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ObjectivesTo form a James Lind Alliance (JLA) Priority Setting Partnership (PSP) to determine research priorities related to the cause, diagnosis, treatment and management of pernicious anaemia (PA) from the perspectives of patients, carers and clinicians.DesignThe PSP conducted two surveys and a workshop to identify the Top 10 questions for research. A first survey identified questions relating to the cause, diagnosis, treatment and management of PA. A literature search checked whether any of these questions had already been answered. A second survey asked respondents to identify and rank their top 10 questions from the list of questions from the first survey. An online workshop used an adapted nominal group technique to agree a final Top 10.ResultsIn the first survey, 933 people submitted 3480 responses that were categorised and summarised to generate a long list of 40 questions. None had been answered by previous research. The combined rankings from the 1068 patients, carers and clinicians who took part in the second survey identified a short list of 16 questions. These were discussed at the final workshop to agree the final Top 10. The number one question was about an accurate and reliable diagnostic test for PA. The other nine questions were about making treatment safe and effective, understanding why people with PA vary in their need for treatment, links to other conditions, and how to encourage clinicians to take PA seriously and provide long-term care.ConclusionsThis JLA PSP enabled patients, carers and clinicians to work together to agree the Top 10 uncertainties relating to the cause, diagnosis, management and treatment of PA. Addressing any of these questions will greatly benefit the end-users of research, the people whose daily lives and decisions will be directly affected by generating high quality research evidence.

PurposeThe Maharashtra Anaemia Study 3 (MAS 3) aims to (1) Investigate the nutritional, environmental, and economic impacts on haemoglobin concentration/anaemia, (2) Identify the underlying micronutrient causes of anaemia and (3) Investigate the association between anaemia and physical and cognitive development of Indian children during their first 18 years of life. This paper introduces the MAS 3 cohort, which consists of data collected from the participants in the prospective Pune Maternal Nutrition Study from the antenatal period to children at 18 years of age (1996–2014) in the Maharashtra state, India.ParticipantsRecruitment of 2466 married non-pregnant women, and their husbands, took place between June 1994 and April 1996 in six villages, approximately 50 km from Pune city in India. Women were followed up monthly to identify those who became pregnant. A total of 797 pregnant women were followed up for data collection at or near gestational week 18 and 28, with further data collection for women and children occurring within 72 hours of delivery, for both live and stillbirths. Of the 797 women, 710 were included in the MAS 3 cohort, and long-term follow-up of children occurred at 6 years, 12 years and 18 years of age.Findings to dateIn the MAS 3 cohort, most mothers (73%) were aged between 18 and 25 years at the time of their final prepregnancy visit (baseline), and half (55%) belonged to families of middle-upper socioeconomic status (SES). At the children’s baseline (birth) visit, children had a mean birth weight of 2630 g (SD: 376), with one third (31%) of low birth weight. At the 6-year, 12-year and 18-year follow-up visits, data were available for 706 (99%), 689 (97%) and 694 (98%) children.Future plansMAS 3 will be used to address a number of research objectives, including (1) Trends of haemoglobin and anaemia-related micronutrients from age 6 to 18 years, (2) Micronutrient causes of anaemia during childhood, (3) Prevalence and risk factors for maternal anaemia and childhood anaemia, (4) Impact of maternal anaemia on immediate birth outcomes and (5) Intergenerational risk factors associated with anaemia.

1 H Nuclear Magnetic Resonance spectroscopy ( 1 H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top‐down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non‐identical twin pairs donated plasma and urine samples longitudinally. We acquired 1 H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common‐environmental), individual‐environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual‐environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in 1 H NMR‐detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker‐discovery studies. We provide a power‐calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect 1 H NMR‐based biomarkers quantifying predisposition to disease. Synopsis Metabolites are small molecules involved in biochemical processes in living systems. Their concentration in biofluids, such as urine and plasma, can offer insights into the functional status of biological pathways within an organism, and reflect input from multiple levels of biological organization—genetic, epigenetic, transcriptomic, and proteomic—as well as from environmental and lifestyle factors. Metabolite levels have the potential to indicate a broad variety of deviations from the ‘normal’ physiological state, such as those that accompany a disease, or an increased susceptibility to disease. A number of recent studies have demonstrated that metabolite concentrations can be used to diagnose disease states accurately. A more ambitious goal is to identify metabolite biomarkers that are predictive of future disease onset, providing the possibility of intervention in susceptible individuals. If an extreme concentration of a metabolite is to serve as an indicator of disease status, it is usually important to know the distribution of metabolite levels among healthy individuals. It is also useful to characterize the sources of that observed variation in the healthy population. A proportion of that variation—the heritable component—is attributable to genetic differences between individuals, potentially at many genetic loci. An effective, molecular indicator of a heritable, complex disease is likely to have a substantive heritable component. Non‐heritable biological variation in metabolite concentrations can arise from a variety of environmental influences, such as dietary intake, lifestyle choices, general physical condition, composition of gut microflora, and use of medication. Variation across a population in stable‐environmental influences leads to long‐term differences between individuals in their baseline metabolite levels. Dynamic environmental pressures lead to short‐term fluctuations within an individual about their baseline level. A metabolite whose concentration changes substantially in response to short‐term pressures is relatively unlikely to offer long‐term prediction of disease. In summary, the potential suitability of a metabolite to predict disease is reflected by the relative contributions of heritable and stable/unstable‐environmental factors to its variation in concentration across the healthy population. Studies involving twins are an established technique for quantifying the heritable component of phenotypes in human populations. Monozygotic (MZ) twins share the same DNA genome‐wide, while dizygotic (DZ) twins share approximately half their inherited DNA, as do ordinary siblings. By comparing the average extent of phenotypic concordance within MZ pairs to that within DZ pairs, it is possible to quantify the heritability of a trait, and also to quantify the familiality , which refers to the combination of heritable and common‐environmental effects (i.e., environmental influences shared by twins in a pair). In addition to incorporating twins into the study design, it is useful to quantify the phenotype in some individuals at multiple time points. The longitudinal aspect of such a study allows environmental effects to be decomposed into those that affect the phenotype over the short term and those that exert stable influence. For the current study, urine and blood samples were collected from a cohort of MZ and DZ twins, with some twins donating samples on two occasions several months apart. Samples were analysed by 1 H nuclear magnetic resonance ( 1 H NMR) spectroscopy—an untargeted, discovery‐driven technique for quantifying metabolite concentrations in biological samples. The application of 1 H NMR to a biological sample creates a spectrum, made up of multiple peaks, with each peak's size quantitatively representing the concentration of its corresponding hydrogen‐containing metabolite. In each biological sample in our study, we extracted a full set of peaks, and thereby quantified the concentrations of all common plasma and urine metabolites detectable by 1 H NMR. We developed bespoke statistical methods to decompose the observed concentration variation at each metabolite peak into that originating from familial, individual‐environmental, and unstable‐environmental sources. We quantified the variability landscape across all common metabolite peaks in the urine and plasma 1 H NMR metabolomes. We annotated a subset of peaks with a total of 65 metabolites; the variance decompositions for these are shown in Figure 1 . Ten metabolites' concentrations were estimated to have familial contributions in excess of 60%. The average proportion of stable variation across all extracted metabolite peaks was estimated to be 47% in the urine samples and 60% in the plasma samples; the average estimated familiality was 30% for urine and 42% for plasma. These results comprise the first quantitative variation map of the 1 H NMR metabolome. The identification and quantification of substantive widespread stability provides support for the use of these biofluids in molecular epidemiology studies. On the basis of our findings, we performed power calculations for a hypothetical study searching for predictive disease biomarkers among 1 H NMR‐detectable urine and plasma metabolites. Our calculations suggest that sample sizes of 2000–5000 should allow reliable identification of disease‐predictive metabolite concentrations explaining 5–10% of disease risk, while greater sample sizes of 5000–20 000 would be required to identify metabolite concentrations explaining 1–2% of disease risk. We designed a longitudinal twin study to characterize the genetic, stable‐environmental, and longitudinally fluctuating influences on metabolite concentrations in two human biofluids—urine and plasma—focusing specifically on the representative subset of metabolites detectable by 1 H nuclear magnetic resonance ( 1 H NMR) spectroscopy. We identified widespread genetic and stable‐environmental influences on the (urine and plasma) metabolomes, with (30 and 42%) attributable on average to familial sources, and (47 and 60%) attributable to longitudinally stable sources. Ten of the metabolites annotated in the study are estimated to have >60% familial contribution to their variation in concentration. Our findings have implications for the design and interpretation of 1 H NMR‐based molecular epidemiology studies. On the basis of the stable component of variation quantified in the current paper, we specified a model of disease association under which we inferred that sample sizes of a few thousand should be sufficient to detect disease‐predictive metabolite biomarkers.

Background Pernicious anaemia (PA) is characterised by vitamin B 12 deficiency due to autoimmune-mediated loss of gastric parietal cells and intrinsic factor - a specific transporter for B 12 ’s intestinal uptake. The Pernicious Anaemia Society (PAS) is a patient-driven charity that recently identified 10 research priorities for improved diagnosis and management of PA through a James Lind Alliance Priority Setting Partnership. To facilitate research addressing these priorities, the aim of this study was 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 (SurveyMonkey Inc., San Mateo, CA, USA). It comprised twenty-one questions collecting data on demographics, mode and timing of PA diagnosis, comorbidities, family history of PA or other autoimmune conditions, and type and regime of management. The survey was sent to 3,482 PAS members (April - September 2022) via the PAS website, newsletter, and email. Results A total of 1,191 PAS members completed the survey. Of those individuals with a probable ( n  = 471) or suspected PA ( n  = 500) diagnosis defined by higher specificity diagnostics 84% were UK-based and 81% were female, with an age-range of 23–93 years. Diagnosis was predominantly based on low serum B 12 (50%), positive intrinsic factor (38%), and/or parietal cell autoantibodies (15%). Diagnostic delays were common with 37% of participants reported waiting ≥ 3 years for a diagnosis. Nearly half of the participants suffered from one or more other autoimmune diseases. One-third also reported having at least 2 and up to 7 family members with PA or other autoimmune diseases. Vitamin B 12 treatment frequency was highly varied, ranging from daily to 3 monthly B 12 injections, with 52% of participants taking injections outside of the recommended guidelines. Conclusion This study’s findings further highlight the gaps in current diagnostic and management approaches for PA and pave the way forward for future work in accordance with the JLA-PSP research priorities. By characterising a cohort of PA patients and compiling essential baseline data, we provide a foundation for research that supports the development of more effective diagnostic and management strategies.

The liver function test (LFT) is among the most commonly used clinical investigations to assess hepatic function, severity of liver diseases and the effect of therapies, as well as to detect drug-induced liver injury (DILI). To determine the relative contribution of genetic and environmental factors as well as test and quantify the effects of sex, age, BMI and alcohol consumption to variation in liver function test proteins--including alanine amino transaminase (ALT), Albumin, gamma glutamyl transpeptidase (GGT), total bilirubin, total protein, total globulin, aspartate transaminase (AST), and alkaline phosphotase (ALP)--using the classical twin model. Blood samples were collected from a total of 5380 twin pairs from the TwinsUK registry. We measured the expression levels of major proteins associated with the LFT, calculated BMI from measured weight and height and questionnaires were completed for alcohol consumption by the twins. The relative contribution of genetic and environmental factors to variation in the LFT proteins was assessed and quantified using a variance components model fitting approach. Our results show that (1) variation in all the LFTs has a significant heritable basis (h(2) ranging from 20% to 77%); (2) other than GGT, the LFTs are all affected to some extent by common environmental factors (c(2) ranging from 24% to 54%); and (3) a small but significant proportion of the variation in the LFTs was due to confounding effects of age, sex, BMI, and alcohol use. Variation in the LFT proteins is under significant genetic and common environmental control although sex, alcohol use, age and BMI also contribute significantly to inter-individual variation in the LFT proteins. Understanding the underlying genetic contribution of liver function tests may help the interpretation of their results and explain wide variation among individuals.

Vitamin B12 is an essential nutrient that is not made by plants; consequently, unfortified plant-based foods are not a reliable supply. Recent estimates suggest high rates of vitamin B12 deficiency among the vegetarian and vegan populations, particularly in pregnant women or women of child-bearing age who, for ethical and health reasons, are shifting towards higher consumption of plant-based foods in ever-increasing numbers. Vitamin B12 plays crucial metabolic roles across the life-course and in particular during pregnancy and in early development (first 1000 days of life). Evidence now implicates vitamin B12 deficiency with increased risk to a range of neuro, vascular, immune, and inflammatory disorders. However, the current UK recommended nutrient intake for vitamin B12 does not adequately consider the vitamin B12 deficit for those choosing a plant-based diet, including vegetarianism and in particular veganism, representing a hidden hunger. We provide a cautionary note on the importance of preventing vitamin B12 deficits for those individuals choosing a plant-based diet and the health professionals advising them.

The integrated analysis of genotypic and expression data for association with complex traits could identify novel genetic pathways involved in complex traits. We profiled 19,573 expression probes in Epstein-Barr virus-transformed lymphoblastoid cell lines (LCLs) from 299 twins and correlated these with 44 quantitative traits (QTs). For 939 expressed probes correlating with more than one QT, we investigated the presence of eQTL associations in three datasets of 57 CEU HapMap founders and 86 unrelated twins. Genome-wide association analysis of these probes with 2.2 m SNPs revealed 131 potential eQTLs (1,989 eQTL SNPs) overlapping between the HapMap datasets, five of which were in cis (58 eQTL SNPs). We then tested 535 SNPs tagging the eQTL SNPs, for association with the relevant QT in 2,905 twins. We identified nine potential SNP-QT associations (P<0.01) but none significantly replicated in five large consortia of 1,097-16,129 subjects. We also failed to replicate previous reported eQTL associations with body mass index, plasma low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides levels derived from lymphocytes, adipose and liver tissue. Our results and additional power calculations suggest that proponents may have been overoptimistic in the power of LCLs in eQTL approaches to elucidate regulatory genetic effects on complex traits using the small datasets generated to date. Nevertheless, larger tissue-specific expression data sets relevant to specific traits are becoming available, and should enable the adoption of similar integrated analyses in the near future.

Little research has assessed serum 25-hydroxyvitamin D (25(OH)D) concentration and its predictors in Western-dwelling South Asians in a relatively large sample size. This observational, cross-sectional analysis assessed baseline prevalence of 25(OH)D deficiency in UK-dwelling South Asians (aged 40–69 years, 2006–2010) from the UK Biobank Cohort. Serum 25(OH)D measurements were undertaken using the DiaSorin Liaison XL assay. Of 6433 South Asians with a 25(OH)D measurement, using commonly used cut-off thresholds, 55 % (n 3538) had 25(OH)D < 25 nmol/l (severe deficiency) and 92 % (n 5918) had 25(OH)D < 50 nmol/l (insufficiency). Of the participants with a measurement, 20 % (n 1287) had 25(OH)D concentration <15 nmol/l (very severe deficiency). When 824 participants with undetectable (<10 nmol/l) 25(OH)D measurements were included (total n 7257), 29 % (n 2105) had 25(OH)D < 15 nmol/l, 60 % (n 4354) had 25(OH)D < 25 nmol/l and 93 % (n 6749) had 25(OH)D < 50 nmol/l. Logistic regression predictors of 25(OH)D < 25 nmol/l included the following characteristics: being male; Pakistani; higher BMI; 40–59 years old; never consuming oily fish; summer sun exposure <5 h/d, not using a vitamin D-containing supplement, measurement in winter or spring and vegetarianism. In terms of region, median 25(OH)D concentration was 19–20 nmol/l in Scotland, Northern England, the Midlands and Wales. Across Southern England and London, it was slightly higher at 24–25 nmol/l. Our analyses suggest the need for increased awareness of vitamin D deficiency in South Asians as well as urgent public health interventions to prevent and treat vitamin D deficiency in this group.

The human microbiota functions at the interface between diet, medication-use, lifestyle, host immune development and health. It is therefore closely aligned with many of the recognised modifiable factors that influence bone mass accrual in the young, and bone maintenance and skeletal decline in older populations. While understanding of the relationship between micro-organisms and bone health is still in its infancy, two decades of broader microbiome research and discovery supports a role of the human gut microbiome in the regulation of bone metabolism and pathogenesis of osteoporosis as well as its prevention and treatment. Pre-clinical research has demonstrated biological interactions between the microbiome and bone metabolism. Furthermore, observational studies and randomized clinical trials have indicated that therapeutic manipulation of the microbiota by oral administration of probiotics may influence bone turnover and prevent bone loss in humans. In this paper, we summarize the content, discussion and conclusions of a workshop held by the Osteoporosis and Bone Research Academy of the Royal Osteoporosis Society in October, 2020. We provide a detailed review of the literature examining the relationship between the microbiota and bone health in animal models and in humans, as well as formulating the agenda for key research priorities required to advance this field. We also underscore the potential pitfalls in this research field that should be avoided and provide methodological recommendations to facilitate bridging the gap from promising concept to a potential cause and intervention target for osteoporosis.

Rapid advances in ‘omics’ technologies have paved the way forward to an era where more ‘precise’ approaches – ‘precision’ nutrition – which leverage data on genetic variability alongside the traditional indices, have been put forth as the state-of-the-art solution to redress the effects of malnutrition across the life course. We purport that this inference is premature and that it is imperative to first review and critique the existing evidence from large-scale epidemiological findings. We set out to provide a critical evaluation of findings from genome-wide association studies (GWAS) in the roadmap to precision nutrition, focusing on GWAS of micronutrient disposition. We found that a large number of loci associated with biomarkers of micronutrient status have been identified. Mean estimates of heritability of micronutrient status ranged between 20 and 35 % for minerals, 56–59 % for water-soluble and 30–70 % for fat-soluble vitamins. With some exceptions, the majority of the identified genetic variants explained little of the overall variance in status for each micronutrient, ranging between 1·3 and 8 % (minerals), <0·1–12 % (water-soluble) and 1·7–2·3 % for (fat-soluble) vitamins. However, GWAS have provided some novel insight into mechanisms that underpin variability in micronutrient status. Our findings highlight obvious gaps that need to be addressed if the full scope of precision nutrition is ever to be realised, including research aimed at (i) dissecting the genetic basis of micronutrient deficiencies or ‘response’ to intake/supplementation (ii) identifying trans-ethnic and ethnic-specific effects (iii) identifying gene–nutrient interactions for the purpose of unravelling molecular ‘behaviour’ in a range of environmental contexts.