Insulin resistance, a precursor to type 2 diabetes, is characterized by impaired insulin action in tissues. Current methods for measuring insulin resistance, while effective, are expensive, inaccessible, not widely available and hinder opportunities for early intervention. In this study, we remotely recruited the largest dataset to date across the US to study insulin resistance (N=1,165 participants, with median BMI=28 kg/m2, age=45 years, HbA1c=5.4%), incorporating wearable device time series data and blood biomarkers, including the ground-truth measure of insulin resistance, homeostatic model assessment for insulin resistance (HOMA-IR). We developed deep neural network models to predict insulin resistance based on readily available digital and blood biomarkers. Our results show that our models can predict insulin resistance by combining both wearable data and readily available blood biomarkers better than either of the two data sources separately (R2=0.5, auROC=0.80, Sensitivity=76%, and specificity 84%). The model showed 93% sensitivity and 95% adjusted specificity in obese and sedentary participants, a subpopulation most vulnerable to developing type 2 diabetes and who could benefit most from early intervention. Rigorous evaluation of model performance, including interpretability, and robustness, facilitates generalizability across larger cohorts, which is demonstrated by reproducing the prediction performance on an independent validation cohort (N=72 participants). Additionally, we demonstrated how the predicted insulin resistance can be integrated into a large language model agent to help understand and contextualize HOMA-IR values, facilitating interpretation and safe personalized recommendations. This work offers the potential for early detection of people at risk of type 2 diabetes and thereby facilitate earlier implementation of preventative strategies.

Early diagnosis and discovery of therapeutic drug targets are crucial objectives for the effective management of Alzheimer's Disease (AD). Current approaches for AD diagnosis and treatment planning are based on radiological imaging and largely inaccessible for population-level screening due to prohibitive costs and limited availability. Recently, blood tests have shown promise in diagnosing AD and highlighting possible biomarkers that can be used as drug targets for AD management. Blood tests are significantly more accessible to disadvantaged populations, cost-effective, and minimally invasive. However, biomarker discovery in the context of AD diagnosis is complex as there exist important associations between various biomarkers. Here, we introduce BRAIN (Biomarker Representation, Analysis, and Interpretation Network), a novel machine learning (ML) framework to jointly optimize the diagnostic accuracy and biomarker discovery processes to identify all relevant biomarkers that contribute to AD diagnosis. Using a holistic graph-based representation for biomarkers, we highlight their inter-dependencies and explain why different ML models identify different discriminative biomarkers. We apply BRAIN to a publicly available blood biomarker dataset, revealing three novel biomarker sub-networks whose interactions vary between the control and AD groups, offering a new paradigm for drug discovery and biomarker analysis for AD.

Blood biomarkers are an essential tool for healthcare providers to diagnose, monitor, and treat a wide range of medical conditions. Current reference values and recommended ranges often rely on population-level statistics, which may not adequately account for the influence of inter-individual variability driven by factors such as lifestyle and genetics. In this work, we introduce a novel framework for predicting future blood biomarker values and define personalized references through learned representations from lifestyle data (physical activity and sleep) and blood biomarkers. Our proposed method learns a similarity-based embedding space that captures the complex relationship between biomarkers and lifestyle factors. Using the UK Biobank (257K participants), our results show that our deep-learned embeddings outperform traditional and current state-of-the-art representation learning techniques in predicting clinical diagnosis. Using a subset of UK Biobank of 6440 participants who have follow-up visits, we validate that the inclusion of these embeddings and lifestyle factors directly in blood biomarker models improves the prediction of future lab values from a single lab visit. This personalized modeling approach provides a foundation for developing more accurate risk stratification tools and tailoring preventative care strategies. In clinical settings, this translates to the potential for earlier disease detection, more timely interventions, and ultimately, a shift towards personalized healthcare.

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease. In particular, immune-based blood-biomarkers might be a promising option, given the recently discovered cross-talk of immune cells of the central nervous system with those in the peripheral immune system. With the help of machine learning algorithms and mechanistic modeling approaches, such as agent-based modeling, an in-depth analysis of the simulation of cell dynamics is possible as well as of high-dimensional omics resources indicative of pathway signaling changes. Here, we give a background on advances in research on brain-immune system cross-talk in Alzheimer's disease and review recent machine learning and mechanistic modeling approaches which leverage modern omics technologies for blood-based immune system-related biomarker discovery.

Breast cancer is one of the most threatening diseases in women's life; thus, the early and accurate diagnosis plays a key role in reducing the risk of death in a patient's life. Mammography stands as the reference technique for breast cancer screening; nevertheless, many countries still lack access to mammograms due to economic, social, and cultural issues. Latest advances in computational tools, infrared cameras and devices for bio-impedance quantification, have given a chance to emerge other reference techniques like thermography, infrared thermography, electrical impedance tomography and biomarkers found in blood tests, therefore being faster, reliable and cheaper than other methods. In the last two decades, the techniques mentioned above have been considered as parallel and extended approaches for breast cancer diagnosis, as well many authors concluded that false positives and false negatives rates are significantly reduced. Moreover, when a screening method works together with a computational technique, it generate a "computer-aided diagnosis" system. The present work aims to review the last breakthroughs about the three techniques mentioned earlier, suggested machine learning techniques for breast cancer diagnosis, thus, describing the benefits of some methods in relation with other ones, such as, logistic regression, decision trees, random forest, deep and convolutional neural networks. With this, we studied several hyper-parameters optimization approaches with parzen tree optimizers to improve the performance of baseline models. An exploratory data analysis for each database and a benchmark of convolutional neural networks for the database of thermal images are presented.

Alzheimer's disease (AD) is characterized by brain protein aggregates of amyloid-β (Aβ) and phosphorylated tau (pTau) that become plaques and tangles, and dystrophic neurites surrounding the plaques, which are accompanied by downstream neurodegeneration. These protein changes can be used as biomarkers detected through positron emission tomography (PET) imaging and in cerebrospinal fluid (CSF), allowing for ATN (amyloid, tau, and neurodegeneration) classification of patients. This phenotyping has become standard in AD clinical trials to overcome the high misclassification rate (20 to 30%) for clinical criteria and also enables enrollment of preclinical AD patients. The recent approval of the first disease-modifying anti-amyloid immunotherapy, aducanumab, for AD will generate a need for widely accessible and inexpensive biomarkers for ATN classification of patients with cognitive complaints. Technological advances have also overcome the challenges of measuring the extraordinarily low amounts of brain-derived proteins in blood samples, and recent studies indicate that AD blood tests may soon be possible. The Aβ42 variant of Aβ is aggregation-prone and is deposited in plaques in the brains of people with AD, whereas the shorter Aβ40 isoform is by far the most abundant Aβ peptide (see the figure). Thus, as AD progresses and Aβ42 forms plaques, its concentration in the CSF and blood is reduced. Ascertaining the ratio of Aβ42 and Aβ40 concentrations in the CSF is known to adjust for between-individual differences in “total” Aβ production, thereby increasing concordance with amyloid PET imaging to detect brain amyloidosis. Applying the same principle for blood plasma Aβ, immunoprecipitation–mass spectrometry (IP-MS) measures of plasma Aβ42/Aβ40 ratio can reach an accuracy exceeding 90% to identify brain amyloidosis ([ 1 ][1]). A population-based study of 441 asymptomatic elderly individuals indicates that IP-MS plasma Aβ can identify those who are amyloid PET-positive with high accuracy ([ 2 ][2]). ![Figure][3] Biomarkers of Alzheimer's disease Low amyloid-β (Aβ) 42/40 isoform ratio is associated with brain amyloidosis, and several phosphorylated tau (pTau) fragments increase with tau pathology; both are specific blood biomarkers for Alzheimer's disease (AD). Among neurodegeneration biomarkers, neurofilament light (NFL) is modestly increased in AD, and total tau (T-tau) is markedly increased only in cerebrospinal fluid (CSF), and not blood, in AD. Glial fibrillary acidic protein (GFAP) is a candidate blood biomarker for astrocytic activation, to indicate neuroinflammation. GRAPHIC: KELLIE HOLOSKI/ SCIENCE The question then arises whether plasma Aβ detection can replace PET or CSF tests for brain amyloidosis. A potential issue is that Aβ is produced not only in the brain but also in platelets and peripheral tissues, which will obscure the central nervous system–derived Aβ signal in plasma. Consequently, in amyloid PET-positive cases, plasma Aβ42/Aβ40 ratio is only ∼10% lower than in individuals without brain amyloidosis, whereas it is more than 40% lower in CSF ([ 3 ][4]). This leads to an overlap that introduces challenges to robustly classify individuals as being either amyloid positive or negative, especially in those with Aβ42/Aβ40 ratios close to the cut-off for normality. Algorithms combining plasma Aβ42/Aβ40 ratio with the ϵ4 variant of apolipoprotein E ( APOE ), which is the major AD risk gene, and age (the main risk factor for AD) increase accuracy in detecting brain amyloidosis by 2 to 6% ([ 2 ][2], [ 3 ][4]). However, merging biomarker data with genetic risk and aging may cause confusion because some younger APOE -ϵ4 noncarriers with low plasma Aβ42/Aβ40 will be misclassified as amyloid negative by the algorithm, whereas a proportion of older individuals with homozygous APOE -ϵ4 but normal plasma Aβ42/Aβ40 will be wrongly classified as amyloid positive. Tau protein is truncated into amino-terminal to mid-domain fragments before being secreted in blood plasma and CSF ([ 4 ][5]). CSF pTau has long been used as an AD-specific biomarker. A major breakthrough is the use of new ultrasensitive methods that allow for quantification of pTau in blood plasma, with high concentrations occurring in AD ([ 5 ][6]). Of 321 patients and controls, high plasma concentrations of pTau181 fragments were associated with brain tau pathology as measured by PET ([ 6 ][7]). Similar results were subsequently presented for other pTau species, including pTau217 ([ 7 ][8]) and pTau231 ([ 8 ][9]). The findings of very high accuracy of plasma pTau217 in the ability to discriminate AD from other neurodegenerative disorders ([ 7 ][8]) and IP-MS data showing a higher magnitude of increase and better association with amyloid plaques by PET of plasma pTau217 than of pTau181 ([ 4 ][5]) suggest that there may be diagnostic or pathophysiological differences between pTau species, but this remains a matter of debate. Nonetheless, these pTau blood biomarkers all show high concordance with AD pathology at autopsy, with accuracies in differentiating AD from non-AD dementia cases up to 99% for pTau231 ([ 8 ][9]). However, these studies are based on different analytical methods and cohorts. In an attempt to directly compare these pTau species, a study of 381 participants employing digital immunoassays for pTau181, pTau217, and pTau231 found strong correlations with amounts of pTau species in CSF. Moreover, although the fold change was highest for pTau217, the accuracy in identifying amyloid PET positivity was very high for all pTau species ([ 9 ][10]), suggesting that differences are not meaningful. A study of two large cohorts of 883 individuals with cognitive symptoms also showed high accuracy (90 to 91%) of both plasma pTau181 and pTau217 to predict clinical progression to AD dementia in algorithms that include memory and executive function tests and APOE genotyping ([ 10 ][11]). Overall, plasma pTau biomarkers fulfill many requirements for a clinically useful AD test, with a high fold change in AD (between two to four times higher in AD than non-AD controls across studies), and an increase early in the AD continuum (even preclinically), an association with amyloid-associated tau pathophysiology and tangle burden in the brain, and an increase specifically found in AD but not in other types of dementia. The findings of an early increase in plasma pTau fragments in patients with evidence of amyloid plaques, but not tau abnormalities, by PET imaging may be interpreted as a neuronal response to Aβ aggregates that gives rise to increased pTau secretion into CSF and blood plasma. However, findings in biomarker studies are only associations and may not directly reveal causal relationships. For example, plasma pTau231 shows a 10- to 15-fold increase within 24 hours after acute traumatic brain injury, especially evident in younger patients (who are unlikely to have amyloid or tau pathology) ([ 11 ][12]). Total tau (T-tau), referring to any tau variant or fragment regardless of phosphorylation, and other brain proteins such as glial fibrillary acidic protein (GFAP) also increase in blood plasma, hypothetically mediated by a trauma-induced compromise of the blood-brain barrier, with release of proteins preexisting in the extracellular space. Even if different mechanisms operate in specific disorders, further research is needed to understand the mechanisms underlying the increase in plasma pTau in AD. In the search for blood biomarkers of neurodegeneration, it has become evident that in contrast to CSF, where T-tau is markedly increased in AD, T-tau does not work as a biomarker of AD neurodegeneration in blood. Instead, another axonal protein, neurofilament light (NFL), has been evaluated as a substitute AD neurodegeneration biomarker, even though it is not involved in AD pathogenesis. Plasma NFL concentrations correlate well with CSF concentrations, supporting that it reflects brain pathophysiology. But high amounts are found in a wide variety of neurodegenerative disorders, so this biomarker lacks specificity. Nevertheless, plasma NFL, which shows a modest increase in AD, predicts both cognitive deterioration and rate of neurodegeneration as measured by atrophy on brain imaging. Notably, both plasma and CSF NFL concentrations increase in cognitively unimpaired people with autosomal dominant AD 7 years before symptom onset ([ 12 ][13]), so this may be a good biomarker for predicting AD. Another candidate AD blood biomarker includes the astrocytic protein GFAP, which is markedly increased in AD. Plasma GFAP distinguishes amyloid PET-positive and -negative cognitively normal elderly with high accuracy ([ 13 ][14]), and may serve as a blood biomarker for glial activation and neuroinflammation. Despite both rapid and robust reductions in amyloid PET ligand binding after treatment with Aβ immunotherapies (indicative of drug target engagement), effects on cognitive outcomes have been less evident. Therefore, biomarker evidence for downstream effects on reducing tau pathology and neurodegeneration is important to support disease-modifying effects by this class of drugs. Given that in most clinical trials only a small percentage of enrolled patients undergo repeat lumbar puncture for CSF testing, blood biomarkers could play an important role to accomplish this. Data from other areas of clinical neuroscience show that children with spinal muscular atrophy have a marked increase in CSF NFL, but treatment with the antisense oligonucleotide drug nusinersen results in a successive reduction of NFL concentrations in CSF with normalization after ∼7 months, and the reduction correlates with clinical improvements ([ 14 ][15]). Similar, but less pronounced, reductions of plasma NFL are seen with disease-modifying treatments in multiple sclerosis patients. These findings may serve as proof of concept for the usefulness of plasma NFL in identifying downstream drug effects on neurodegeneration. Target engagement for the anti-Aβ drug, aducanumab, was demonstrated in 2017, with dose-dependent reductions on amyloid PET ([ 15 ][16]), but to date there are no reports of effects on blood biomarkers of neurodegeneration (or tau pathology) from any Aβ immunotherapy trial. Current studies of blood AD biomarkers come exclusively from cohorts at highly specialized research centers. Thus, further clinical validation is needed, specifically on the diagnostic accuracy of the AD blood biomarkers, alone or in combination, in consecutive patient populations at memory clinics and in primary care settings. In addition, because plasma pTau increases progressively with tau pathology in the brain and more advanced clinical stage, more data are needed on the accuracy of plasma pTau biomarkers to identify individuals with preclinical or early symptoms who will go on to develop AD. Moreover, studies comparing plasma pTau species in the same cohorts and using the same technology are needed to understand if there are pathophysiological differences across the pTau epitopes. Current assays are research grade, and full analytical validation of methods is needed to achieve accurate and comparable results between laboratories, as well as global efforts to develop certified reference materials to achieve harmonization across assay platforms. Transferring the blood tests to fully automated platforms would also help to streamline these procedures and to establish these blood tests as clinically useful tools. Lastly, to make blood biomarkers attractive substitutes for imaging, costs need to be substantially lower than costs for the PET scans. 1. [↵][17]1. A. Nakamura et al ., Nature 554, 249 (2018). [OpenUrl][18][CrossRef][19][PubMed][20] 2. [↵][21]1. A. Keshavan et al ., Brain 144, 434 (2021). [OpenUrl][22] 3. [↵][23]1. S. E. Schindler et al ., Neurology 93, 17 (2019). [OpenUrl][24] 4. [↵][25]1. N. R. Barthélemy, 2. K. Horie, 3. C. Sato, 4. R. J. Bateman , J. Exp. Med. 217, e20200861 (2020). [OpenUrl][26][CrossRef][27][PubMed][28] 5. [↵][29]1. M. M. Mielke et al ., Alzheimers Dement. 14, 989 (2018). [OpenUrl][30][CrossRef][31][PubMed][28] 6. [↵][32]1. T. K. Karikari et al ., Lancet Neurol. 19, 422 (2020). [OpenUrl][33][CrossRef][34][PubMed][28] 7. [↵][35]1. S. Palmqvist et al ., JAMA 324, 772 (2020). [OpenUrl][36][PubMed][28] 8. [↵][37]1. N. J. Ashton et al ., Acta Neuropathol. 141, 709 (2021). [OpenUrl][38][PubMed][28] 9. [↵][39]1. M. Suárez-Calvet et al ., EMBO Mol. Med. 12, e12921 (2020). [OpenUrl][40] 10. [↵][41]1. S. Palmqvist et al ., Nat. Med. 27, 1034 (2021). [OpenUrl][42] 11. [↵][43]1. R. Rubenstein et al ., JAMA Neurol. 74, 1063 (2017). [OpenUrl][44] 12. [↵][45]1. O. Preische et al ., Nat. Med. 25, 277 (2019). [OpenUrl][46][PubMed][28] 13. [↵][47]1. P. Chatterjee et al ., Transl. Psychiatry 11, 27 (2021). [OpenUrl][48] 14. [↵][49]1. B. Olsson et al ., J. Neurol. 266, 2129 (2019). [OpenUrl][50] 15. [↵][51]1. J. Sevigny et al ., Nature 546, 564 (2017). [OpenUrl][52] Acknowledgments: K.B. has consulted for Axon, Biogen, Lilly, and Roche Diagnostics and is cofounder of Brain Biomarker Solutions in Gothenburg AB. 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A published article in the journal PLOS One led researchers at UT Southwestern Medical Center to the identification of biomarkers in the blood that may result in a quicker diagnosis of autism spectrum disorder (ASD) among children. During their study, researchers uncovered nine serum proteins with the ability to predict the onset of autism. "Serum samples from 76 boys with ASD and 78 typically developing (TD) boys, 18 months-8 years of age, were analyzed to identify possible early biological markers for ASD," said Laura Hewitson, and her colleagues, in their findings. A total of 1,125 proteins were analyzed. There were 86 downregulated proteins and 52 upregulated proteins in ASD." Using a form of artificial intelligence, known as machine learning, nine proteins were established to be significantly correlated with autism. "Using machine learning methods, a panel of serum proteins was identified that may be useful as a blood biomarker for ASD in boys.

The UT Southwestern team has used machine learning tools to analyse hundreds of proteins that has led to the identification of nine serum proteins that predict the disorder. The researchers hope this will help develop more effective therapies for ASD sooner. The study has been published in the journal PLOS ONE. Early diagnosis of ASD is vital to make a difference to the lives of young children living with ASD who are typically not diagnosed until the age of four, says Dwight German, PhD, professor of psychiatry at UT Southwestern and senior author of the study. To date, blood-based biomarkers such as neurotransmitters, cytokines, and markers of mitochondrial dysfunction, oxidative stress, and impaired methylation, have been investigated.