Attention-Deficit/Hyperactivity Disorder Diagnoses in Finland During the COVID-19 Pandemic.

Importance: Several reports suggest an increase in attention-deficit/hyperactivity disorder (ADHD) symptoms during the COVID-19 pandemic. This nationwide study assessed new ADHD diagnoses and ADHD prevalence before and during the pandemic. Objective: To investigate trends in new ADHD diagnoses, prevalence, and ADHD medication use from 2015 to 2022 in Finland. Design, Setting, and Participants: This longitudinal cohort study comprised the entire Finnish population. ADHD diagnoses and medication use were obtained from nationwide registers and assessed at 3 time points: in 2015 and before (2020) and after (2022) the pandemic. Data were analyzed from January 2015 to June 2022. Main outcomes and Measures: New ADHD diagnoses, ADHD lifetime prevalence, and ADHD medication use. Results: The cohort comprised 5 572 420 individuals (2 819 645 women [50.6%]). Lifetime prevalence of ADHD increased by 2.7-fold during 2015 to 2022; prevalence was 1.02% in 2015 (95% CI, 1.01%-1.03%), 1.80% in 2020 (95% CI, 1.79%-1.81%), and 2.76% in 2022 (95% CI, 2.75%-2.77%). Young men aged 13 to 20 years had the highest lifetime prevalence of 11.68% (95% CI, 11.56%-11.81%) in 2022. New ADHD diagnoses doubled during the pandemic, from 238 per 100 000 in 2020 to 477 per 100 000 in 2022. The pandemic-associated incremental increase in new diagnoses was 18.60% (95% CI, 16.47%-20.49%; 9482 per 50 897 cases). Young women aged 13 to 20 years had a 2.6-fold increase in new diagnoses during the pandemic, from 577 per 100 000 in 2020 to 1488 per 100 000 in 2022, and women aged 21 to 30 years had a 3.0-fold increase, from 361 per 100 000 to 1100 per 100 000. New diagnoses increased by 2.9-fold among those older than 55 years (from 5 per 100 000 to 13 per 100 000 in women and from 5 per 100 000 to 14 per 100 000 in men). Boys younger than 13 years had the highest absolute rate of new ADHD diagnoses in 2022 (1745 per 100 000), but boys and young men younger than 21 years did not show a significant incremental increase in new diagnoses. Lifetime prevalence of ADHD medication purchases was 0.57% (95% CI, 0.56%-0.58%) in 2015 (31 771 [55.62%] of those with ADHD diagnosis), 1.15% (95% CI, 1.14%-1.16%) in 2020 (64 034 [63.83%]), and 1.69% (95% CI 1.68%-1.70%) in 2022 (92 557 [61.43%]), respectively. Conclusions and Relevance: In this nationwide cohort study, new ADHD diagnoses and ADHD prevalence showed significant increase in Finland during the pandemic. ADHD medication use did not increase in relation to ADHD diagnoses. These results highlight potential adverse outcomes of pandemic-associated changes in living conditions.

A metabolic profile of xenon and metabolite associations with 6-month mortality after out-of-hospital cardiac arrest: A post-hoc study of the randomised Xe-Hypotheca trial.

PURPOSE: Out-of-hospital cardiac arrest (OHCA) carries a relatively poor prognosis and requires multimodal prognostication to guide clinical decisions. Identification of previously unrecognized metabolic routes associated with patient outcome may contribute to future biomarker discovery. In OHCA, inhaled xenon elicits neuro- and cardioprotection. However, the metabolic effects remain unknown. MATERIALS AND METHODS: In this post-hoc study of the randomised, 2-group, single-blind, phase 2 Xe-Hypotheca trial, 110 OHCA survivors were randomised 1:1 to receive targeted temperature management (TTM) at 33°C with or without inhaled xenon during 24 h. Blood samples for nuclear magnetic resonance spectroscopy metabolic profiling were drawn upon admission, at 24 and 72 h. RESULTS: At 24 h, increased lactate, adjusted hazard-ratio 2.25, 95% CI [1.53; 3.30], p<0.001, and decreased branched-chain amino acids (BCAA) leucine 0.64 [0.5; 0.82], p = 0.007, and valine 0.37 [0.22; 0.63], p = 0.003, associated with 6-month mortality. At 72 h, increased lactate 2.77 [1.76; 4.36], p<0.001, and alanine 2.43 [1.56; 3.78], p = 0.001, and decreased small HDL cholesterol ester content (S-HDL-CE) 0.36 [0.19; 0.68], p = 0.021, associated with mortality. No difference was observed between xenon and control groups. CONCLUSIONS: In OHCA patients receiving TTM with or without xenon, high lactate and alanine and decreased BCAAs and S-HDL-CE associated with increased mortality. It remains to be established whether current observations on BCAAs, and possibly alanine and lactate, could reflect neural damage via their roles in the metabolism of the neurotransmitter glutamate. Xenon did not significantly alter the measured metabolic profile, a potentially beneficial attribute in the context of compromised ICU patients. TRIAL REGISTRATION: Trial Registry number: ClinicalTrials.gov Identifier: NCT00879892.

Deep learning-based prediction of one-year mortality in Finland is an accurate but unfair aging marker.

Short-term mortality risk, which is indicative of individual frailty, serves as a marker for aging. Previous age clocks focused on predicting either chronological age or longer-term mortality. Aging clocks predicting short-term mortality are lacking and their algorithmic fairness remains unexamined. We developed a deep learning model to predict 1-year mortality using nationwide longitudinal data from the Finnish population (FinRegistry; n = 5.4 million), incorporating more than 8,000 features spanning up to 50 years. We achieved an area under the curve (AUC) of 0.944, outperforming a baseline model that included only age and sex (AUC = 0.897). The model generalized well to different causes of death (AUC > 0.800 for 45 of 50 causes), including coronavirus disease 2019, which was absent in the training data. Performance varied among demographics, with young females exhibiting the best and older males the worst results. Extensive prediction fairness analyses highlighted disparities among disadvantaged groups, posing challenges to equitable integration into public health interventions. Our model accurately identified short-term mortality risk, potentially serving as a population-wide aging marker.

Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction.

Restless legs syndrome (RLS) affects up to 10% of older adults. Their healthcare is impeded by delayed diagnosis and insufficient treatment. To advance disease prediction and find new entry points for therapy, we performed meta-analyses of genome-wide association studies in 116,647 individuals with RLS (cases) and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci eightfold to 164, including three on chromosome X. Sex-specific meta-analyses revealed largely overlapping genetic predispositions of the sexes (rg = 0.96). Locus annotation prioritized druggable genes such as glutamate receptors 1 and 4, and Mendelian randomization indicated RLS as a causal risk factor for diabetes. Machine learning approaches combining genetic and nongenetic information performed best in risk prediction (area under the curve (AUC) = 0.82-0.91). In summary, we identified targets for drug development and repurposing, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up and provided evidence that nonlinear interactions are likely relevant to RLS risk prediction.

Rare variants in genes coding for components of the terminal pathway of the complement system in preeclampsia.

Preeclampsia is a common multifactorial disease of pregnancy. Dysregulation of the complement activation is among emerging candidates responsible for disease pathogenesis. In a targeted exomic sequencing study we identified 14 variants within nine genes coding for components of the membrane attack complex (MAC, C5b-9) that are associated with preeclampsia. We found two rare missense variants in the C5 gene that predispose to preeclampsia (rs200674959: I1296V, OR (CI95) = 24.13 (1.25-467.43), p-value = 0.01 and rs147430470: I330T, OR (CI95) = 22.75 (1.17-440.78), p-value = 0.01). In addition, one predisposing rare variant and one protective rare variant were discovered in C6 (rs41271067: D396G, OR (CI95) = 2.93 (1.18-7.10), p-value = 0.01 and rs114609505: T190I, 0.02 OR (CI95) = 0.47 (0.22-0.92), p-value = 0.02). The results suggest that variants in terminal complement pathway predispose to preeclampsia.

Patterns of reproductive health in inflammatory rheumatic diseases and other immune-mediated diseases: a nationwide registry study.

OBJECTIVES: Rheumatic diseases may impair reproductive success and pregnancy outcomes, but systematic evaluations across diseases are lacking. We conducted a nationwide cohort study to examine the impact of rheumatic diseases on reproductive health measures, comparing the impacts with those of other immune-mediated diseases (IMDs). METHODS: Out of all of the 5 339 804 Finnish citizens, individuals born 1964-1984 and diagnosed with any of the 19 IMDs before age 30 (women) or 35 (men) were matched with 20 controls by birth year, sex, and education. We used data from nationwide health registers to study the impact of IMDs on reproductive health measures, such as reproductive success and, for women, ever having experienced adverse maternal and perinatal outcomes. RESULTS: Several of the rheumatic diseases, particularly SLE, JIA, and seropositive RA, were associated with higher rates of childlessness and fewer children. The risks for pre-eclampsia, newborns being small for gestational age, preterm delivery, non-elective Caesarean sections, and need of neonatal intensive care were increased in many IMDs. Particularly, SLE, SS, type 1 diabetes, and Addison's disease showed >2-fold risks for some of these outcomes. In most rheumatic diseases, moderate (1.1-1.5-fold) risk increases were observed for diverse adverse pregnancy outcomes, with similar effects in IBD, celiac disease, asthma, ITP, and psoriasis. CONCLUSION: Rheumatic diseases have a broad impact on reproductive health, with effects comparable with that of several other IMDs. Of the rheumatic diseases, SLE and SS conferred the largest risk increases on perinatal adverse event outcomes.

Risk factors for severe respiratory syncytial virus infection during the first year of life: development and validation of a clinical prediction model.

BACKGROUND: Novel immunisation methods against respiratory syncytial virus (RSV) are emerging, but knowledge of risk factors for severe RSV disease is insufficient for optimal targeting of interventions against them. Our aims were to identify predictors for RSV hospital admission from registry-based data and to develop and validate a clinical prediction model to guide RSV immunoprophylaxis for infants younger than 1 year. METHODS: In this model development and validation study, we studied all infants born in Finland between June 1, 1997, and May 31, 2020, and in Sweden between June 1, 2006, and May 31, 2020, along with the data for their parents and siblings. Infants were excluded if they died or were admitted to hospital for RSV within the first 7 days of life. The outcome was hospital admission due to RSV bronchiolitis during the first year of life. The Finnish study population was divided into a development dataset (born between June 1, 1997, and May 31, 2017) and a temporal hold-out validation dataset (born between June 1, 2017, and May 31, 2020). The development dataset was used for predictor discovery and selection in which we screened 1511 candidate predictors from the infants', parents', and siblings' data, and developed a logistic regression model with the 16 most important predictors. This model was then validated using the Finnish hold-out validation dataset and the Swedish dataset. FINDINGS: In total, there were 1 124 561 infants in the Finnish development dataset, 130 352 infants in the Finnish hold-out validation dataset, and 1 459 472 infants in the Swedish dataset. In addition to known predictors such as severe congenital heart defects (adjusted odds ratio 2·89, 95% CI 2·28-3·65), we confirmed some less established predictors for RSV hospital admission, most notably oesophageal malformations (3·11, 1·86-5·19) and lower complexity congenital heart defects (1·43, 1·25-1·63). The prediction model's C-statistic was 0·766 (95% CI 0·742-0·789) in Finnish data and 0·737 (0·710-0·762) in Swedish validation data. The infants in the highest decile of predicted RSV hospital admission probability had 4·5 times higher observed risk compared with others. Calibration varied according to epidemic intensity. The model's performance was similar to a machine learning (XGboost) model using all 1511 candidate predictors (C-statistic in Finland 0·771, 95% CI 0·754-0·788). The prediction model showed clinical utility in decision curve analysis and in hypothetical number needed to treat calculations for immunisation, and its C-statistic was similar across different strata of parental income. INTERPRETATION: The identified predictors and the prediction model can be used in guiding RSV immunoprophylaxis in infants, or as a basis for further immunoprophylaxis targeting tools. FUNDING: Sigrid Jusélius Foundation, European Research Council, Pediatric Research Foundation, and Academy of Finland.

Waist-hip ratio is superior to BMI in predicting liver-related outcomes and synergizes with harmful alcohol use.

BACKGROUND: Obesity is associated with liver disease, but the best obesity-related predictor remains undefined. Controversy exists regarding possible synergism between obesity and alcohol use for liver-related outcomes (LRO). We assessed the predictive performance for LROs, and synergism with alcohol use, of abdominal obesity (waist-hip ratio, WHR), and compared it to overall obesity (body mass index, BMI). METHODS: Forty-thousand nine-hundred twenty-two adults attending the Finnish health-examination surveys, FINRISK 1992-2012 and Health 2000 studies, were followed through linkage with electronic healthcare registries for LROs (hospitalizations, cancers, and deaths). Predictive performance of obesity measures (WHR, waist circumference [WC], and BMI) were assessed by Fine-Gray models and time-dependent area-under-the-curve (AUC). RESULTS: There are 355 LROs during a median follow-up of 12.9 years (509047.8 person-years). WHR and WC emerge as more powerful predictors of LROs than BMI. WHR shows significantly better 10-year AUC values for LROs (0.714, 95% CI 0.685-0.743) than WC (0.648, 95% CI 0.617-0.679) or BMI (0.550, 95% CI 0.514-0.585) both overall and separately among men and women. WHR is predictive also in BMI strata. Absolute 10-year risks of LROs are more dependent on WHR than BMI. Moreover, WHR shows a significant supra-additive interaction effect with harmful alcohol use for liver-related outcomes (excess 10-year cumulative incidence of 2.8% from the interaction), which is not seen between BMI and harmful alcohol use. CONCLUSIONS: WHR is a better predictor than BMI or WC for LROs, and WHR better reflects the synergism with harmful alcohol use. WHR should be included in clinical assessment when evaluating obesity-related risks for liver outcomes.

Obesity has been linked to liver disease, but the most accurate measure for predicting obesity-related liver disease outcomes remains uncertain. In this study, we analyzed data from over 40,000 adults to compare the extent to which different measures of obesity can predict liver-related outcomes, such as severe liver disease, liver failure, or death from liver disease. The measures of obesity were the ratio of waist circumference to hip circumference (waist-hip ratio, WHR), waist circumference (WC), and body mass index (BMI). Our findings reveal that WHR and WC are stronger predictors of these outcomes than BMI. In particular, WHR demonstrated superior predictive ability and this predictive ability was influenced by harmful alcohol use. This study suggests that WHR may be a relatively simple but useful measure for clinicians to use when predicting obesity-related risks for liver health.

A FinnGen pilot clinical recall study for Alzheimer's disease.

Successful development of novel therapies requires that clinical trials are conducted in patient cohorts with the highest benefit-to-risk ratio. Population-based biobanks with comprehensive health and genetic data from large numbers of individuals hold promise to facilitate identification of trial participants, particularly when interventions need to start while symptoms are still mild, such as for Alzheimer's disease (AD). This study describes a process for clinical recall studies from FinnGen. We demonstrate the feasibility to systematically ascertain customized clinical data from FinnGen participants with ICD10 diagnosis of AD or mild cognitive disorder (MCD) in a single-center cross-sectional study testing blood-based biomarkers and cognitive functioning in-person, computer-based and remote. As a result, 19% (27/140) of a pre-specified FinnGen subcohort were successfully recalled and completed the study. Hospital records largely validated registry entries. For 8/12 MCD patients, other reasons than AD were identified as underlying diagnosis. Cognitive measures correlated across platforms, with highest consistencies for dementia screening (r = 0.818) and semantic fluency (r = 0.764), respectively, for in-person versus telephone-administered tests. Glial fibrillary acidic protein (GFAP) (p < 0.002) and phosphorylated-tau 181 (pTau-181) (p < 0.020) most reliably differentiated AD from MCD participants. We conclude that informative, customized clinical recall studies from FinnGen are feasible.

Persistent organic pollutants associate with liver disease in a Finnish general population sample.

BACKGROUND AND AIMS: Persistent organic pollutants (POPs) have multiple adverse effects on human health. Recent studies show a possible association with liver disease, but population-based data are scarce. In this population-based study, we studied the associations between POPs and biomarkers of liver disease and incident liver disease. METHODS: This study consisted of 2789 adults that participated in the environmental toxin subset of the Finnish health-examination survey, FINRISK 2007. Toxins were measured from serum samples, and standard liver tests and dynamic aspartate aminotransferase-alanine aminotransferase ratio (dAAR) were measured as biomarkers of liver function. Associations between POPs and the biomarkers were then analysed using linear regression. Associations between POPs and incident liver disease (n = 36) were analysed by Cox regression. RESULTS: Organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs) and several perfluorinated alkyl substances exhibited statistically significant positive associations with several biomarkers of liver injury (betacoefficient per SD 0.04-0.14, p < 0.05). These associations were stronger in subgroups of individuals with obesity or non-alcoholic fatty liver disease. OCPs, PCBs and perfluoro-octanoic acid also had significant positive associations with dAAR, which can be used to predict risk of incident severe liver outcomes (beta coefficient per SD 0.05-0.08, p < 0.05). OCPs and PCBs were also significantly and positively associated with incident liver disease (hazard ratio per SD 1.82 95% CI 1.21-2.73, p < 0.01 and hazard ratio per SD 1.69, 95% CI 1.07-2.68, p < 0.05 respectively). CONCLUSIONS: Several POPs show positive associations with markers of liver injury and incident liver disease, suggesting that environmental toxins are important risk factors for chronic liver disease.

Identification of complement factor H variants that predispose to pre-eclampsia: A genetic and functional study.

OBJECTIVE: The objective of the study was to investigate the role of genetic variants in complement proteins in pre-eclampsia. DESIGN: In a case-control study involving 609 cases and 2092 controls, five rare variants in complement factor H (CFH) were identified in women with severe and complicated pre-eclampsia. No variants were identified in controls. SETTING: Pre-eclampsia is a leading cause of maternal and fetal morbidity and mortality. Immune maladaptation, in particular, complement activation that disrupts maternal-fetal tolerance leading to placental dysfunction and endothelial injury, has been proposed as a pathogenetic mechanism, but this remains unproven. POPULATION: We genotyped 609 pre-eclampsia cases and 2092 controls from FINNPEC and the national FINRISK cohorts. METHODS: Complement-based functional and structural assays were conducted in vitro to define the significance of these five missense variants and each compared with wild type. MAIN OUTCOME MEASURES: Secretion, expression and ability to regulate complement activation were assessed for factor H proteins harbouring the mutations. RESULTS: We identified five heterozygous rare variants in complement factor H (L3V, R127H, R166Q, C1077S and N1176K) in seven women with severe pre-eclampsia. These variants were not identified in controls. Variants C1077S and N1176K were novel. Antigenic, functional and structural analyses established that four (R127H, R166Q, C1077S and N1176K) were deleterious. Variants R127H and C1077S were synthesised, but not secreted. Variants R166Q and N1176K were secreted normally but showed reduced binding to C3b and consequently defective complement regulatory activity. No defect was identified for L3V. CONCLUSIONS: These results suggest that complement dysregulation due to mutations in complement factor H is among the pathophysiological mechanisms underlying severe pre-eclampsia.

Nationwide health, socio-economic and genetic predictors of COVID-19 vaccination status in Finland.

Understanding factors associated with COVID-19 vaccination can highlight issues in public health systems. Using machine learning, we considered the effects of 2,890 health, socio-economic and demographic factors in the entire Finnish population aged 30-80 and genome-wide information from 273,765 individuals. The strongest predictors of vaccination status were labour income and medication purchase history. Mental health conditions and having unvaccinated first-degree relatives were associated with reduced vaccination. A prediction model combining all predictors achieved good discrimination (area under the receiver operating characteristic curve, 0.801; 95% confidence interval, 0.799-0.803). The 1% of individuals with the highest predicted risk of not vaccinating had an observed vaccination rate of 18.8%, compared with 90.3% in the study population. We identified eight genetic loci associated with vaccination uptake and derived a polygenic score, which was a weak predictor in an independent subset. Our results suggest that individuals at higher risk of suffering the worst consequences of COVID-19 are also less likely to vaccinate.

Abdominal obesity and alcohol use modify the impact of genetic risk for incident advanced liver disease in the general population.

BACKGROUND & AIMS: Genetic variants, abdominal obesity and alcohol use are risk factors for incident liver disease (ILD). We aimed to study whether variants either alone or when aggregated into genetic risk scores (GRSs) associate with ILD, and whether waist-hip ratio (WHR) or alcohol use interacts with this risk. METHODS: Our study included 33 770 persons (mean age 50 years, 47% men) who participated in health-examination surveys (FINRISK 1992-2012 or Health 2000) with data on alcohol use, WHR and 63 genotypes associated with liver disease. Data were linked with national health registers for liver-related outcomes (hospitalizations, malignancies and death). Exclusions were baseline clinical liver disease. Mean follow-up time was 12.2 years. Cox regression analyses between variants and ILD were adjusted for age, sex and BMI. RESULTS: Variants in PNPLA3, IFNL4, TM6SF2, FDFT1, PPP1R3B, SERPINA1 and HSD17B13 were associated with ILD. GRSs calculated from these variants were not associated with WHR or alcohol use, but were exponentially associated with ILD (up to 25-fold higher risk in high versus low score). The risk of ILD in individuals with high GRS and high WHR or alcohol use compared with those with none of these risk factors was increased by up to 90-fold. GRSs provided new prognostic information particularly in individuals with high WHR. CONCLUSIONS: The effect of multiple genetic variants on the risk of ILD is potentiated by abdominal obesity and alcohol use. Simple GRSs may help to identify individuals with adverse lifestyle who are at a particularly high risk of ILD.

Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank.

Blood lipids and metabolites are markers of current health and future disease risk. Here, we describe plasma nuclear magnetic resonance (NMR) biomarker data for 118,461 participants in the UK Biobank. The biomarkers cover 249 measures of lipoprotein lipids, fatty acids, and small molecules such as amino acids, ketones, and glycolysis metabolites. We provide an atlas of associations of these biomarkers to prevalence, incidence, and mortality of over 700 common diseases ( nightingalehealth.com/atlas ). The results reveal a plethora of biomarker associations, including susceptibility to infectious diseases and risk of various cancers, joint disorders, and mental health outcomes, indicating that abundant circulating lipids and metabolites are risk markers beyond cardiometabolic diseases. Clustering analyses indicate similar biomarker association patterns across different disease types, suggesting latent systemic connectivity in the susceptibility to a diverse set of diseases. This work highlights the value of NMR based metabolic biomarker profiling in large biobanks for public health research and translation.

Longitudinal metabolomics of increasing body-mass index and waist-hip ratio reveals two dynamic patterns of obesity pandemic.

BACKGROUND/OBJECTIVE: This observational study dissects the complex temporal associations between body-mass index (BMI), waist-hip ratio (WHR) and circulating metabolomics using a combination of longitudinal and cross-sectional population-based datasets and new systems epidemiology tools. SUBJECTS/METHODS: Firstly, a data-driven subgrouping algorithm was employed to simplify high-dimensional metabolic profiling data into a single categorical variable: a self-organizing map (SOM) was created from 174 metabolic measures from cross-sectional surveys (FINRISK, n = 9708, ages 25-74) and a birth cohort (NFBC1966, n = 3117, age 31 at baseline, age 46 at follow-up) and an expert committee defined four subgroups of individuals based on visual inspection of the SOM. Secondly, the subgroups were compared regarding BMI and WHR trajectories in an independent longitudinal dataset: participants of the Young Finns Study (YFS, n = 1286, ages 24-39 at baseline, 10 years follow-up, three visits) were categorized into the four subgroups and subgroup-specific age-dependent trajectories of BMI, WHR and metabolic measures were modelled by linear regression. RESULTS: The four subgroups were characterised at age 39 by high BMI, WHR and dyslipidemia (designated TG-rich); low BMI, WHR and favourable lipids (TG-poor); low lipids in general (Low lipid) and high low-density-lipoprotein cholesterol (High LDL-C). Trajectory modelling of the YFS dataset revealed a dynamic BMI divergence pattern: despite overlapping starting points at age 24, the subgroups diverged in BMI, fasting insulin (three-fold difference at age 49 between TG-rich and TG-poor) and insulin-associated measures such as triglyceride-cholesterol ratio. Trajectories also revealed a WHR progression pattern: despite different starting points at the age of 24 in WHR, LDL-C and cholesterol-associated measures, all subgroups exhibited similar rates of change in these measures, i.e. WHR progression was uniform regardless of the cross-sectional metabolic profile. CONCLUSIONS: Age-associated weight variation in adults between 24 and 49 manifests as temporal divergence in BMI and uniform progression of WHR across metabolic health strata.

Cross-sectionally Calculated Metabolic Aging Does Not Relate to Longitudinal Metabolic Changes-Support for Stratified Aging Models.

CONTEXT: Aging varies between individuals, with profound consequences for chronic diseases and longevity. One hypothesis to explain the diversity is a genetically regulated molecular clock that runs differently between individuals. Large human studies with long enough follow-up to test the hypothesis are rare due to practical challenges, but statistical models of aging are built as proxies for the molecular clock by comparing young and old individuals cross-sectionally. These models remain untested against longitudinal data. OBJECTIVE: We applied novel methodology to test if cross-sectional modeling can distinguish slow vs accelerated aging in a human population. METHODS: We trained a machine learning model to predict age from 153 clinical and cardiometabolic traits. The model was tested against longitudinal data from another cohort. The training data came from cross-sectional surveys of the Finnish population (n = 9708; ages 25-74 years). The validation data included 3 time points across 10 years in the Young Finns Study (YFS; n = 1009; ages 24-49 years). Predicted metabolic age in 2007 was compared against observed aging rate from the 2001 visit to the 2011 visit in the YFS dataset and correlation between predicted vs observed metabolic aging was determined. RESULTS: The cross-sectional proxy failed to predict longitudinal observations (R2 = 0.018%, P = 0.67). CONCLUSION: The finding is unexpected under the clock hypothesis that would produce a positive correlation between predicted and observed aging. Our results are better explained by a stratified model where aging rates per se are similar in adulthood but differences in starting points explain diverging metabolic fates.