibrosis (F2 n = 39, F3 n = 19, F4 n = 13). Thirty-nine of these patients were referred from the primary sector. A logistic regression analysis (adjusted for age and gender) including all 323 patients showed that type 2 diabetes was the only significant independent predictor of NASH with fibrosis. [ABSTRACT FROM AUTHOR]

Introduction: The NF-κB transcription factor family regulates several genes encoding pro-inflammatory and anti-inflammatory proteins in adipose tissues and in atherosclerotic plaques. The deletion variant allele of the NFKB1 - 94ins/delATTG promoter polymorphism leads to lower transcript levels of the p50 subunit, and the variant allele has been associated with the risk of several inflammatory diseases as well as coronary heart disease where inflammation is important in the pathogenesis. The objective of this study was to explore the potential interaction between the NFKB1-94ins/delATTG promoter polymorphism and general, abdominal, and gluteofemoral obesity in relation to the risk of incident acute coronary syndrome (ACS) in three large independent cohorts. Methods and Results: The analyses were conducted in the Danish prospective study Diet, Cancer and Health and the two US based cohorts; Nurses’ Health Study and Health Professionals Follow-up Study. We conducted sex stratified analyses that included 1202 male and 708 female cases of incident ACS. We observed a positive association for general and abdominal obesity with risk of incident ACS, independent of genotype in both genders. Gluteofemoral obesity was negatively associated with ACS risk in women independent of genotype, whereas there was no clear association for men. We calculated the relative excess risk due to interaction (RERI) and observed a statistically significant excess risk among men jointly exposed to general or abdominal obesity and the variant allele of the NFKB1-94ATTG polymorphism, whereas there was a tendency towards sub-additivity for gluteofemoral obesity. The excess risks in all analyses were, however, small and could not clearly be demonstrated in women. Conclusion: The variant allele of the NFKB1-94ins/delATTG promoter polymorphism did not substantially modify the association between obesity and incident ACS.

Genetic variation in fatty acid desaturases (FADS) has previously been linked to long-chain polyunsaturated fatty acids (PUFAs) in adipose tissue and cardiovascular risk. The goal of our study was to test associations between six common FADS polymorphisms (rs174556, rs3834458, rs174570, rs2524299, rs174589, rs174627), intermediate cardiovascular risk factors, and non-fatal myocardial infarction (MI) in a matched population based case–control study of Costa Rican adults (n = 1756). Generalized linear models and multiple conditional logistic regression models were used to assess the associations of interest. Analyses involving intermediate cardiovascular risk factors and MI were also conducted in two replication cohorts, The Nurses’ Health Study (n = 1200) and The Health Professionals Follow-Up Study (n = 1295). In the Costa Rica Study, genetic variation in the FADS cluster was associated with a robust linear decrease in adipose gamma-linolenic, arachidonic, and eicosapentaenoic fatty acids, and significant or borderline significant increases in the eicosadienoic, eicosatrienoic, and dihomo-gamma-linolenic fatty acids. However, the associations with adipose tissue fatty acids did not translate into changes in inflammatory biomarkers, blood lipids, or the risk of MI in the discovery or the replication cohorts. In conclusion, fatty acid desaturase polymorphisms impact long-chain PUFA biosynthesis, but their overall effect on cardiovascular health likely involves multiple pathways and merits further investigation.

Background: High-density lipoproteins (HDL) are structurally and metabolically heterogeneous and subclasses with differential effects on coronary heart disease (CHD) might exist. Apolipoprotein (apo) C-III, a small proinflammatory protein that resides on the surface of lipoproteins, enhances the atherogenicity of VLDL and LDL particles, but little is known about the role apoC-III on HDL. We investigated whether the presence or absence of apoC-III differentiates HDL into subtypes with nonprotective or protective associations with risk of future CHD. Methods and Results: High-density lipoprotein cholesterol (HDL-C) levels were measured in plasma separated according to apoC-III (by immunoaffinity chromatography) in two prospective case-control studies nested within the Nurses’ Health and the Health Professionals Follow-Up Studies. Baseline was in 1990 and 1994, and 634 incident CHD cases were documented through 10 to 14 years of follow-up. The relative risk of CHD per each standard deviation of total HDL-C was 0.78 (95% confidence intervals, 0.63–0.96). The HDL-C subtypes were differentially associated with risk of CHD, HDL-C without apoC-III inversely and HDL-C with apoC-III directly (P=0.02 for a difference between the HDL types). The relative risk per standard deviation of HDL-C without apoC-III was 0.66 (0.53 to 0.93) and 1.18 (1.03 to 1.34) for HDL-C with apoC-III. HDL-C with apoC-III comprised ∼13% of the total HDL-C. Adjustment for triglycerides and apoB attenuated the risks; however, the two HDL-C subgroups remained differentially associated with risk of CHD (P=0.05). Conclusion: Separating HDL-C according to apoC-III identified two types of HDL with opposing associations with risk of CHD. The proatherogenic effects of apoC-III, as a component of VLDL and LDL, may extend to HDL. (J Am Heart Assoc. 2012;1:jah3-e000232 doi: 10.1161/JAHA.111.000232.)

Recently, genetic association findings for nicotine dependence, smoking behavior, and smoking-related diseases converged to implicate the chromosome 15q25.1 region, which includes the CHRNA5-CHRNA3-CHRNB4 cholinergic nicotinic receptor subunit genes. In particular, association with the nonsynonymous CHRNA5 SNP rs16969968 and correlates has been replicated in several independent studies. Extensive genotyping of this region has suggested additional statistically distinct signals for nicotine dependence, tagged by rs578776 and rs588765. One goal of the Consortium for the Genetic Analysis of Smoking Phenotypes (CGASP) is to elucidate the associations among these markers and dichotomous smoking quantity (heavy versus light smoking), lung cancer, and chronic obstructive pulmonary disease (COPD). We performed a meta-analysis across 34 datasets of European-ancestry subjects, including 38,617 smokers who were assessed for cigarettes-per-day, 7,700 lung cancer cases and 5,914 lung-cancer-free controls (all smokers), and 2,614 COPD cases and 3,568 COPD-free controls (all smokers). We demonstrate statistically independent associations of rs16969968 and rs588765 with smoking (mutually adjusted p-values<10\(^{−35}\) and <10\(^{−8}\) respectively). Because the risk alleles at these loci are negatively correlated, their association with smoking is stronger in the joint model than when each SNP is analyzed alone. Rs578776 also demonstrates association with smoking after adjustment for rs16969968 (p<10\(^{−6}\)). In models adjusting for cigarettes-per-day, we confirm the association between rs16969968 and lung cancer (p<10\(^{−20}\)) and observe a nominally significant association with COPD (p = 0.01); the other loci are not significantly associated with either lung cancer or COPD after adjusting for rs16969968. This study provides strong evidence that multiple statistically distinct loci in this region affect smoking behavior. This study is also the first report of association between rs588765 (and correlates) and smoking that achieves genome-wide significance; these SNPs have previously been associated with mRNA levels of CHRNA5 in brain and lung tissue.

Purpose: The Danish Pathology Life Course (PATHOLIFE) cohort was established to facilitate epidemiological research relating histological and cytological features extracted from patient tissue specimens to the rich life course histories, including both prior and future register data, of the entire Danish population. Research results may increase quality of diagnosis, prognosis and stratification of patient subtypes, possibly identifying novel routes of treatment., Participants: All Danish residents from 1 January 1986 to 31 December 2019, totalling 8 593 421 individuals., Findings to Date: We provide an overview of the subpopulation of Danish residents who have had a tissue specimen investigated within the Danish healthcare system, including both the primary sector and hospitals. We demonstrate heterogeneity in sociodemographic and prognostic factors between the general Danish population and the above mentioned subpopulation, and also between the general Danish population and subpopulations of patients with tissue specimens from selected anatomical sites. Results demonstrate the potential of the PATHOLIFE cohort for integrating many different factors into identification and selection of the most valuable tissue blocks for studies of specific diseases and their progression. Broadly, we find that living with a partner, having higher education and income associates with having a biopsy overall. However, this association varies across different tissue and patient types, which also display differences in time-to-death and causes of death., Future Plans: The PATHOLIFE cohort may be used to study specified patient groups and link health related events from several national health registries, and to sample patient groups, for which stored tissue specimens are available for further research investigations. The PATHOLIFE cohort thereby provides a unique opportunity to prospectively follow people that were characterised and sampled in the past., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: The MUNICH Preterm and Term Clinical (MUNICH-PreTCl) birth cohort was established to uncover pathological processes contributing to infant/childhood morbidity and mortality. We collected comprehensive medical information of healthy and sick newborns and their families, together with infant blood samples for proteomic analysis. MUNICH-PreTCl aims to identify mechanism-based biomarkers in infant health and disease to deliver more precise diagnostic and predictive information for disease prevention. We particularly focused on risk factors for pregnancy complications, family history of genetically influenced health conditions such as diabetes and paediatric long-term health-all to be further monitored and correlated with proteomics data in the future., Participants: Newborns and their parents were recruited from the Perinatal Center at the LMU University Hospital, Munich, between February 2017 and June 2019. Infants without congenital anomalies, delivered at 23-41 weeks of gestation, were eligible., Findings: Findings to date concern the clinical data and extensive personal patient information. A total of 662 infants were recruited, 44% were female (36% in preterm, 46% in term). 90% of approached families agreed to participate. Neonates were grouped according to gestational age: extremely preterm (<28 weeks, N=28), very preterm (28 to <32 weeks, N=36), late preterm (32 to <37 weeks, N=97) and term infants (>37+0 weeks, N=501). We collected over 450 data points per child-parent set, (family history, demographics, pregnancy, birth and daily follow-ups throughout hospitalisation) and 841 blood samples longitudinally. The completion rates for medical examinations and blood samples were 100% and 95% for the questionnaire., Future Plans: The correlation of large clinical datasets with proteomic phenotypes, together with the use of medical registries, will enable future investigations aiming to decipher mechanisms of disorders in a systems biology perspective., Trial Registration Number: DRKS (00024189); Pre-results., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)