Prospective functional classification of all possible missense variants in PPARG.

Clinical exome sequencing routinely identifies missense variants in disease-related genes, but functional characterization is rarely undertaken, leading to diagnostic uncertainty. For example, mutations in PPARG cause Mendelian lipodystrophy and increase risk of type 2 diabetes (T2D). Although approximately 1 in 500 people harbor missense variants in PPARG, most are of unknown consequence. To prospectively characterize PPARγ variants, we used highly parallel oligonucleotide synthesis to construct a library encoding all 9,595 possible single-amino acid substitutions. We developed a pooled functional assay in human macrophages, experimentally evaluated all protein variants, and used the experimental data to train a variant classifier by supervised machine learning. When applied to 55 new missense variants identified in population-based and clinical sequencing, the classifier annotated 6 variants as pathogenic; these were subsequently validated by single-variant assays. Saturation mutagenesis and prospective experimental characterization can support immediate diagnostic interpretation of newly discovered missense variants in disease-related genes.This work was supported by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (1K08DK102877-01, to A.R.M.; 1R01DK097768-01, to D.A.), NIH/Harvard Catalyst (1KL2TR001100-01, to A.R.M.), the Broad Institute (SPARC award, to A.R.M. and T.M.), and the Wellcome Trust (095564, to K.C.; 107064, to D.B.S.).This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/ng.370

Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

\ua9 2025 World Health OrganizationWithout careful dissection of the ways in which biases can be encoded into artificial intelligence (AI) health technologies, there is a risk of perpetuating existing health inequalities at scale. One major source of bias is the data that underpins such technologies. The STANDING Together recommendations aim to encourage transparency regarding limitations of health datasets and proactive evaluation of their effect across population groups. Draft recommendation items were informed by a systematic review and stakeholder survey. The recommendations were developed using a Delphi approach, supplemented by a public consultation and international interview study. Overall, more than 350 representatives from 58 countries provided input into this initiative. 194 Delphi participants from 25 countries voted and provided comments on 32 candidate items across three electronic survey rounds and one in-person consensus meeting. The 29 STANDING Together consensus recommendations are presented here in two parts. Recommendations for Documentation of Health Datasets provide guidance for dataset curators to enable transparency around data composition and limitations. Recommendations for Use of Health Datasets aim to enable identification and mitigation of algorithmic biases that might exacerbate health inequalities. These recommendations are intended to prompt proactive inquiry rather than acting as a checklist. We hope to raise awareness that no dataset is free of limitations, so transparent communication of data limitations should be perceived as valuable, and absence of this information as a limitation. We hope that adoption of the STANDING Together recommendations by stakeholders across the AI health technology lifecycle will enable everyone in society to benefit from technologies which are safe and effective

The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly

Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the first release of the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analyse the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. MGRB individuals have fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK Biobank cohorts, consistent with risk depletion. Age-related somatic changes are correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing

Occult Metabolic Bone Disease in Chronic Pancreatitis

Background: Chronic pancreatitis (CP) leads to malabsorption and metabolic bone disease (MBD). Alcoholic CP (ACP) and tropical CP (TCP) are the two common types of CP. Objective: We investigated the presence of occult MBD in patients with CP and compared the same between ACP and TCP. Materials and Methods: In this cross‑sectional, observational study, we included serial patients of CP in different stages and are grouped as ACP (Group 1; n = 67) and TCP (Group 2; n = 35). We determined serum calcium, phosphorus, alkaline phosphatase, 25‑hydroxyvitamin D (25OHD), and intact parathyroid hormone (PTH) levels. Bone mineral density (BMD) was measured by dual‑energy X‑ray absorptiometry in the neck of the left femur. MBD was defined by the presence of either low bone mass (Z‑score <−2) or osteomalacia. The results were analyzed using appropriate statistical methods. Results: The study participants (85 males; 17 females) had a mean age of 40.8 ± 12.6 years, CP duration of 3.7 ± 4.7 years, and Body Mass Index of 22.5 ± 3.2 kg/m2. A total of 37 (36%) patients had MBD (osteomalacia in 31 and low bone mass in 6). The frequency of MBD was same in the TCP (16/35) and ACP (21/65) groups (P = 0.1940). Elevated PTH (>70 pg/mL) was seen in 14 patients with 25OHD deficiency and low calcium (<8.5 mg/dL) in 29 patients. BMD did not show a significant correlation with the duration of CP. Conclusion: Occult MBD is seen in a third of patients with CP and is similar irrespective of the etiology. The disease is silent and mandates active screening in all susceptible individuals.Keywords: Chronic pancreatitis, metabolic bone disease, osteomalacia, osteopenia, osteoporosi

Comparisons of polyexposure, polygenic, and clinical risk scores in risk prediction of type 2 diabetes

OBJECTIVE: To establish a polyexposure score (PXS) for type 2 diabetes (T2D) incorporating 12 nongenetic exposures and examine whether a PXS and/or a polygenic risk score (PGS) improves diabetes prediction beyond traditional clinical risk factors. RESEARCH DESIGN AND METHODS: We identified 356,621 unrelated individuals from the UK Biobank of White British ancestry with no prior diagnosis of T2D and normal HbA1c levels. Using self-reported and hospital admission information, we deployed a machine learning procedure to select the most predictive and robust factors out of 111 nongenetically ascertained exposure and lifestyle variables for the PXS in prospective T2D. We computed the clinical risk score (CRS) and PGS by taking a weighted sum of eight established clinical risk factors and >6 million single nucleotide polymorphisms, respectively. RESULTS: In the study population, 7,513 had incident T2D. The C-statistics for the PGS, PXS, and CRS models were 0.709, 0.762, and 0.839, respectively. Individuals in the top 10% of PGS, PXS, and CRS had 2.00-, 5.90-, and 9.97-fold greater risk, respectively, compared to the remaining population. Addition of PGS and PXS to CRS improved T2D classification accuracy, with a continuous net reclassification index of 15.2% and 30.1% for cases, respectively, and 7.3% and 16.9% for controls, respectively. CONCLUSIONS: For T2D, the PXS provides modest incremental predictive value over established clinical risk factors. However, the concept of PXS merits further consideration in T2D risk stratification and is likely to be useful in other chronic disease risk prediction models