Bruce H. R. Wolffenbuttel

Background: Despite early and continued treatment, neurocognitive deficits have been reported in individuals with phenylketonuria (PKU). As treatment targets in PKU differ internationally, direct cross-national comparison of neurocognitive outcomes remains scarce, yet potentially valuable. This pilot study compared neurocognitive performance between Australian and Dutch individuals with PKU and examined associations between metabolic control and cognitive outcomes. Methods: Thirteen individuals with PKU from the Queensland Lifespan Metabolic Service (4M;9F, mean age 14 years) were assessed using the Cambridge Neuropsychological Test Automated Battery (CANTAB). Data from 13 age-matched Dutch participants (4M;9F, mean age 15 years) were obtained from the Frist Dutch PKU Database (FDPD+) databank. Six cognitive domains were assessed: sustained attention, multitasking, emotion recognition, reaction time, visuospatial memory, and decision-making. Concurrent and prior-year Phe, Tyr, and Phe:Tyr ratios were collected for all participants. Results: The two cohorts performed comparably across most CANTAB domains. No significant between-group differences survived correction for multiple comparisons. Concurrent Phe was significantly associated with poorer sustained attention on both the RVP 1T (ρ = −0.57, p = 0.015) and RVP 3T (ρ = −0.78, p = 0.001). The concurrent Phe:Tyr ratio was similarly associated with RVP 3T performance (ρ = −0.76, p = 0.001). Participants with concurrent Phe ≥360 µmol/L showed significantly worse sustained attention compared to those with Phe <360 µmol/L (p = 0.048). Conclusion: The neurocognitive profile of Australian and Dutch individuals with PKU appeared broadly comparable in this pilot study. The association between concurrent Phe and sustained attention underscores the importance of maintaining metabolic control beyond childhood.

Pernicious anemia is a chronic autoimmune disease characterized by impaired of vitamin B12 absorption and deficiency. Current diagnostic approaches rely on laboratory biomarkers with limitations in sensitivity and specificity, leading to diagnostic uncertainty and potentially delayed and/or suboptimal treatment. The 2024 NICE guidelines acknowledge that treatment frequency should be guided by individual symptom response rather than current one-size-fits-all schedules; however, the symptom heterogeneity underpinning this recommendation remains poorly characterized. We conducted secondary analysis of symptom survey data from 1,117 members of the Pernicious Anaemia Society (PAS) collected between August 2010 and November 2012. We applied latent class analysis (LCA) to 46 self-reported indicators comprising demographic characteristics, symptoms, and comorbid conditions, to identify distinct symptom-based subtypes and assess whether such subgroups can inform more tailored management of PA. Associations between symptom subtypes and diagnostic test results, age when symptoms first started, symptom duration, and treatment satisfaction were examined using chi-square and Fisher's exact tests. The best model included three distinct symptom subtypes: High Burden (n=334, 29.9%), Moderate Burden (n=613, 54.9%), and Low Burden (n=170, 15.2%). The High Burden subtype exhibited fatigue (99.4%), cognitive dysfunction (97.9% memory loss), neurological manifestations (95.5% clumsiness), and suicidal thoughts (41.6%). Intrinsic factor antibody (IFA) status did not differ significantly across subtypes (χ ² =1.04, df=2, p=0.593). Age at symptom onset differed significantly across subtypes (p=0.002), with the Low Burden subtype overrepresented in older age groups. The High Burden subtype had the longest diagnostic delays (56.9% >2 years). Standard three-monthly injections showed low satisfaction across all classes (High Burden: 8.5%, Moderate Burden: 23.7%, Low Burden: 30.7%). Symptom-based stratification identifies clinically meaningful subgroups independent of IFA status, supporting tailored, symptom-guided treatment rather than the current piecemeal approach. The inverse association between high symptom burden and age of onset warrants clinical attention. The co-occurrence of markedly elevated rates of depression and suicidal thoughts in the High Burden subtype suggest that integrated mental health assessments should form part of routine clinical management.

Background: Health-adjusted life expectancy (HALE) quantifies health span, i.e., years lived in good health, at the population level and serves as an important benchmark for public health policy. However, existing HALE estimates are retrospective and prevalence-based, limiting their applicability for prospective, individualized health forecasting. We therefore developed and externally validated a model-based framework to prospectively estimate health span at the individual level. Methods: We trained logistic regression models to predict nine chronic diseases and all-cause mortality using 15 questionnaire-based demographic and lifestyle features in 499,045 UK Biobank participants (median 14-year follow-up), with external validation in 152,229 Lifelines Biobank participants (median 5-year follow-up). Model discrimination for nine disease outcomes and mortality was assessed using the area under the receiver operating characteristic curve (ROC-AUC), calibration curves, and positive and negative predictive values. Predicted disease and mortality risks were combined with Global Burden of Disease disability weights to construct a model-based prediction of individual health span. Prospective model-derived health span estimates were benchmarked against retrospective HALE estimates from the World Health Organization (WHO).Findings: Model-based predicted health span closely approximated WHO retrospective HALE benchmarks at age 60 years (UK Biobank: 18.0 years [95% CI 17.9–18.0]; Lifelines: 19.5 years [19.4–19.7]). Health span stratification by quintiles revealed steep gradients in disease burden, lifestyle factors, and mortality. Model discrimination for all study outcomes ranged from good to excellent (ROC-AUC 0.66–0.89 in UK Biobank), with comparable performance in external validation (ROC-AUC 0.57–0.88 in Lifelines).Interpretation: We introduce a scalable, interpretable, and externally validated machine learning framework to prospectively estimate individual-level health span using 15 questionnaire features. This method enables real-time evaluation of preventive interventions and digital health strategies, bridging population-level metrics and personalised health forecasting.

Introduction: Type 1 diabetes is an autoimmune disease with a strong genetic basis. The aim of this study was to identify additional single-nucleotide polymorphisms (SNPs) for type 1 diabetes age at diagnosis and to replicate previously identified loci. Research design and methods: Meta genome-wide association studies of age at diagnosis from eight cohorts (n=5910 in total) were performed in three models. Model 1 was age at diagnosis with no covariates. Model 2 was age at diagnosis adjusted for DR3/DR4 genotype categories. Model 3 was similar to model 2, including the most significant SNP from model 2 (coded additively). Models 1 and 2 were also performed for major histocompatibility complex (MHC) imputed data. In addition, we tested previously identified loci for age at diagnosis and type 1 diabetes risk for association with age at diagnosis in model 1. Results: In model 1, we identified a genome-wide significant locus (rs2856721, p=3.3×10 ⁻¹¹ ) in the MHC region whose effect was attenuated in model 2 (p=0.03). In model 2, we identified another locus in the MHC region, rs76730244, p=4.9×10 ⁻⁹ , which was associated with age at diagnosis adjusted for DR3/DR4 genotypes. Model 3 and analysis of the MHC region did not reveal novel loci. Among 14 previously identified SNPs for age at diagnosis, 6 were confirmed; in addition, 11 out of 78 non-HLA loci for type 1 diabetes risk were associated with age at diagnosis. Conclusions: We identified rs76730244 in the MHC region for age at diagnosis of type 1 diabetes, which was independent of the HLA-DR3/DR4 genotype categories. We also confirmed 6 previously identified SNPs and showed that 11 non-HLA loci for type 1 diabetes risk are associated with age at diagnosis.

Introduction: Tissue glycation, measured as skin autofluorescence (SAF) with an AGE Reader, has been associated with incident type 2 diabetes (T2D), cardiovascular disease (CVD), and CVD and cancer mortality. Validated reference intervals in healthy individuals are needed to make useful and precise risk estimations. Methodology: This study utilises data from 82,870 participants of Western European descent in the Lifelines Cohort Study, a population-based study in the Netherlands. Reference values for SAF were established in healthy participants. Decade-specific mean SAF values were evaluated for former and current smokers, T2D, CVD, and impaired renal function. We also assessed the association of regular physical activity (PA) with SAF scores. Results: Reference values for healthy individuals were established in 8,179 men and 7,930 women between 18 and 70 years, who had never smoked, had a BMI below 35 kg/m ² , and did not meet the metabolic syndrome criteria. Linear regression analyses yielded the following prediction for SAF, separately by sex: in males, SAF predicted = (0.0191 × age) + 1.038, and in females, SAF predicted = (0.0188 × age) + 0.994. Current smokers had consistently higher SAF scores. There was a progressively higher SAF with a higher number of pack-years of smoking in each age decade, in both sexes. For each decade, both people with T2D and CVD had significantly higher SAF values compared to healthy individuals. The same applied to participants with impaired renal function. There was a complex non-linear relationship between PA and age-adjusted SAF score: mean SAF was the highest in sedentary participants (e.g., 0 min/week moderate-to-vigorous physical activity), lowest in those with 150–299 min/week PA, and gradually increasing in participants with higher than 600 min/week PA. Conclusions: We provide robust reference values for SAF, established in healthy individuals of Western European descent, separately by sex, who have never smoked. Higher levels of SAF are observed in former and current smokers and in people with T2D, CVD, and impaired renal function. The relationship between physical activity and SAF scores is complex, with higher SAF scores demonstrated in sedentary people and those performing a large amount of moderate-to-vigorous physical exercise.

Impaired awareness of hypoglycaemia (IAH) is a complication of diabetes treatment, whereby individuals are no longer able to feel an oncoming hypoglycaemic event. IAH may be a result of central nervous system adaptation to low recurrent hypoglycaemias, however the precise pathways involved remain unknown. This study employed proteomics analysis to explore potential pathophysiological pathways in IAH, using a nested case-control design within the Dutch Type 1 Diabetes Biomarker study. The Olink^® Cardiovascular II panel was used for targeted proteomics, comparing 67 individuals with IAH to 108 age- and sex-matched individuals with normal awareness of hypoglycaemia (NAH). Univariate analysis revealed that agouti-related protein (AGRP) levels were significantly lower in individuals with IAH compared to NAH (6.12 NPX vs. 6.44 NPX, FDR-adjusted P = 0.012). In multivariate models adjusted for sex and diabetes duration, AGRP remained significant before p-value adjustment (P < 0.001) but not after adjusting for false discovery rate (FDR) (P = 0.057). AGRP, known for its orexigenic effects and expression in the arcuate nucleus of the hypothalamus, is involved in glucose sensing and hypothalamic-pituitary-adrenal (HPA) axis stimulation, suggesting its potential role in the pathophysiology of IAH. This study highlights the need for further research to clarify AGRP’s role and its possible implications for managing IAH in diabetes.

Tissue glycation, assessed through skin autofluorescence (SAF) using an AGE reader, is linked to an increased risk of type 2 diabetes (T2D), cardiovascular disease (CVD), as well as mortality from both CVD and cancer. It was also suggested that higher SAF be linked to a greater incidence of cancer. We aimed to evaluate the relationship between SAF and the time to a new cancer diagnosis in the Lifelines Cohort Study, a population-based study in the Northern Netherlands, in participants with and without T2D. Initial participants’ screening was performed between 2006 and 2013. Detailed pathology diagnoses were obtained from the Dutch Nationwide Pathology Databank (Palga) and linked to Lifelines data up to March 2023. We performed Cox proportional hazards analyses adjusted for confounders to investigate the relationship between SAF and cancer incidence. A total of 77,961 participants (75,678 without T2D; 42% males; mean age 43 ± 12 years and 2,283 with T2D; 53% males; mean age 56 ± 12 years), who were free of cancer, were followed for a median of 11.5 years. Cumulative cancer incidence was 10.7% in males and 12.5% in females without diabetes, and 23.6% and 20.2% in males and females with T2D, respectively. SAF was significantly linked to an increased cancer incidence (HR 2.36, 95% CI 2.26–2.45, p < 0.001) in the entire cohort. This relationship was significantly stronger in males than in females (HR 3.13, 95% CI 2.95–3.33, p < 0.001 vs. 1.96, 95% CI 1.86–2.07, p < 0.001). After adjusting for age, sex, waist circumference, body mass index, pack-years of smoking and presence of diabetes and metabolic syndrome, the association between SAF and cancer remained significant (HR 1.11, 95% CI 1.05–1.17, p < 0.001). Sensitivity analyses for incident cancers diagnosed more than 2 years after baseline Lifelines screening and incident cancer after skin cancer exclusion yielded similar results. Especially lung, oesophageal and urinary tract cancer (all p < 0.001) and ovarian, female genital and liver cancer (p < 0.05) were associated with increased SAF. Similarly, higher SAF was linked to increased cancer incidence in people with T2D (HR 1.76, 95% CI 1.50–2.06, p < 0.001). This relationship was no longer statistically significant after adjusting for age and sex (HR 1.07, 95% CI 0.89–1.29) or for all confounders (HR 1.07, 95% CI 0.86–1.32). We conclude that, alongside its previously established application in evaluating the risk of future diabetes, CVD, as well as cause-specific mortality in people without diabetes, higher SAF measurements are linked to a slightly higher incidence of cancer.

Objective: Pernicious anaemia (PA) is characterised by vitamin B12 deficiency due to autoimmune-mediated loss of gastric parietal cells and intrinsic factor. The Pernicious Anaemia Society (PAS) identified 10 research priorities for PA through a James-Lind Alliance Priority Setting Partnership (JLA-PSP). This study aimed to survey PAS members to identify and characterise a cohort of patients to form a PA research repository. Methods: An online survey was designed using SurveyMonkey, comprising 21 questions on diagnosis, comorbidities, family history, and management. The survey was sent to 3,482 PAS members (April-September 2022) via the PAS website and email. Results: A total of 1,191 PAS members completed the survey. Among individuals with a probable (n=471) or suspected PA (n=500) diagnosis, 84% were UK-based, and 81% were female, with an age range of 23-93 years. Diagnosis was predominantly based on low serum B12 (50%), positive intrinsic factor (38%), and/or parietal cell autoantibodies (15%). Diagnostic delays were common, with 37% waiting ≥3 years for a diagnosis. Nearly half had one or more other autoimmune diseases. One-third reported having at least 2 and up to 7 family members with PA or other autoimmune diseases. Vitamin B12 treatment frequency varied widely, ranging from daily to 3-monthly injections. Conclusion: This study highlights gaps in current diagnostic and management approaches for PA, paving the way for future work in line with the JLA-PSP research priorities. By characterising a cohort of PA patients and compiling baseline data, we provide a foundation for research to develop more effective diagnostic and management strategies.

We aimed to assess the association of SAF with cardiovascular mortality in the general population and the possible association between SAF with other disease-specific mortality rates. We evaluated 77,143 participants without known diabetes or cardiovascular disease. The cause of death was ascertained by the municipality database. The associations between SAF and all-cause mortality, cardiovascular mortality and cancer mortality were assessed with Cox proportional hazard analysis.After a median follow-up of 115 months, 1447 participants were deceased (1.9%). SAF and age-adjusted SAF-z score were higher in all mortality groups. Cox regression analysis revealed that the highest quartile of SAF was associated with increased odds of cardiovascular mortality, (HR) 12.6 (7.3–21.7) and after adjusting for age (HR 1.8 (1.0–3.2)). Significance was lost after additional adjustments for sex, smoking status, and BMI (HR 1.4 (0.8–2.5). For cancer-related mortality the highest quartile of SAF was associated with higher probability of mortality in all models (unadjusted HR 8.6 (6.6–11.3), adjusted for age HR 2.1 (1.6–2.8)), adjusted for age, sex, smoking status, and BMI HR 1.7 (1.3–2.4)). SAF is associated with all-cause mortality as well as cardiovascular and cancer-related mortality in the general population.