Background: Inter-individual variability in lipid response to dietary interventions complicates cardiometabolic prevention in overweight and obese women. Although several dietary strategies improve lipid profiles on average, predictors of comprehensive, multi-marker lipid improvement remain unclear. Objective: To identify baseline clinical predictors of comprehensive lipid improvement across seven dietary interventions and to evaluate the performance of three machine learning (ML) classifiers in predicting a composite Global Score. Methods: This secondary analysis pooled individual-level data from 284 overweight or obese women enrolled in three randomized controlled trials (RCTs). Participants were assigned to continuous energy restriction (CER), intermittent energy restriction (IER), intermittent energy and carbohydrate restriction (IECR), IECR with added protein and fat (IECR+PF), high-carbohydrate, high-monounsaturated-fat, or daily energy-restriction diets. Eleven baseline clinical features served as predictors. Four binary lipid improvement scores (TC/HDL, LDL/HDL, non-HDL cholesterol, TG/HDL) were calculated from baseline to week 12, and a composite Global Score was defined as TRUE only when all four improved. Three ML classifiers (J48, Logistic Model Tree [LMT], Random Forest) were evaluated using stratified 10-fold cross-validation. Results: Overall, 30-35% achieved improvement in the Global Score. Improvement rates varied across diets, with High Mono and High Carb showing the highest rates (48.4%). LMT performed best (AUC = 0.66; accuracy = 70%). Baseline TG, BMI, age, total cholesterol, and weight were the strongest predictors. Conclusions: Comprehensive lipid improvement varies across dietary strategies and is influenced by baseline triglycerides, adiposity, age, and diet type. ML-based stratification may support personalized dietary prescriptions.

Background/Objectives: Intermittent fasting (IF) has shown feature effectiveness in reducing blood pressure, highlighting the need for personalized intervention strategies. Methods: To address this, a machine learning framework was developed to predict the likelihood of blood pressure improvement (≥5 mmHg systolic reduction) across different IF and calorie restriction protocols in premenopausal women without diagnosed hypertension. Results: The model achieved 77% accuracy and an AUC of 0.8 in distinguishing responders from non-responders. Logistic regression analysis identified dietary intervention type as the strongest predictor of success, with Intermittent Energy and Carbohydrate Restriction + free Protein and Fat (IECR + FF) and Intermittent Energy and Carbohydrate Restriction + free Protein and Fat (IECR) protocols showing the highest effectiveness (coefficients 0.55 and 0.41 respectively). Decision tree analysis revealed age in years as a critical stratification factor, with younger patients (≤47 years) responding optimally to IECR + FF combinations, while older patients benefited from IECR, Continuous Energy Restriction (CER), or Intermittent Energy Restriction (IER) approaches. Notably, waist-to-hip ratio emerged as the strongest negative predictor, indicating that central adiposity significantly impedes blood pressure improvement regardless of intervention type. Higher baseline HDL positively predicted success, while elevated LDL and the DER diet were associated with poor outcomes. The complementary analytical approaches demonstrated that logistic regression and decision tree methods highlight different aspects of the data, with the former identifying independent linear associations and the latter suggesting potential non-linear interactions and candidate thresholds involving age years, dietary intervention type, baseline blood pressure, and metabolic markers. Conclusions: This exploratory, hypothesis-generating analysis was conducted in a cohort of premenopausal women without diagnosed hypertension and is not intended to inform clinical decision-making. The observed patterns should be interpreted as preliminary and may reflect sample-specific effects or model instability. Confirmation in larger, independent, and more diverse populations is essential before any clinical relevance can be inferred.

Background: Autism spectrum disorder (ASD) diagnosis traditionally relies on behavioral assessments, which can be subjective and often lead to delayed identification. Recent advances in metabolomics and machine learning offer promising alternatives for more objective and precise diagnostic approaches. Methods: First-morning urine samples were collected from 52 children (32 with ASD and 20 neurotypical controls), aged 5.04 ± 1.87 and 5.50 ± 1.74 years, respectively. Using liquid chromatography-mass spectrometry (LC-MS), 293 metabolites were identified and categorized into 189 endogenous and 104 exogenous metabolites. Various machine learning classifiers (random forest, logistic regression, random tree, and naïve Bayes) were applied to differentiate ASD and control groups through 10-fold cross-validation. Results: The random forest classifier achieved 85% accuracy and an area under the curve (AUC) of 0.9 using all 293 metabolites. Classification based solely on endogenous metabolites yielded 85% accuracy and an AUC of 0.86, whereas using exogenous metabolites alone resulted in lower performance (71% accuracy and an AUC of 0.72). Conclusion: This study demonstrates the potential of urine metabolomic profiling, particularly endogenous metabolites, as a complementary diagnostic tool for ASD. The high classification accuracy highlights the feasibility of developing assistive diagnostic methods based on metabolite profiles, although further research is needed to link these profiles to specific behavioral characteristics and ASD subtypes.

Essential for brain formation and protective against tauopathy, activity-dependent neuroprotective protein (ADNP) is critical for neurogenesis and cognitive functions, while regulating steroid hormone biogenesis. As such, de novo mutations in ADNP lead to syndromic autism and somatic ADNP mutations parallel Alzheimer's disease progression. Furthermore, clinical trials with the ADNP fragment NAP (the investigational drug davunetide) showed efficacy in women suffering from the tauopathy progressive supranuclear palsy and differentially boosted memory in men (spatial) and women (verbal), exhibiting prodromal Alzheimer's disease. While autism is more prevalent in boys and Alzheimer's disease in women, both involve impaired neurogenesis. Here, we asked whether ADNP sex-dependently regulates neurogenesis. Using bromodeoxyuridine (BrdU) as a marker of neurogenesis, we identified two-fold higher labeling in the hippocampal sub-ventricular zone of ADNP-intact male versus female mice. Adnp haplo-insufficient (Adnp+/-) mice or mice CRSIPR/Cas9-edited to present the most prevalent neurodevelopmental ADNP syndrome mutation, p.Tyr718* (Tyr) showed dramatic reductions in male BrdU incorporation, resulting in mutated females presenting higher labeling than males. Treatment with NAP compensated for the male reduction of BrdU labeling. Mechanistically, hippocampal RNAseq revealed male-specific Tyr down-regulation of endoplasmic reticulum unfolded protein response genes critical for sex-dependent organogenesis. Newly discovered mitochondrial accessibility of ADNP was inhibited by the Tyr718* mutation further revealing female-specific Tyr downregulation of mitochondrial ATP6. NAP moderated much of the differential expression caused by p.Tyr718*, accompanied by the down-regulation of neurotoxic, pro-inflammatory and pro-apoptotic genes. Thus, ADNP is a key regulator of sex-dependent neurogenesis that acts by controlling canonical pathways, with NAP compensating for fundamental ADNP deficiencies, striding toward clinical development targeting the ADNP syndrome and related neurodevelopmental/neurodegenerative diseases.

Activity-dependent neuroprotective protein (ADNP) is essential for neurodevelopment and de novo mutations in ADNP cause the ADNP syndrome. From brain pathologies point of view, tauopathy has been demonstrated at a young age, implying stunted development coupled with early/accelerated neurodegeneration. Given potential genotype–phenotype differences and age-dependency, we have assessed here a cohort of 15 individuals (1–27-year-old), using 1–3 longitudinal parent (caretaker) interview/s (Vineland 3 questionnaire) over several years. Our results indicated developmental delays, or even developmental arrests, coupled with potential spurts of development at early ages. Severe outcomes correlated with the truncating high impact mutation, in other words, the remaining mutated protein length as well as with the tested individual age, corroborating the hypothesis of developmental delays coupled with accelerated aging. A significant correlation was noted between mutated protein length and communication, implying a high impact of ADNP on communicative skills. Additionally, correlations were discovered between the two previously described epi-genetic signatures in ADNP emphasizing aberrant acquisition of motor behaviors, with truncating mutations around the nuclear localization signal being mostly affected. Finally, all individuals seem to acquire an age equivalent of 1–6 years, requiring disease modification treatment, such as the ADNP-derived drug candidate, NAP (davunetide), which has recently shown efficacy in women suffering from the neurodegenerative disorder, progressive supranuclear palsy (PSP), a late-onset tauopathy.

Cocaine use disorder (CUD) is a chronic neuropsychiatric disorder estimated to effect 1–3

Activity-dependent neuroprotective protein (ADNP), essential for brain formation/function, reveals multiple cytoplasmic and chromatin interacting sites. Computational modeling, alongside the Vineland Adaptive Behavior Scales, a leading instrument supporting the diagnosis of intellectual/developmental disabilities, now revealed a protective frame shift/stop mutation in ADNP. Thus, a woman with inherited mutation, ADNP_Glu931Glyfs*12 (VB), showed above average Vineland performance. Bioinformatics/in silico protein modeling indicated that while ADNP contains four 14-3-3 protein interaction sites (instrumental for ADNP nuclear/cytoplasmic shuttling), ADNP_Glu931Glyfs*12 contains an additional fifth 14-3-3 interaction site, implicating stronger associations. Furthermore, the endogenous neuroprotective (investigational drug, davunetide) NAPVSIPQ (NAP) site was involved in the ADNP and ADNP_Glu931Glyfs*12-14-3-3 interactions. In this respect, the mutation also enhanced ADNP-SH3 associations (another NAPVISP interaction site 354-361 aa on ADNP, critical for cytoskeletal/cellular signaling). HB, the 8-year-old VB's son, while inheriting the mother's ADNP mutation, further presented a heterozygous pathogenic de novo mutation ADNP, p.Arg730Thrfs*5. However, in comparison to carriers of a similar p.Arg730* mutation (part of the autistic/intellectual disability ADNP syndrome), HB exhibited overall better Vineland 3 standard score of 70–80 for all measures, compared to the nominal score of 20 in a 27-year-old ADNP, p.Arg730* subject and the 100 ± 15 norm, corroborating ADNP_Glu931Glyfs*12 protection.

NAP (NAPVSIPQ, drug candidate name, davunetide) is the neuroprotective fragment of activity-dependent neuroprotective protein (ADNP). Recent studies identified NAPVSIP as a Src homology 3 (SH3) domain-ligand association site, responsible for controlling signalling pathways regulating the cytoskeleton. Furthermore, the SIP motif in NAP/ADNP was identified as crucial for direct microtubule end-binding protein interaction facilitating microtubule dynamics and Tau microtubule interaction, at the microtubule end-binding protein site EB1 and EB3. Most de novo ADNP mutations reveal heterozygous STOP or frameshift STOP aberrations, driving the autistic/intellectual disability-related ADNP syndrome. Here, we report for the first time on a de novo missense mutation, resulting in ADNP containing NAPVISPQE instead of NAPVSIPQQ, in a child presenting developmental hypotonia, possibly associated with inflammation affecting food intake in early life coupled with fear of peer interactions and suggestive of a novel case of the ADNP syndrome. In silico modelling showed that the mutation Q (polar side chain) to E (negative side chain) affected the electrostatic characteristics of ADNP (reducing, while scattering the electrostatic positive patch). Comparison with the most prevalent pathogenic ADNP mutation, p.Tyr719*, indicated a further reduction in the electrostatic patch. Previously, exogenous NAP partially ameliorated deficits associated with ADNP p.Tyr719* mutations in transfected cells and in CRISPR/Cas9 genome edited cell and mouse models. These findings stress the importance of the NAP sequence in ADNP and as a future putative therapy for the ADNP syndrome.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by elevated blood glucose levels. Despite the availability of pharmacological treatments, dietary plans, and exercise regimens, T2DM remains a significant global cause of mortality. As a result, there is an increasing interest in exploring lifestyle interventions, such as intermittent fasting (IF). This study aims to identify underlying patterns and principles for effectively improving T2DM risk parameters through IF. By analyzing data from multiple randomized clinical trials investigating various IF interventions in humans, a machine learning algorithm was employed to develop a personalized recommendation system. This system offers guidance tailored to pre-diabetic and diabetic individuals, suggesting the most suitable IF interventions to improve T2DM risk parameters. With a success rate of 95%, this recommendation system provides highly individualized advice, optimizing the benefits of IF for diverse population subgroups. The outcomes of this study lead us to conclude that weight is a crucial feature for females, while age plays a determining role for males in reducing glucose levels in blood. By revealing patterns in diabetes risk parameters among individuals, this study not only offers practical guidance but also sheds light on the underlying mechanisms of T2DM, contributing to a deeper understanding of this complex metabolic disorder.

(1) Background: Recently, we showed aberrant nuclear/cytoplasmic boundaries/activity-dependent neuroprotective protein (ADNP) distribution in ADNP-mutated cells. This malformation was corrected upon neuronal differentiation by the ADNP-derived fragment drug candidate NAP (davunetide). Here, we investigated the mechanism of NAP nuclear protection. (2) Methods: CRISPR/Cas9 DNA-editing established N1E-115 neuroblastoma cell lines that express two different green fluorescent proteins (GFPs)-labeled mutated ADNP variants (p.Tyr718* and p.Ser403*). Cells were exposed to NAP conjugated to Cy5, followed by live imaging. Cells were further characterized using quantitative morphology/immunocytochemistry/RNA and protein quantifications. (3) Results: NAP rapidly distributed in the cytoplasm and was also seen in the nucleus. Furthermore, reduced microtubule content was observed in the ADNP-mutated cell lines. In parallel, disrupting microtubules by zinc or nocodazole intoxication mimicked ADNP mutation phenotypes and resulted in aberrant nuclear-cytoplasmic boundaries, which were rapidly corrected by NAP treatment. No NAP effects were noted on ADNP levels. Ketamine, used as a control, was ineffective, but both NAP and ketamine exhibited direct interactions with ADNP, as observed via in silico docking. (4) Conclusions: Through a microtubule-linked mechanism, NAP rapidly localized to the cytoplasmic and nuclear compartments, ameliorating mutated ADNP-related deficiencies. These novel findings explain previously published gene expression results and broaden NAP (davunetide) utilization in research and clinical development.

De novo heterozygous mutations in activity-dependent neuroprotective protein (ADNP) cause autistic ADNP syndrome. ADNP mutations impair microtubule (MT) function, essential for synaptic activity. The ADNP MT-associating fragment NAPVSIPQ (called NAP) contains an MT end-binding protein interacting domain, SxIP (mimicking the active-peptide, SKIP). We hypothesized that not all ADNP mutations are similarly deleterious and that the NAPV portion of NAPVSIPQ is biologically active. Using the eukaryotic linear motif (ELM) resource, we identified a Src homology 3 (SH3) domain-ligand association site in NAP responsible for controlling signaling pathways regulating the cytoskeleton, namely NAPVSIP. Altogether, we mapped multiple SH3-binding sites in ADNP. Comparisons of the effects of ADNP mutations p.Glu830synfs*83, p.Lys408Valfs*31, p.Ser404* on MT dynamics and Tau interactions (live-cell fluorescence-microscopy) suggested spared toxic function in p.Lys408Valfs*31, with a regained SH3-binding motif due to the frameshift insertion. Site-directed-mutagenesis, abolishing the p.Lys408Valfs*31 SH3-binding motif, produced MT toxicity. NAP normalized MT activities in the face of all ADNP mutations, although, SKIP, missing the SH3-binding motif, showed reduced efficacy in terms of MT-Tau interactions, as compared with NAP. Lastly, SH3 and multiple ankyrin repeat domains protein 3 (SHANK3), a major autism gene product, interact with the cytoskeleton through an actin-binding motif to modify behavior. Similarly, ELM analysis identified an actin-binding site on ADNP, suggesting direct SH3 and indirect SHANK3/ADNP associations. Actin co-immunoprecipitations from mouse brain extracts showed NAP-mediated normalization of Shank3-Adnp-actin interactions. Furthermore, NAP treatment ameliorated aberrant behavior in mice homozygous for the Shank3 ASD-linked InsG3680 mutation, revealing a fundamental shared mechanism between ADNP and SHANK3.

OPINION article Front. Endocrinol., 23 March 2022Sec. Neuroendocrine Science https://doi.org/10.3389/fendo.2022.867442

Intermittent fasting (IF) is the cycling between periods of eating and fasting. The main types of IF are: complete alternate-day fasting; time-restricted feeding (eating within specific time frames such as the most prevalent 16:8 fast, with 16 hours of fasting and 8 hours for eating); religious fasting such as the Ramadan (occurs one month per year, with eating taking place only after nightfall). IF can be effective in reducing metabolic disorders and age-related diseases by bringing about changes in metabolic parameters associated with type 2 diabetes. Questions do remain, however, about the effects of the different types of IF as a function of the age at which fasting begins, gender and severity of type 2 diabetes. In this paper we describe a machine learning approach to selecting the best type of IF to improve health in type 2 diabetes. For the purposes of this research, the health outcomes of interest are changes in fasting glucose and insulin. The different types of intermittent fast offer promising non-pharmacological approaches to improving health at the population level, with multiple public health benefits.

We present OnTheFly (http://bhapp.c2b2.columbia.edu/OnTheFly/index.php), a database comprising a systematic collection of transcription factors (TFs) of Drosophila melanogaster and their DNA-binding sites. TFs predicted in the Drosophila melanogaster genome are annotated and classified and their structures, obtained via experiment or homology models, are provided. All known preferred TF DNA-binding sites obtained from the B1H, DNase I and SELEX methodologies are presented. DNA shape parameters predicted for these sites are obtained from a high throughput server or from crystal structures of protein–DNA complexes where available. An important feature of the database is that all DNA-binding domains and their binding sites are fully annotated in a eukaryote using structural criteria and evolutionary homology. OnTheFly thus provides a comprehensive view of TFs and their binding sites that will be a valuable resource for deciphering non-coding regulatory DNA.