Tag: nevin manimala

J Clin Psychol. 2024 Apr 17. doi: 10.1002/jclp.23695. Online ahead of print.

ABSTRACT

The Difficulties in Emotion Regulation Scale (DERS) is the most used self-report questionnaire to assess deficits in emotion regulation (ER), composed of 6 dimensions and 36 items. Many studies have evaluated its factor structure, not always confirming the original results, and proposed different factor models. A possible way to try to identify the dimensionality of the DERS could be through a meta-analysis with structural equation models (MASEM) of its factor structure. The MASEM indicated that a six-factor model with 32 items (DERS-32) was the most suitable to represent the dimensionality of the DERS (χ² = 2095.96, df = 449, p < .001; root mean square error of approximation [RMSEA] = 0.024, 95% confidence interval [CI]: 0.023-0.025; comparative fit index [CFI] = 0.97; Tucker Lewis index [TLI] = 0.96; standardized root mean squared residual [SRMR] = 0.04). This result was also confirmed by a confirmatory factor analysis (χ² = 3229.67, df = 449, p < 0.001; RMSEA = 0.075, 95% CI: 0.073-0.078; CFI = 0.94; TLI = 0.93; SRMR = 0.05) on a new sample (1092 participants; mean age: 28.28, SD = 5.82 years) recruited from the Italian population. Analyses and results from this sample are reported in the second study of this work. The DERS-32 showed satisfactory internal consistency (i.e., ordinal α, Molenaar Sijtsma statistic, and latent class reliability coefficient) for all its dimensions and correctly categorized individuals with probable borderline symptomatology. In conclusion, the DERS-32 has demonstrated to be the best model for the DERS among all the others considered in this work, as well as a reliable tool to assess deficits in ER.

PMID:38630901 | DOI:10.1002/jclp.23695

J Travel Med. 2024 Mar 17:taae062. doi: 10.1093/jtm/taae062. Online ahead of print.

ABSTRACT

BACKGROUND: The international flight network creates multiple routes by which pathogens can quickly spread across the globe. In the early stages of infectious disease outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common and can help inform disease control efforts. A challenge faced in this modelling is that the latest aviation statistics (referred to as contemporary data) are typically not immediately available. Therefore, flight patterns from a previous year are often used (referred to as historical data). We explored the suitability of historical data for predicting the spatial spread of emerging epidemics.

METHODS: We analysed monthly flight passenger data from the International Air Transport Association to assess how baseline air travel patterns were affected in outbreaks of MERS, Zika, and SARS-CoV-2 over the past decade. We then used a stochastic discrete time SEIR metapopulation model to simulate global spread of different pathogens, comparing how epidemic dynamics differed in simulations based on historical and contemporary data.

RESULTS: We observed local, short-term disruptions to air travel from South Korea and Brazil for the MERS and Zika outbreaks we studied, whereas global and longer-term flight disruption occurred during the SARS-CoV-2 pandemic.For outbreak events that were accompanied by local, small, and short-term changes in air travel, epidemic models using historical flight data gave similar projections of timing and locations of disease spread as when using contemporary flight data. However, historical data were less reliable to model the spread of an atypical outbreak such as SARS-CoV-2 in which there were durable and extensive levels of global travel disruption.

CONCLUSIONS: The use of historical flight data as a proxy in epidemic models is an acceptable practice except in rare, large epidemics that lead to substantial disruptions to international travel.

PMID:38630887 | DOI:10.1093/jtm/taae062

J Bone Miner Res. 2024 Mar 4;39(1):17-29. doi: 10.1093/jbmr/zjad015.

ABSTRACT

Older men with high bone turnover have faster bone loss. We assessed the link between the baseline levels of bone turnover markers (BTMs) and the prospectively assessed bone microarchitecture decline in men. In 825 men aged 60-87 yr, we measured the serum osteocalcin (OC), bone alkaline phosphatase (BAP), N-terminal propeptide of type I procollagen (PINP), and C-terminal telopeptide of type I collagen (CTX-I), and urinary total deoxypyridinoline (tDPD). Bone microarchitecture and strength (distal radius and distal tibia) were estimated by high-resolution pQCT (XtremeCT, Scanco Medical) at baseline and then after 4 and 8 yr. Thirty-seven men took medications affecting bone metabolism. Statistical models were adjusted for age and BMI. At the distal radius, the decrease in the total bone mineral density (Tt.BMD), cortical BMD (Ct.BMD), cortical thickness (Ct.Thd), and cortical area (Ct.Ar) and failure load was faster in the highest vs the lowest CTX-I quartile (failure load: -0.94 vs -0.31% yr-1, P < .001). Patterns were similar for distal tibia. At the distal tibia, bone decline (Tt.BMD, Ct.Thd, Ct.Ar, Ct.BMD, and failure load) was faster in the highest vs the lowest tDPD quartile. At each skeletal site, the rate of decrease in Tb.BMD differed between the extreme OC quartiles (P < .001). Men in the highest BAP quartile had a faster loss of Tt.BMD, Tb.BMD, reaction force, and failure load vs the lowest quartile. The link between PINP and bone decline was poor. The BTM score is the sum of the nos. of the quartiles for each BTM. Men in the highest quartile of the score had a faster loss of cortical bone and bone strength vs the lowest quartile. Thus, in the older men followed prospectively for 8 yr, the rate of decline in bone microarchitecture and estimated bone strength was 50%-215% greater in men with high bone turnover (highest quartile, CTX-I above the median) compared to the men with low bone turnover (lowest quartile, CTX-I below the median).

PMID:38630881 | DOI:10.1093/jbmr/zjad015

J Bone Miner Res. 2024 Mar 4;39(1):50-58. doi: 10.1093/jbmr/zjad005.

ABSTRACT

Overt and subclinical hyperthyroidism are associated with an increased fracture risk, but whether thyroid hormones are associated with fracture risk in individuals with normal thyroid-stimulating hormone (TSH) has mostly been investigated in women. Therefore, we investigated if serum levels of free thyroxine (FT4) or TSH are associated with fracture risk in Swedish men. We followed (median 12.2 yr) elderly men (n = 1825; mean age 75, range 69-81 yr) participating in the Gothenburg and Malmö subcohorts of the prospective, population-based MrOS-Sweden study. The statistical analyses included Cox proportional hazards regression. Men receiving levothyroxine treatment were excluded. In our total cohort, serum FT4 (per SD increase) was associated with increased risk of major osteoporotic fractures (MOFs; n = 479; fully adjusted hazard ratio [HR] 1.14, 95% CI, 1.05-1.24) and hip fractures (n = 207; HR 1.18, 95% CI, 1.04-1.33). Also, in men with normal TSH (n = 1658), FT4 (per SD increase) was significantly associated with increased risk of MOF and hip fractures. Furthermore, men in the highest FT4 quartile had a 1.5-fold increase in hip fracture risk compared with men in the three lower FT4 quartiles, both in the total population and in men with normal TSH (fully adjusted: HR 1.45, 95% CI, 1.04-2.02 and HR 1.51, 95% CI, 1.07-2.12, respectively). In contrast, the risk of MOF was not statistically different in the highest FT4 quartile compared with the three lower FT4 quartiles. Finally, serum TSH was not associated with fracture risk after full adjustment for covariates. In conclusion, serum FT4, but not serum TSH, is a predictor of hip fracture risk in elderly Swedish men. Additionally, there was an association between FT4 (per SD increase) and the risk of MOF.

PMID:38630877 | DOI:10.1093/jbmr/zjad005

Europace. 2024 Mar 30;26(4):euae065. doi: 10.1093/europace/euae065.

ABSTRACT

AIMS: Photoplethysmography- (PPG) based smartphone applications facilitate heart rate and rhythm monitoring in patients with paroxysmal and persistent atrial fibrillation (AF). Despite an endorsement from the European Heart Rhythm Association, validation studies in this setting are lacking. Therefore, we evaluated the accuracy of PPG-derived heart rate and rhythm classification in subjects with an established diagnosis of AF in unsupervised real-world conditions.

METHODS AND RESULTS: Fifty consecutive patients were enrolled, 4 weeks before undergoing AF ablation. Patients used a handheld single-lead electrocardiography (ECG) device and a fingertip PPG smartphone application to record 3907 heart rhythm measurements twice daily during 8 weeks. The ECG was performed immediately before and after each PPG recording and was given a diagnosis by the majority of three blinded cardiologists. A consistent ECG diagnosis was exhibited along with PPG data of sufficient quality in 3407 measurements. A single measurement exhibited good quality more often with ECG (93.2%) compared to PPG (89.5%; P < 0.001). However, PPG signal quality improved to 96.6% with repeated measurements. Photoplethysmography-based detection of AF demonstrated excellent sensitivity [98.3%; confidence interval (CI): 96.7-99.9%], specificity (99.9%; CI: 99.8-100.0%), positive predictive value (99.6%; CI: 99.1-100.0%), and negative predictive value (99.6%; CI: 99.0-100.0%). Photoplethysmography underestimated the heart rate in AF with 6.6 b.p.m. (95% CI: 5.8 b.p.m. to 7.4 b.p.m.). Bland-Altman analysis revealed increased underestimation in high heart rates. The root mean square error was 11.8 b.p.m.

CONCLUSION: Smartphone applications using PPG can be used to monitor patients with AF in unsupervised real-world conditions. The accuracy of AF detection algorithms in this setting is excellent, but PPG-derived heart rate may tend to underestimate higher heart rates.

PMID:38630867 | DOI:10.1093/europace/euae065

Vital Health Stat 1. 2024 Apr;(207):1-31.

ABSTRACT

The National Health Interview Survey (NHIS), conducted by the National Center for Health Statistics since 1957, is the principal source of information on the health of the U.S. civilian noninstitutionalized population. NHIS selects one adult (Sample Adult) and, when applicable, one child (Sample Child) randomly within a family (through 2018) or a household (2019 and forward). Sampling weights for the separate analysis of data from Sample Adults and Sample Children are provided annually by the National Center for Health Statistics. A growing interest in analysis of parent-child pair data using NHIS has been observed, which necessitated the development of appropriate analytic weights. Objective This report explains how dyad weights were created such that data users can analyze NHIS data from both Sample Children and their mothers or fathers, respectively. Methods Using data from the 2019 NHIS, adult-child pair-level sampling weights were developed by combining each pair’s conditional selection probability with their household-level sampling weight. The calculated pair weights were then adjusted for pair-level nonresponse, and large sampling weights were trimmed at the 99th percentile of the derived sampling weights. Examples of analyzing parent-child pair data by means of domain estimation methods (that is, statistical analysis for subpopulations or subgroups) are included in this report. Conclusions The National Center for Health Statistics has created dyad or pair weights that can be used for studies using parent-child pairs in NHIS. This method could potentially be adapted to other surveys with similar sampling design and statistical needs.

PMID:38630839

PLoS Comput Biol. 2024 Apr 17;20(4):e1011965. doi: 10.1371/journal.pcbi.1011965. Online ahead of print.

ABSTRACT

The efficient coding hypothesis posits that early sensory neurons transmit maximal information about sensory stimuli, given internal constraints. A central prediction of this theory is that neurons should preferentially encode stimuli that are most surprising. Previous studies suggest this may be the case in early visual areas, where many neurons respond strongly to rare or surprising stimuli. For example, previous research showed that when presented with a rhythmic sequence of full-field flashes, many retinal ganglion cells (RGCs) respond strongly at the instance the flash sequence stops, and when another flash would be expected. This phenomenon is called the ‘omitted stimulus response’. However, it is not known whether the responses of these cells varies in a graded way depending on the level of stimulus surprise. To investigate this, we presented retinal neurons with extended sequences of stochastic flashes. With this stimulus, the surprise associated with a particular flash/silence, could be quantified analytically, and varied in a graded manner depending on the previous sequences of flashes and silences. Interestingly, we found that RGC responses could be well explained by a simple normative model, which described how they optimally combined their prior expectations and recent stimulus history, so as to encode surprise. Further, much of the diversity in RGC responses could be explained by the model, due to the different prior expectations that different neurons had about the stimulus statistics. These results suggest that even as early as the retina many cells encode surprise, relative to their own, internally generated expectations.

PMID:38630835 | DOI:10.1371/journal.pcbi.1011965

Sci Adv. 2024 Apr 19;10(16):eadi8419. doi: 10.1126/sciadv.adi8419. Epub 2024 Apr 17.

ABSTRACT

We generated Japanese Encyclopedia of Whole-Genome/Exome Sequencing Library (JEWEL), a high-depth whole-genome sequencing dataset comprising 3256 individuals from across Japan. Analysis of JEWEL revealed genetic characteristics of the Japanese population that were not discernible using microarray data. First, rare variant-based analysis revealed an unprecedented fine-scale genetic structure. Together with population genetics analysis, the present-day Japanese can be decomposed into three ancestral components. Second, we identified unreported loss-of-function (LoF) variants and observed that for specific genes, LoF variants appeared to be restricted to a more limited set of transcripts than would be expected by chance, with PTPRD as a notable example. Third, we identified 44 archaic segments linked to complex traits, including a Denisovan-derived segment at NKX6-1 associated with type 2 diabetes. Most of these segments are specific to East Asians. Fourth, we identified candidate genetic loci under recent natural selection. Overall, our work provided insights into genetic characteristics of the Japanese population.

PMID:38630824 | DOI:10.1126/sciadv.adi8419

PLoS Comput Biol. 2024 Apr 17;20(4):e1012016. doi: 10.1371/journal.pcbi.1012016. Online ahead of print.

ABSTRACT

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.

PMID:38630807 | DOI:10.1371/journal.pcbi.1012016

PLoS Genet. 2024 Apr 17;20(4):e1011212. doi: 10.1371/journal.pgen.1011212. eCollection 2024 Apr.

ABSTRACT

Population differences in risk of disease are common, but the potential genetic basis for these differences is not well understood. A standard approach is to compare genetic risk across populations by testing for mean differences in polygenic scores, but existing studies that use this approach do not account for statistical noise in effect estimates (i.e., the GWAS betas) that arise due to the finite sample size of GWAS training data. Here, we show using Bayesian polygenic score methods that the level of uncertainty in estimates of genetic risk differences across populations is highly dependent on the GWAS training sample size, the polygenicity (number of causal variants), and genetic distance (FST) between the populations considered. We derive a Wald test for formally assessing the difference in genetic risk across populations, which we show to have calibrated type 1 error rates under a simplified assumption that all SNPs are independent, which we achieve in practise using linkage disequilibrium (LD) pruning. We further provide closed-form expressions for assessing the uncertainty in estimates of relative genetic risk across populations under the special case of an infinitesimal genetic architecture. We suggest that for many complex traits and diseases, particularly those with more polygenic architectures, current GWAS sample sizes are insufficient to detect moderate differences in genetic risk across populations, though more substantial differences in relative genetic risk (relative risk > 1.5) can be detected. We show that conventional approaches that do not account for sampling error from the training sample, such as using a simple t-test, have very high type 1 error rates. When applying our approach to prostate cancer, we demonstrate a higher genetic risk in African Ancestry men, with lower risk in men of European followed by East Asian ancestry.

PMID:38630784 | DOI:10.1371/journal.pgen.1011212