Metabolic profiling of smoking, associations with type 2 diabetes and interaction with genetic susceptibility.

BACKGROUND: Smokers are at increased risk of type 2 diabetes (T2D), but the underlying mechanisms are unclear. We investigated if the smoking-T2D association is mediated by alterations in the metabolome and assessed potential interaction with genetic susceptibility to diabetes or insulin resistance. METHODS: In UK Biobank (n = 93,722), cross-sectional analyses identified 208 metabolites associated with smoking, of which 131 were confirmed in Mendelian Randomization analyses, including glycoprotein acetyls, fatty acids, and lipids. Elastic net regression was applied to create a smoking-related metabolic signature. We estimated hazard ratios (HR) of incident T2D in relation to baseline smoking/metabolic signature and calculated the proportion of the smoking-T2D association mediated by the signature. Additive interaction between the signature and genetic risk scores for T2D (GRS-T2D) and insulin resistance (GRS-IR) on incidence of T2D was assessed as relative excess risk due to interaction (RERI). FINDINGS: The HR of T2D was 1·73 (95% confidence interval (CI) 1·54 - 1·94) for current versus never smoking, and 38·3% of the excess risk was mediated by the metabolic signature. The metabolic signature and its mediation role were replicated in TwinGene. The metabolic signature was associated with T2D (HR: 1·61, CI 1·46 - 1·77 for values above vs. below median), with evidence of interaction with GRS-T2D (RERI: 0·81, CI: 0·23 - 1·38) and GRS-IR (RERI 0·47, CI: 0·02 - 0·92). INTERPRETATION: The increased risk of T2D in smokers may be mediated through effects on the metabolome, and the influence of such metabolic alterations on diabetes risk may be amplified in individuals with genetic susceptibility to T2D or insulin resistance.

Epigenome-wide analysis of frailty: Results from two European twin cohorts.

Epigenetics plays an important role in the aging process, but it is unclear whether epigenetic factors also influence frailty, an age-related state of physiological decline. In this study, we performed a meta-analysis of epigenome-wide association studies in four samples drawn from the Swedish Adoption/Twin Study of Aging (SATSA) and the Longitudinal Study of Aging Danish Twins (LSADT) to explore the association between DNA methylation and frailty. Frailty was defined using the frailty index (FI), and DNA methylation levels were measured in whole blood using Illumina's Infinium HumanMethylation450K and MethylationEPIC arrays. In the meta-analysis consisting of a total of 829 participants, we identified 589 CpG sites that were statistically significantly associated with either the continuous or categorical FI (false discovery rate <0.05). Many of these CpGs have previously been associated with age and age-related diseases. The identified sites were also largely directionally consistent in a longitudinal analysis using mixed-effects models in SATSA, where the participants were followed up to a maximum of 20 years. Moreover, we identified three differentially methylated regions within the MGRN1, MIR596, and TAPBP genes that have been linked to neuronal aging, tumor growth, and immune functions. Furthermore, our meta-analysis results replicated 34 of the 77 previously reported frailty-associated CpGs at p < 0.05. In conclusion, our findings demonstrate robust associations between frailty and DNA methylation levels in 589 novel CpGs, previously unidentified for frailty, and strengthen the role of neuronal/brain pathways in frailty.

Clinical biomarker-based biological ageing and future risk of neurological disorders in the UK Biobank.

BACKGROUND: Many common neurological disorders are associated with advancing chronological age, but their association with biological age (BA) remains poorly understood. METHODS: We studied 325 870 participants in the UK Biobank without a diagnosed neurological condition at baseline and generated three previously-described measures of BA based on 18 routinely measured clinical biomarkers (PhenoAge, Klemera-Doubal method age (KDMAge), homeostatic dysregulation age). Using survival models, we assessed the effect of advanced BA on incident neurological diagnoses, including all-cause and cause-specific dementia, ischaemic stroke, Parkinson's disease and motor neuron disease. RESULTS: During a mean follow-up of 9.0 years, there were 1397 incident cases of dementia and 2515 of ischaemic stroke, with smaller case numbers of other diagnoses. The strongest associations with a 1 SD in BA residual were seen for all-cause dementia (KDMAge HR=1.19, 95% CI=1.11 to 1.26), vascular dementia (1.41, 1.25 to 1.60) and ischaemic stroke (1.39, 1.34 to 1.46). Weaker associations were seen for Alzheimer's disease and motor neuron disease, while, in contrast, HRs for Parkinson's disease tended to be <1. Results were largely consistent after adjustment for disease-specific covariates including common cardiometabolic risk factors. CONCLUSIONS: Advanced BA calculated from routine clinical biomarker results increases the risk of subsequent neurological diagnoses including all-cause dementia and ischaemic stroke.

Use of Electronic Medical Records (EMR) in Gerontology: Benefits, Considerations and a Promising Future.

Electronic medical records (EMRs) have many benefits in clinical research in gerontology, enabling data analysis, development of prognostic tools and disease risk prediction. EMRs also offer a range of advantages in clinical practice, such as comprehensive medical records, streamlined communication with healthcare providers, remote data access, and rapid retrieval of test results, ultimately leading to increased efficiency, enhanced patient safety, and improved quality of care in gerontology, which includes benefits like reduced medication use and better patient history taking and physical examination assessments. The use of artificial intelligence (AI) and machine learning (ML) approaches on EMRs can further improve disease diagnosis, symptom classification, and support clinical decision-making. However, there are also challenges related to data quality, data entry errors, as well as the ethics and safety of using AI in healthcare. This article discusses the future of EMRs in gerontology and the application of AI and ML in clinical research. Ethical and legal issues surrounding data sharing and the need for healthcare professionals to critically evaluate and integrate these technologies are also emphasized. The article concludes by discussing the challenges related to the use of EMRs in research as well as in their primary intended use, the daily clinical practice.

Temporal dynamics of epigenetic aging and frailty from midlife to old age.

BACKGROUND: DNA methylation-derived epigenetic clocks and frailty are well-established biological age measures capturing different aging processes. However, whether they are dynamically linked to each other across chronological age remains poorly understood. METHODS: This analysis included 1,309 repeated measurements in 524 individuals aged 50 to 90 years from the Swedish Adoption/Twin Study of Aging. Frailty was measured using a validated 42-item frailty index (FI). Five epigenetic clocks were calculated, including four principal component (PC)-based clocks trained on chronological age (PCHorvathAge, PCHannumAge) and aging-related physiological conditions (PCPhenoAge, PCGrimAge), and a pace of aging clock (DunedinPACE). Using dual change score models, we examined the dynamic, bidirectional associations between each of the epigenetic clocks and the FI over age to test for potential causal associations. RESULTS: The FI exhibited a nonlinear, accelerated increase across the older adulthood, whereas the epigenetic clocks mostly increased linearly with age. For PCHorvathAge, PCHannumAge, PCPhenoAge, and PCGrimAge, their associations with the FI were primarily due to correlated levels at age 50 but with no evidence of a dynamic longitudinal association. In contrast, we observed a unidirectional association between DunedinPACE and the FI, where a higher DunedinPACE predicted a subsequent increase in the FI, but not vice versa. CONCLUSION: Our results highlight a temporal order between epigenetic aging and frailty such that changes in DunedinPACE precede changes in the FI. This potentially suggests that the pace of aging clock can be used as an early marker of the overall physiological decline at system level.

Is Frailty Different in Younger Adults Compared to Old? Prevalence, Characteristics, and Risk Factors of Early-Life and Late-Life Frailty in Samples from Sweden and UK.

INTRODUCTION: Although frailty is commonly considered as a syndrome of old individuals, recent studies show that it can affect younger adults, too. Whether and how frailty differs in younger adults compared to old is however unknown. To this end, we analyzed the prevalence, characteristics, and risk factors of early-life (aged <65) and late-life (aged ≥65) frailty. METHODS: We analyzed individuals in the UK Biobank (N = 405,123) and Swedish Screening Across the Lifespan Twin (SALT; N = 43,641) study. Frailty index (FI) scores ≥0.21 were used to demarcate frailty. Characteristics of early-life versus late-life frailty were analyzed by collating the FI items (deficits) into domains and comparing the domain scores between younger and older frail individuals. Logistic regression was used to assess the risk factors of frailty. RESULTS: The pooled prevalence rates of frailty were 10.3% (95% confidence interval [CI]: 2.7-32.7), 14.4% (95% CI: 4.5-37.2), 19.2% (95% CI: 2.5-68.5) in individuals aged ≤55, 55-64, 65-74, respectively. Younger frail adults (aged <65) had higher scores in immunological, mental wellbeing, and pain-related domains, whereas older frail adults (aged ≥65) had higher scores in cardiometabolic, cancer, musculoskeletal, and sensory-related domains. Higher age, female sex, smoking, lower alcohol consumption, lower education, obesity, overweight, low income, and maternal smoking were similarly associated with the risk of early-life and late-life frailty. CONCLUSION: Frailty is prevalent also in younger age groups (aged <65) but differs in some of its characteristics from the old. The risk factors of frailty are nevertheless largely similar for early-life and late-life frailty.

Advanced biological ageing predicts future risk for neurological diagnoses and clinical examination findings.

Age is a dominant risk factor for some of the most common neurological diseases. Biological ageing encompasses interindividual variation in the rate of ageing and can be calculated from clinical biomarkers or DNA methylation data amongst other approaches. Here, we tested the hypothesis that a biological age greater than one's chronological age affects the risk of future neurological diagnosis and the development of abnormal signs on clinical examination. We analysed data from the Swedish Adoption/Twin Study of Aging (SATSA): a cohort with 3175 assessments of 802 individuals followed-up over several decades. Six measures of biological ageing were generated: two physiological ages (created from bedside clinical measurements and standard blood tests) and four blood methylation age measures. Their effects on future stroke, dementia or Parkinson's disease diagnosis, or development of abnormal clinical signs, were determined using survival analysis, with and without stratification by twin pairs. Older physiological ages were associated with ischaemic stroke risk; for example one standard deviation advancement in baseline PhenoAgePhys or KDMAgePhys residual increased future ischaemic stroke risk by 29.2% [hazard ratio (HR): 1.29, 95% confidence interval (CI) 1.06-1.58, P = 0.012] and 42.9% (HR 1.43, CI 1.18-1.73, P = 3.1 × 10-4), respectively. In contrast, older methylation ages were more predictive of future dementia risk, which was increased by 29.7% (HR 1.30, CI 1.07-1.57, P = 0.007) per standard deviation advancement in HorvathAgeMeth. Older physiological ages were also positively associated with future development of abnormal patellar or pupillary reflexes, and the loss of normal gait. Measures of biological ageing can predict clinically relevant pathology of the nervous system independent of chronological age. This may help to explain variability in disease risk between individuals of the same age and strengthens the case for trials of geroprotective interventions for people with neurological disorders.

Rare functional variants in the CRP and G6PC genes modify the relationship between obesity and serum C-reactive protein in white British population.

BACKGROUND: C-reactive protein (CRP) is a sensitive biomarker of inflammation with moderate heritability. The role of rare functional genetic variants in relation to serum CRP is understudied. We aimed to examine gene mutation burden of protein-altering (PA) and loss-of-function (LOF) variants in association with serum CRP, and to further explore the clinical relevance. METHODS: We included 161,430 unrelated participants of European ancestry from the UK Biobank. Of the rare (minor allele frequency <0.1%) and functional variants, 1,776,249 PA and 266,226 LOF variants were identified. Gene-based burden tests, linear regressions, and logistic regressions were performed to identify the candidate mutations at the gene and variant levels, to estimate the potential interaction effect between the identified PA mutation and obesity, and to evaluate the relative risk of 16 CRP-associated diseases. RESULTS: At the gene level, PA mutation burdens of the CRP (ß = -0.685, p = 2.87e-28) and G6PC genes (ß = 0.203, p = 1.50e-06) were associated with reduced and increased serum CRP concentration, respectively. At the variant level, seven PA alleles in the CRP gene decreased serum CRP, of which the per-allele effects were approximately three to seven times greater than that of a common variant in the same locus. The effects of obesity and central obesity on serum CRP concentration were smaller among the PA mutation carriers in the CRP (pinteraction = 0.008) and G6PC gene (pinteraction = 0.034) compared to the corresponding non-carriers. CONCLUSION: PA mutation burdens in the CRP and G6PC genes are strongly associated with decreased serum CRP concentrations. As serum CRP and obesity are important predictors of cardiovascular risks in clinics, our observations suggest taking rare genetic factors into consideration might improve the delivery of precision medicine.

Unraveling the metabolic underpinnings of frailty using multicohort observational and Mendelian randomization analyses.

Identifying metabolic biomarkers of frailty, an age-related state of physiological decline, is important for understanding its metabolic underpinnings and developing preventive strategies. Here, we systematically examined 168 nuclear magnetic resonance-based metabolomic biomarkers and 32 clinical biomarkers for their associations with frailty. In up to 90,573 UK Biobank participants, we identified 59 biomarkers robustly and independently associated with the frailty index (FI). Of these, 34 associations were replicated in the Swedish TwinGene study (n = 11,025) and the Finnish Health 2000 Survey (n = 6073). Using two-sample Mendelian randomization, we showed that the genetically predicted level of glycoprotein acetyls, an inflammatory marker, was statistically significantly associated with an increased FI (ß per SD increase = 0.37%, 95% confidence interval: 0.12-0.61). Creatinine and several lipoprotein lipids were also associated with increased FI, yet their effects were mostly driven by kidney and cardiometabolic diseases, respectively. Our findings provide new insights into the causal effects of metabolites on frailty and highlight the role of chronic inflammation underlying frailty development.

Clinical biomarker-based biological aging and risk of cancer in the UK Biobank.

BACKGROUND: Despite a clear link between aging and cancer, there has been inconclusive evidence on how biological age (BA) may be associated with cancer incidence. METHODS: We studied 308,156 UK Biobank participants with no history of cancer at enrolment. Using 18 age-associated clinical biomarkers, we computed three BA measures (Klemera-Doubal method [KDM], PhenoAge, homeostatic dysregulation [HD]) and assessed their associations with incidence of any cancer and five common cancers (breast, prostate, lung, colorectal, and melanoma) using Cox proportional-hazards models. RESULTS: A total of 35,426 incident cancers were documented during a median follow-up of 10.9 years. Adjusting for common cancer risk factors, 1-standard deviation (SD) increment in the age-adjusted KDM (hazard ratio = 1.04, 95% confidence interval = 1.03-1.05), age-adjusted PhenoAge (1.09, 1.07-1.10), and HD (1.02, 1.01-1.03) was significantly associated with a higher risk of any cancer. All BA measures were also associated with increased risks of lung and colorectal cancers, but only PhenoAge was associated with breast cancer risk. Furthermore, we observed an inverse association between BA measures and prostate cancer, although it was attenuated after removing glycated hemoglobin and serum glucose from the BA algorithms. CONCLUSIONS: Advanced BA quantified by clinical biomarkers is associated with increased risks of any cancer, lung cancer, and colorectal cancer.

Can frailty scores predict the incidence of cancer? Results from two large population-based studies.

While chronological age is the single biggest risk factor for cancer, it is less clear whether frailty, an age-related state of physiological decline, may also predict cancer incidence. We assessed the associations of frailty index (FI) and frailty phenotype (FP) scores with the incidence of any cancer and five common cancers (breast, prostate, lung, colorectal, melanoma) in 453,144 UK Biobank (UKB) and 36,888 Screening Across the Lifespan Twin study (SALT) participants, who aged 38-73 years and had no cancer diagnosis at baseline. During a median follow-up of 10.9 and 10.7 years, 53,049 (11.7%) and 4,362 (11.8%) incident cancers were documented in UKB and SALT, respectively. Using multivariable-adjusted Cox models, we found a higher risk of any cancer in frail vs. non-frail UKB participants, when defined by both FI (hazard ratio [HR] = 1.22; 95% confidence interval [CI] = 1.17-1.28) and FP (HR = 1.16; 95% CI = 1.11-1.21). The FI in SALT similarly predicted risk of any cancer (HR = 1.31; 95% CI = 1.15-1.49). Moreover, frailty was predictive of lung cancer in UKB, although this association was not observed in SALT. Adding frailty scores to models including age, sex, and traditional cancer risk factors resulted in little improvement in C-statistics for most cancers. In a within-twin-pair analysis in SALT, the association between FI and any cancer was attenuated within monozygotic but not dizygotic twins, indicating that it may partly be explained by genetic factors. Our findings suggest that frailty scores are associated with the incidence of any cancer and lung cancer, although their clinical utility for predicting cancers may be limited.

Health progression for Covid-19 survivors hospitalized in geriatric clinics in Sweden.

OBJECTIVE: To analyse if the health progression of geriatric Covid-19 survivors three months after an acute Covid-19 infection was worse than in other geriatric patients. Specifically, we wanted to see if we could see distinct health profiles in the flow of re-admitted Covid-19 patients compared to re-admitted non-Covid-19 controls. DESIGN: Matched cohort study. SETTING AND PARTICIPANTS: Electronic medical records of geriatric patients hospitalised in geriatric clinics in Stockholm, Sweden, between March 2020 and January 2022. Patients readmitted three months after initial admission were selected for the analysis and Covid-19 survivors (n = 895) were compared to age-sex-Charlson comorbidity index (CCI)-matched non-Covid-19 controls (n = 2685). METHODS: We assessed using binary logistic and Cox regression if a previous Covid-19 infection could be a risk factor for worse health progression indicated by the CCI, hospital frailty risk score (HFRS), mortality and specific comorbidities. RESULTS: The patients were mostly older than 75 years and, already at baseline, had typically multiple comorbidities. The Covid-19 patients with readmission had mostly had their acute-phase infection in the 1st or 2nd pandemic waves before the vaccinations. The Covid-19 patients did not have worse health after three months compared to the matched controls according to the CCI (odds ratio, OR[95% confidence interval, CI] = 1.12[0.94-1.34]), HFRS (OR[95%CI] = 1.05[0.87-1.26]), 6-months (hazard ratio, HR[95%CI] = 1.04[0.70-1.52]) and 1-year-mortality risk (HR[95%CI] = 0.89[0.71-1.10]), adjusted for age, sex and health at baseline (the CCI and HFRS). CONCLUSIONS AND IMPLICATIONS: The overall health progression of re-hospitalized geriatric Covid-19 survivors did not differ dramatically from other re-hospitalized geriatric patients with similar age, sex and health at baseline. Our results emphasize that Covid-19 was especially detrimental for geriatric patients in the acute-phase, but not in the later phase. Further studies including post-vaccination samples are needed.

Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study.

INTRODUCTION: Frailty, a measure of biological aging, has been linked to worse COVID-19 outcomes. However, as the mortality differs across the COVID-19 waves, it is less clear whether a medical record-based electronic frailty index (eFI) that we have previously developed for older adults could be used for risk stratification in hospitalized COVID-19 patients. OBJECTIVES: The aim of the study was to examine the association of frailty with mortality, readmission, and length of stay in older COVID-19 patients and to compare the predictive accuracy of the eFI to other frailty and comorbidity measures. METHODS: This was a retrospective cohort study using electronic health records (EHRs) from nine geriatric clinics in Stockholm, Sweden, comprising 3,980 COVID-19 patients (mean age 81.6 years) admitted between March 2020 and March 2022. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the Clinical Frailty Scale, and the Hospital Frailty Risk Score. Comorbidity was measured using the Charlson Comorbidity Index. We analyzed in-hospital mortality and 30-day readmission using logistic regression, 30-day and 6-month mortality using Cox regression, and the length of stay using linear regression. Predictive accuracy of the logistic regression and Cox models was evaluated by area under the receiver operating characteristic curve (AUC) and Harrell's C-statistic, respectively. RESULTS: Across the study period, the in-hospital mortality rate decreased from 13.9% in the first wave to 3.6% in the latest (Omicron) wave. Controlling for age and sex, a 10% increment in the eFI was significantly associated with higher risks of in-hospital mortality (odds ratio = 2.95; 95% confidence interval = 2.42-3.62), 30-day mortality (hazard ratio [HR] = 2.39; 2.08-2.74), 6-month mortality (HR = 2.29; 2.04-2.56), and a longer length of stay (ß-coefficient = 2.00; 1.65-2.34) but not with 30-day readmission. The association between the eFI and in-hospital mortality remained robust across the waves, even after the vaccination rollout. Among all measures, the eFI had the best discrimination for in-hospital (AUC = 0.780), 30-day (Harrell's C = 0.733), and 6-month mortality (Harrell's C = 0.719). CONCLUSION: An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic.

Genetic and Environmental Influences on Longitudinal Frailty Trajectories From Adulthood into Old Age.

BACKGROUND: Frailty is a complex, dynamic geriatric condition, but limited evidence has shown how genes and environment may contribute to its longitudinal changes. We sought to investigate sources of individual differences in the longitudinal trajectories of frailty, considering potential selection bias when including a sample of oldest-old twins. METHODS: Data were from 2 Swedish twin cohort studies: a younger cohort comprising 1 842 adults aged 29-96 years followed up to 15 waves, and an older cohort comprising 654 adults aged ≥79 years followed up to 5 waves. Frailty was measured using the frailty index (FI). Age-based latent growth curve models were used to examine longitudinal trajectories, and extended to a biometric analysis to decompose variability into genetic and environmental etiologies. RESULTS: A bilinear model with an inflection point at age 75 best described the data, indicating a fourfold to fivefold faster FI increase after 75 years. Twins from the older cohort had significantly higher mean FI at baseline but slower rate of increase afterward. FI level at age 75 was moderately heritable in both men (42%) and women (55%). Genetic influences were relatively stable across age for men and increasing for women, although the most salient amplification in FI variability after age 75 was due to individual-specific environmental influences for both men and women; conclusions were largely consistent when excluding the older cohort. CONCLUSION: Increased heterogeneity of frailty in late life is mainly attributable to environmental influences, highlighting the importance of targeting environmental risk factors to mitigate frailty in older adults.

Development of an Electronic Frailty Index for Hospitalized Older Adults in Sweden.

BACKGROUND: Frailty assessment in the Swedish health system relies on the Clinical Frailty Scale (CFS), but it requires training, in-person evaluation, and is often missing in medical records. We aimed to develop an electronic frailty index (eFI) from routinely collected electronic health records (EHRs) and assess its association with adverse outcomes in hospitalized older adults. METHODS: EHRs were extracted for 18 225 patients with unplanned admissions between 1 March 2020 and 17 June 2021 from 9 geriatric clinics in Stockholm, Sweden. A 48-item eFI was constructed using diagnostic codes, functioning and other health indicators, and laboratory data. The CFS, Hospital Frailty Risk Score, and Charlson Comorbidity Index were used for comparative assessment of the eFI. We modeled in-hospital mortality and 30-day readmission using logistic regression; 30-day and 6-month mortality using Cox regression; and length of stay using linear regression. RESULTS: Thirteen thousand one hundred and eighty-eight patients were included in analyses (mean age 83.1 years). A 0.03 increment in the eFI was associated with higher risks of in-hospital (odds ratio: 1.65; 95% confidence interval: 1.54-1.78), 30-day (hazard ratio [HR]: 1.43; 1.38-1.48), and 6-month mortality (HR: 1.34; 1.31-1.37) adjusted for age and sex. Of the frailty and comorbidity measures, the eFI had the highest area under receiver operating characteristic curve for in-hospital mortality of 0.813. Higher eFI was associated with longer length of stay, but had a rather poor discrimination for 30-day readmission. CONCLUSIONS: An EHR-based eFI has robust associations with adverse outcomes, suggesting that it can be used in risk stratification in hospitalized older adults.

Sex differences in genetic and environmental influences on frailty and its relation to body mass index and education.

Frailty is influenced by numerous genetic and environmental factors. However, sex differences in how these factors affect frailty, and the gene-environment interplay among frailty and two of its well-established risk factors, unhealthy body mass index (BMI) and low education, are less clear. In a large sample of 42,994 Swedish twins, we used structural equation models to estimate the genetic (heritability) and environmental sources of variance in frailty, defined as the frailty index (FI), separately in men and women. Genetic and individual-specific environmental factors contributed approximately equally to the FI variance. The heritability of FI was slightly, but significantly, higher in women (52%) than in men (45%), yet we found only weak-to-no indication of different sources of genetic variance influencing frailty across sexes. We observed a small-to-moderate genetic overlap between FI and BMI, and that the correlation between FI and education was largely explained by environmental factors common to twins in a pair. Additionally, genetic factors accounted for more of FI variation at both low and high BMI levels, with similar patterns in both sexes. In conclusion, the twin-based heritability of frailty is higher in women than in men, and different mechanisms may underlie the associations of frailty with BMI and education.

Frailty and comorbidity in predicting community COVID-19 mortality in the U.K. Biobank: The effect of sampling.

BACKGROUND/OBJECTIVES: Frailty has been linked to increased risk of COVID-19 mortality, but evidence is mainly limited to hospitalized older individuals. This study aimed to assess and compare predictive abilities of different frailty and comorbidity measures for COVID-19 mortality in a community sample and COVID-19 inpatients. DESIGN: Population-based cohort study. SETTING: Community. PARTICIPANTS: We analyzed (i) the full sample of 410,199 U.K. Biobank participants in England, aged 49-86 years, and (ii) a subsample of 2812 COVID-19 inpatients with COVID-19 data from March 1 to November 30, 2020. MEASUREMENTS: Frailty was defined using the physical frailty phenotype (PFP), frailty index (FI), and Hospital Frailty Risk Score (HFRS), and comorbidity using the Charlson Comorbidity Index (CCI). PFP and FI were available at baseline, whereas HFRS and CCI were assessed both at baseline and concurrently with the start of the pandemic. Inpatient COVID-19 cases were confirmed by PCR and/or hospital records. COVID-19 mortality was ascertained from death registers. RESULTS: Overall, 514 individuals died of COVID-19. In the full sample, all frailty and comorbidity measures were associated with higher COVID-19 mortality risk after adjusting for age and sex. However, the associations were stronger for the concurrent versus baseline HFRS and CCI, with odds ratios of 20.40 (95% confidence interval = 16.24-25.63) comparing high (>15) to low (<5) concurrent HFRS risk category and 1.53 (1.48-1.59) per point increase in concurrent CCI. Moreover, only the concurrent HFRS or CCI significantly improved predictive ability of a model including age and sex, yielding areas under the receiver operating characteristic curve (AUC) >0.8. When restricting analyses to COVID-19 inpatients, similar improvement in AUC was not observed. CONCLUSION: HFRS and CCI constructed from medical records concurrent with the start of the pandemic can be used in COVID-19 mortality risk stratification at the population level, but they show limited added value in COVID-19 inpatients.

Gestational diabetes and postnatal depressive symptoms: A prospective cohort study in Western China.

BACKGROUND: Gestational diabetes mellitus (GDM) can lead to adverse birth outcomes, but its effect on postnatal depression has not been thoroughly investigated, especially in Asian populations. AIM: To determine the prospective association between GDM and postnatal depressive symptoms in Western China. METHODS: A prospective cohort study of 1449 mothers was conducted in Chengdu, capital city of Sichuan Province. GDM was diagnosed during pregnancy using oral glucose tolerance tests. Maternal depressive symptoms were measured at 32-37weeks of gestation, then at one and three months after giving birth using a validated Chinese version of the Edinburgh Postnatal Depression Scale (EPDS). Associations between the postnatal EPDS scores, GDM status and blood glucose levels were assessed by multivariable mixed-effects regression models, accounting for baseline EPDS scores of the cohort and other confounding factors. FINDINGS: Compared to the non-GDM group (n=1220), women with GDM (n=229, 15.8%) reported significantly higher mean EPDS scores at both 1-month (p=0.02) and 3-month (p<0.01) postpartum. Similarly, high levels of fasting, 1-h and 2-h blood glucose levels during pregnancy were associated with increased EPDS scores. Mixed-effects models further confirmed the positive association between GDM status and postnatal depressive symptoms, even though the mean EPDS scores decreased substantially over the three time points. CONCLUSION: Chinese women with GDM were more susceptible to postnatal depression than others without the condition, despite their depressive symptoms reducing over time after childbirth. It is thus important to raise awareness of postnatal depression amongst healthcare professionals who care for women with GDM.

Gestational diabetes incidence and delivery outcomes in Western China: A prospective cohort study.

BACKGROUND: Few studies have examined the age-standardized incidence of gestational diabetes mellitus (GDM) for comparison between populations. Information on delivery outcomes is also lacking for Chinese women with GDM. Therefore, the present study aimed to determine age-standardized GDM incidence and assess its association with maternal and neonatal outcomes. METHODS: A total of 1901 pregnant women were recruited in Chengdu, Sichuan Province. GDM was diagnosed between 24 and 28 weeks' gestation using oral glucose tolerance tests. Age-standardized incidence rates of GDM were calculated using the direct method. Delivery outcomes were extracted from medical records and compared between the GDM and non-GDM groups. RESULTS: The age-standardized GDM incidence was 18.3% (95% CI 15.6-21.1) and increased with maternal age and prepregnancy body mass index (BMI). Women with GDM experienced longer length of stay in hospital, shorter gestation at delivery, and a higher risk of cesarean delivery. Their newborns were more likely to be macrosomic or small for gestational age, and to require neonatal intensive care. CONCLUSIONS: The incidence of GDM was high in Western China, especially among older and overweight women. Moreover, women with GDM had higher rates of adverse delivery outcomes. The findings lend further support for the screening, prevention, and management of GDM in Chinese women.