PNAS Nexus

In IVF treatments, extended culture to single blastocyst-transfer is the recommended protocol over cleavage-stage transfer. However, evidence-based criteria for assessing the heterogeneous implications on implantation outcome are lacking. To estimate the causal effect of blastocyst-transfer on implantation outcome, we assembled a multicenter dataset of embryo time-lapse imaging. The data includes a natural source of randomness and has a strong claim for satisfying the assumptions needed for valid causal inference. By fitting a causal forest model, we assessed the Transfer Lift, which quantifies the probability difference in embryo implantation if transferred as a blastocyst versus cleavage-stage. Blastocyst transfer increased the average implantation rate, however we revealed a subpopulation of negative Transfer Lift embryos whose implantation potential is predicted to increase via cleavage-stage transfer. We provide day-of-transfer decision-support tools that are retrospectively estimated to improve implantation rate by 32%, thus demonstrating the efficacy of embryo-level causal inference in reproductive medicine. One Sentence SummaryA causal inference model predicts the heterogeneous effect of prolonged incubation to blastocyst transfer on embryo implantation, thus providing means for optimizing pregnancy rates in IVF treatments.

Cellulose microfibrils that are essential for mechanical strength and overall quality of cotton fibers. This study quantifies and compares the nanoscale structural and mechanical properties of cellulose microfibrils such as microfibril dimensions, crossover count and angles, roughness, and Youngs modulus for two popular cotton species: Gossypium hirsutum (Gh) and Gossypium barbadense (Gb) fibers across four growth stages (8, 12, 18, and 22 days post-anthesis) using atomic force microscopy (AFM). Our results revealed for the first time that Gb fibers exhibit a better alignment, finer dimensions, and higher stiffness compared to Gh fibers at nanoscale, resulting in smoother fiber surfaces, and improved quality at macroscale. We are also the first to develop machine-learning models to predict macroscale phenotypic traits specifically boll length and cellulose content using nanoscale features alone and in combination with multi-omics modalities, substantially enhancing the predictive accuracy and highlighting opportunities for robust cross-species modeling of cotton fiber traits.

We sought to discover universal organizing principles behind phenotypic variation within cell types. Pareto optimality describes how trade-offs between optimal solutions account for variation, predicting that the boundary points of a data distribution reflect specialized functions. We hypothesized that Pareto optimality dominates transcriptomic variation across all cell types. We used the Tabula Sapiens atlas of single-cell RNA sequencing across cell types and tissues in the human body to test this hypothesis and discovered that most cell types adhere to this theory. This enabled us to use this principled method to characterize the functions performed by each cell type. These phenotypes are derived from an unbiased approach and do not incorporate ideas from existing biological models or theories, and yet in many cases they recapitulate our understanding of the functions of major cell types. Ultimately, we conclude that multi-objective optimization broadly shapes the observed phenotypic variation within cell types. This finding enables us to write explicit representations of the low-dimensional manifolds on which transcriptomes of single cells reside. This can inform the design of the next generation of virtual cell language models, which aim to statistically learn low-dimensional transcriptomic manifolds.

Despite the growing prevalence of non-healing diabetic wounds, no current treatment options overcome multifactorial deficits in repair. To this end, a mesenchymal stromal cell-derived regenerative extracellular matrix (rECM) was evaluated for the ability to accelerate cutaneous wound repair in leptin receptor-deficient (db/db) diabetic mice with paired full-thickness dorsal skin defects. A single dose of rECM significantly accelerated wound closure compared with vehicle controls. Also, rECM dose-dependently improved overall histological healing scores and modulated granulation tissue dynamics, with the highest dose promoting rapid resolution of granulation tissue relative to wound area. Spatial transcriptomics and immunofluorescence revealed that rECM drove robust formation of de novo peripheral nerve clusters characterized by the Schwann cell marker, p75. The rECM also enhanced vascular maturation in healed wounds, increasing average blood vessel size, smooth muscle actin-positive vessels, and vessel density within myofibroblast-rich regions. In a complementary 3D angiogenic sprouting model, rECM accelerated endothelial invasion and filopodia extension, and at higher concentrations induced contraction of collagen matrices consistent with accelerated resolution of granulation tissue. These data demonstrate that rECM accelerates closure of diabetic skin defects by coordinating faster granulation tissue remodeling with enhanced peripheral nerve formation and vascular maturation.

We here address the causal relationship between maternal depression and child human capital using UK cohort data. We exploit the conditionally-exogenous variation in mothers genomes in an instrumental-variable approach, and describe the conditions under which mothers genetic variants can be used as valid instruments. An additional episode of maternal depression between the childs birth up to age nine reduces both their cognitive and non-cognitive skills by 20 to 45% of a SD throughout adolescence. Our results are robust to a battery of sensitivity tests addressing, among others, concerns about pleiotropy and the maternal transmission of genes to her child.

Papua New Guinea is home to >10% of the worlds languages and rich and varied biocultural knowledge, but the future of this diversity remains unclear. We measured language skills of 6,190 students speaking 392 languages (5.5% of the global total) and modelled their future trends, using individual-level variables characterizing family language use, socio-economic conditions, students skills, and language traits. This approach showed that only 58% of the students, compared to 91% of their parents, were fluent in indigenous languages, while the trends in key drivers of language skills (language use at home, proportion of mixed-language families, urbanization, students traditional skills) predicted accelerating decline of fluency, to an estimated 26% in the next generation of students. Ethnobiological knowledge declined in close parallel with language skills. Varied medicinal plant uses known to the students speaking indigenous languages are replaced by a few, mostly non-native species for the students speaking English or Tok Pisin, the national lingua franca. Most (88%) students want to teach indigenous language to their children. While crucial for keeping languages alive, this intention faces powerful external pressures as key factors (education, cash economy, road networks, urbanization) associated with language attrition are valued in contemporary society. Significance StatementAround the world, more than 7,000 languages are spoken, most of them by small populations of speakers in the tropics. Globalization puts small languages at a disadvantage, but our understanding of the drivers and rate of language loss remains incomplete. When we tested key factors causing language attrition among Papua New Guinean students speaking 392 different indigenous languages, we found an unexpectedly rapid decline in their language skills compared to their parents and predicted further acceleration of language loss in the next generation. Language attrition was accompanied by decline in the traditional knowledge of nature among the students, pointing to an uncertain future for languages and biocultural knowledge in the most linguistically diverse place on Earth.

We develop tools to explore and catalogue the topologies of knotted or pseudoknotted circular folds due to secondary and tertiary interactions within a closed loop of RNA which generate multiple double-helices due (for example) to strand complementarity. The fold topology is captured by a contracted fold which merges helices separated by bulges and removes hairpin loops. Contracted folds are either trivial or pseudoknotted. Strand folding is characterised by a rigid-vertex polarised strand graph, whose vertices correspond to double-helices and edges correspond to strands joining those helices. Each vertex has a plumbline whose polarisation direction defines the helical axis. That polarised graph has a corresponding circular ribbon diagram and canonical alphanumeric fold label. Key features of the fully-flagged fold are the arrangement of complementary domains along the strand, described by a numerical bare fold label, and a pair of binary flags: a parity flag that specifies the twist in each helix (even or odd half-twists), and an orientation flag that characterises each double-helix as parallel or antiparallel. A simple algorithm is presented to translate an arbitrary fold label into a polarised strand graph. Any embedding of the graph in 3-space is an admissible fold geometry; the simplest embeddings minimise the number of edge-crossings in a planar graph drawing. If that number is zero, the fold lies in one of two classes: (a)-type relaxed folds, which contain conventional junctions and (b)-type folds whose junctions are described as meso-junctions in H. Wang and N.C. Seeman, Biochem, vol. 34, pp920-929. (c)-type folds induce polarised strand graphs with edge-crossings, regardless of the planar graph drawing. Canonical fold labelling allows us to sort and enumerate all semi-flagged folds with up to six contracted double-helices as windings around the edges of a graph-like fold skeleton, whose cyclomatic number - the fold genus - ranges from 0 - 3, resulting in a pair of duplexed strands along each skeletal edge. Those semi-flagged folds admit both even and odd double-helical twists. Appending specific parity flags to those semi-flagged folds gives fully-flagged (a)-type folds, which are also enumerated up to genus-3 cases. We focus on all-antiparallel folds, characteristic of conventional ssRNA and enumerate all distinct (a), (b) and (c)-type folds with up to five double-helices. Those circular folds lead to pseudoknotted folds for linear ssRNA strands. We describe all linear folds derived from (a) or (b)-type circular folds with up to four contracted double-helices, whose simplest cases correspond to so-called H, K and L pseudoknotted folds, detected in ssRNA. Fold knotting is explored in detail, via constructions of so-called antifolds and isomorphic folds. We also tabulate fold knottings for (a) and (b)-type folds whose embeddings minimise the number of edge-crossings and outline the procedure for (c)-type folds. The inverse construction - from a specific knot to a suitable nucleotide sequence - results in a hierarchy of knots. A number of specific alternating knots with up to 10 crossings emerge as favoured fold designs for ssRNA, since they are readily constructed as (a)-type all-antiparallel folds.

Widespread genetic testing for diseases may cause adverse selection, escalating premiums, or discrimination in various insurance markets. Here, without systematically informing study participants of their genetic predisposition, we estimate to what extent genetic data are informative about differences in longevity, health expectations, and economic behavior. We compute measures of genetic liability (polygenic scores) for 27 common diseases and mortality risks in 9,272 participants of the Health and Retirement Study (HRS). Survival analysis suggests that the highest decile of cumulative genetic risk can distinguish a median lifespan up to 4.5 years shorter, a difference that is similar to or larger than that distinguished by conventional actuarial risk factors, including sex. Furthermore, greater genetic liability is associated with less long-term care insurance, among other economic behaviors. We conclude that the rapid developments in genetic epidemiology pose new challenges for regulating consumer genetics and insurance markets, requiring urgent attention from policymakers.

Nitrogen is the most limiting nutrient to forest productivity worldwide. Recently, it has been established that diverse ecosystems source a substantial fraction of their water from weathered bedrock, leading to questions about whether root-driven nitrogen cycling extends into weathered bedrock as well. In this study, we specifically examined nitrogen dynamics using specialized instrumentation distributed across a 16 m weathered bedrock vadose zone (WBVZ) underlying an old growth forest in northern California where the rhizosphere--composed of plant roots and their associated microbiome--extends meters into rock. We documented total dissolved nitrogen (TDN), dissolved organic carbon (DOC), inorganic N (ammonium and nitrate) and CO2 and O2 gasses every 1.5 m to 16 m depth for two years. We found that biologically available nitrogen in the weathered bedrock rhizosphere was comparable in concentration to temperate forest soils and primarily organic. TDN concentrations in the WBVZ exhibited distinct patterns with depth and were correlated with periods of increased whole-ecosystem metabolic activity as well as stream discharge, suggesting competing rhizosphere and leaching processes in the fate of TDN in the WBVZ. Carbon isotope composition of the DOC suggests that dissolved organic matter in the WBVZ is primarily derived from fresh plant sources. We conclude that N cycling in the WBVZ is driven by an active rhizosphere meters below the base of soil and represents an important and overlooked component of deeply rooted ecosystems that must be incorporated into future models and theory of ecosystem function.

Nudibranchs are well known for their bright and diverse color patterns. This coloration is typically a form of aposematism, warning predators against toxic compounds sequestered from their prey and weaponized as a form of defense. Although many of the hues in nudibranchs have pigmentary origin, multilayer structures composed of guanine nano-platelets have been suggested as the source of color enhancement in the nudibranch Flabellina iodenea. Here, using a combination of white light and Raman microspectroscopy techniques, we report that such guanine-based multilayer structures are a widespread mechanism to create angular-independent structural color across the dorid and aeolid groups. Additionally, by using cryo-FIB tomography, we were able to access the complex 3D organization of the guanine nano-platelets responsible for the strong blue coloration of Chromodoris annae. We propose that the multilayer organization of guanine platelets with varying orientations across the tissue offers a particularly effective strategy for producing diverse optical effects. In this configuration, hue is mainly governed by interlayer spacing, while the angular dependence of color can be tuned through the degree of local order, allowing a single structural motif to generate a broad palette of optical appearances. Significance statementNudibranchs are an extraordinarily diverse group of marine animals, renowned for their dazzling range of colors and striking patterns. Whilst their pigmentary coloration is well understood, so far, structural coloration, obtained only by nanostructures, has only been reported in the nudibranch Flabellina iodenea. In this work, we present a comparative analysis of structural coloration across nudibranch species from both benthic and coral reef environments, and we show that guanine-based nano-structures are a common motif responsible for a wide range of colors, spanning the dorid and aeolid groups. We foresee that the 3D imaging conducted here may serve as inspiration for bio-photonics studies in other marine organisms, and that the structures themselves could serve as inspiration for bio-inspired materials.

Epithelial cell collectives migrate through tissue interfaces and crevices to orchestrate processes of development, tumor invasion, and wound healing. Naturally, traversal of cell collective through confining environments involves crowding due to the narrowing space, which seems tenuous given the conventional inverse relationship between cell density and migration. However, physical transitions required to overcome such epithelial densification for migration across confinements remain unclear. Here, in contiguous microchannels, we show that epithelial (MCF10A) monolayers accumulate higher cell density before entering narrower channels; however, overexpression of breast cancer oncogene +ErbB2 reduced this need for density accumulation across confinement. While wildtype MCF10A cells migrated faster in narrow channels, this confinement sensitivity reduced after +ErbB2 mutation or with constitutively-active RhoA. The migrating collective developed pressure differentials upon encountering microchannels, like fluid flow into narrowing spaces, and this pressure dropped with their continued migration. These transitions of pressure and density altered cell shapes and increased effective temperature, estimated by treating cells as granular thermodynamic system. While +RhoA cells and those in confined regions were effectively warmer, cancer-like +ErbB2 cells remained cooler. Epithelial reinforcement by metformin treatment increased density and temperature differentials across confinement, indicating that higher cell cohesion could reduce unjamming. Our results provide experimental evidence for previously proposed theories of inverse relationship between density and motility-related effective temperature. Indeed, we show across cell lines that confinement increases pressure and effective temperature, which enable migration by reducing density. This physical interpretation of collective cell migration as granular matter could advance our understanding of complex living systems.

The sense of smell remains poorly understood, especially in contrast to visual and auditory coding. At the core of our sense of smell is the olfactory information flow, in which odorant molecules activate a subset of our olfactory receptors and combinations of unique receptor activations code for unique odors. Understanding this relationship is crucial for unraveling the mysteries of human olfaction and its potential therapeutic applications. Despite this, predicting molecule-OR interactions remains incredibly difficult. Here, we develop a novel, biologically-inspired approach that first maps odorant molecules to their respective OR activation profiles and subsequently predicts their odor percepts. Despite a lack of overlap between molecules with OR activation data and percept annotations, our joint model improves percept prediction by leveraging the OR activation profile of each odorant as auxiliary features in predicting its percepts. We extend this cross receptor-percept approach, showing that sets of molecules with very different structures but similar percepts, a common challenge for chemosensory prediction, have similar predicted OR activation profiles. Lastly, we further probe the odorant-OR models predictive ability, showing it can distinguish binding patterns across unique OR families, as well as between protein-coding genes or frequently occuring pseudogenes in the human olfactory subgenome. This work may aid in the potential discovery of novel odorant ligands targeting functions of orphan ORs, and in further characterizing the relationship between chemical structures and percepts. In doing so, we hope to advance our understanding of olfactory perception and the design of new odorants with desired perceptual qualities.

Bacterias ability to withstand mechanical challenges is enhanced in their biofilm lifestyle, where they are encased in a viscoelastic polymer matrix [1]. Under fluid flow, biofilms can form as streamers - slender filaments tethered to solid surfaces and suspended in the flowing fluid [2, 3]. Streamers thrive in environments subjected to intense hydrodynamic stresses, such as medical devices and water filters, often resulting in catastrophic clogging [4]. Their colonisation success may depend on a highly adaptable mechanical response to varying stress conditions, though the evidence and underlying mechanisms of this adaptation remain elusive. Here, we demonstrate that biofilm streamers exhibit a stress-hardening behaviour, with both differential elastic modulus and effective viscosity increasing linearly with external stress. This stress-hardening is consistent across biofilms with different matrix compositions, formed by various bacterial species, and under diverse growth conditions. We further demonstrate that this mechanical response originates from the properties of extracellular DNA (eDNA) molecules [5], which constitute the structural backbone of the streamers. In addition, our results identify extracellular RNA (eRNA) as a modulator of the matrix network, contributing to both the structure and rheological properties of the eDNA backbone. Our findings reveal an instantaneous, purely physical mechanism enabling streamers to adapt to hydrodynamic stresses. Given the ubiquity of extracellular nucleic acids (eNA) in biofilms [1, 6], this discovery prompts a re-evaluation of their functional role in biofilm mechanics, with potential implications for biofilm structural integrity, ecological resilience, and colonisation dynamics.

Syntax is an aspect of human language responsible for the hierarchical ordering of linguistic structures. Syntax can be summarized by dependency trees with words as nodes and edges reflecting syntactic subordination. By merging trees from several sentences, we obtain syntax graphs or networks, which display distinct shapes depending on whether the language capability is well-formed, still developing, or pathological. Such graphs make syntactic capacity quantifiable at a systemic level, revealing emerging patterns and universalities. What is the structure of syntax networks during ontogeny in typically developing (TD) children? Do cognitively challenged children develop language through alternative routes? Here we quantify and portray the typical development of syntax networks in Dutch, and find that children affected by Down syndrome, hearing impairment, and specific language impairment initially seem to follow the typical developmental path but eventually halt, culminating in a different linguistic phenotype. Our expanded data set (with almost 50 times more data than earlier studies) and increased mathematical dimensions to quantify network shape enable us to: (i) confirm and refine a proposed sharp transition in language development, (ii) correlate specific network traits with syntax maturation, and (iii) quantify the aspects that fall short in atypical development--suggesting potential diagnostic tools. We also find grounds to hypothesize a gap in syntax maturation, separating challenged children who nevertheless reach the latest stage from others systematically stuck, regardless of their condition. Our quantitative analysis enables a rigorous visualization of linguistic development trajectories, an old (yet mostly qualitative) theme in linguistics. Similar works should allow to test and propose specific hypotheses on solid grounds, as we do here. Future efforts should generalize to other languages and/or clinical conditions, seeking patterns that might point at universalities in language development.

In response to rising nitrate levels in Iowas public drinking water, the city of Des Moines built the worlds largest nitrate removal facility. The facility operates when nitrate levels exceed the regulatory threshold of 10 mg/L, incurring costs exceeding $10,000 per day. To evaluate the effect of the nitrate removal facility on birth weight and gestational age, we analyzed publicly available birth certificate microdata (1992-2004). In addition to adjusting for maternal and fraternal sociodemographic factors, our linear regression model included county-year fixed-effects to adjust for annual differences across counties and year-month fixed-effects to account for seasonal variation in birth outcomes. Unbiased identification relied on exogenous geotemporal variation in first-trimester exposure to operation of the nitrate removal facility. Operating the nitrate removal facility was associated with a 0.4-percentage-point increase in the probability of normal birth weight and was associated with a 1.6-percentage-point increase in the probability of a normal term birth. The positive associations were largest among mothers with higher risk of adverse outcomes (smoked during pregnancy, cesarean deliveries, prior preterm births, excessive weight gain, unmarried, unknown father). A post-hoc analysis suggests that this intervention may be cost effective, multiplying public investment by 3.5 dollars in health benefits. As policymakers explore how to address rising nitrate pollution, quantifying the value of nitrate reduction strategies could inform future public health interventions. Although the Des Moines nitrate removal facility appears to serve a crucial public health function and advance health equity, challenges remain for deciding how to pay for this potentially cost-effective intervention.

Staphylococcus aureus is a pathogenic bacterium that has caused multiple epidemics linked with the emergence of new antibiotic resistance. Vancomycin is the first-line antibiotic to treat methicillin-resistant S. aureus (MRSA) infections. However, several types of vancomycin-non-susceptible MRSA strains have been isolated from patients to date. Rapid assessment of their resistance levels and underlying molecular profiles is crucial for preventing their spread and counteracting resistance; however, the broad resistance spectrum and the diversity of genetic changes have impeded this practice. Here, we demonstrate that the vancomycin resistance levels of various MRSA strains can be determined using dimension-reduced Raman spectra obtained from single cells. The transcriptome profiles of the different strains can also be predicted from their dimensionally reduced Raman spectra by simple linear regression. This Raman-transcriptome correspondence allows us to map the transcriptome components onto dimension-reduced Raman space and characterize groups of genes associated with different phenotypes. Furthermore, single-cell Raman spectra predicted a cell strain with greater phenotypic heterogeneity, which was confirmed by single-cell growth analysis. Overall, our results demonstrate the efficacy of Raman spectroscopy in identifying resistant phenotypes, their associated gene expression features, and intrapopulation phenotypic heterogeneity.

BackgroundPregnancy requires finely tuned immune changes that support implantation, placental development, maternal-fetal tolerance, and preparation for labor, yet the normative trajectories of circulating inflammatory proteins across gestation remain poorly defined. This cross-sectional study investigates how circulating inflammatory proteins vary with gestational age in pregnancy and examines the impacts of fundamental biological characteristics, such as gravidity and fetal sex. MethodsData were drawn from 1154 pregnant individuals from six study sites of the National Institutes of Health Environmental influences on Child Health Outcomes (ECHO) Cohort. We used Olink high-throughput proteomic profiling to map cross-sectional associations between protein expression levels and gestational age at blood draw using linear, spline-based, and generalized additive modeling approaches. ResultsGeneralized additive models provided the best fit, revealing that immune changes across pregnancy were predominantly nonlinear. Sixty-one proteins showed significant associations with gestational age, with many exhibiting shared inflection points that aligned with major physiological transitions. A small subset of proteins also showed evidence of modification by fetal and maternal characteristics. CD244 displayed different gestational patterns by fetal sex, while CST5 and SIRT2 showed varied gestational associations by maternal gravidity. ConclusionThe findings highlight pregnancy as a sequence of coordinated immune transitions rather than a simple linear shift and provide one of the most detailed characterizations to date of circulating inflammatory protein dynamics across human gestation. Establishing these normative trajectories offers a crucial reference for detecting early deviations that may signal risk for pregnancy complications and for identifying biomarkers in maternal and fetal health research.

The gut microbiome is a rich source of nutrients that are critical to the development and biology of eukaryotes. Transfer RNAs (tRNAs) are essential components of protein synthesis, and some chemical modifications to tRNA rely on the availability of microbiome-derived nutrients. In eukaryotes, the micronutrient queuosine (Q) is salvaged from the microbiome or diet and then incorporated into eukaryotic tRNA to influence the speed and efficiency of protein synthesis. Here, we examine the role of microbiome-derived Q in mosquito larval development and behavior. When mosquito larvae are grown with a microbiome incapable of synthesizing Q, there is a significant impact on tyrosine levels and processes, which correlate with defects in behavior and cuticle formation. Due to defects in movement and behavioral responses, Q-deficient larvae demonstrate impaired predator evasion, leading to higher instances of capture by predaceous beetle larvae. The broad effects of Q-deficiency in mosquito larvae highlight the importance of microbiome-derived nutrients for eukaryotic physiology and behavior.

ObjectivesTo determine who spends more energy over a lifetime on maintaining their reproductive potential: men or women? DesignAs a model and energetic equivalent, we set the mass of gametes supported over time from birth until exhaustion of fertility. We calculated gender-specific dynamics of gamete pool mass over time. To this purpose we collated data from existing literature, accounting for gamete volume over stages of development, time in each stage, mass density, and count. Our model generates the integral, or area under the curve (AUC) of the gamete pool mass over a lifetime as a proxy to energetic requirements. Main outcome measuresThe area under gamete mass curve over a lifetime in men and women. ResultsThe number of gametes over a lifetime is 600,000 in women and close to 1 trillion in men. Accounting for mass and time, women invest approximately 100 gram*days in maintaining the female oocyte pool. Women reach 50% of lifetime AUC by age 10, and 90% by age 25. Men invest approximately 30 Kg*days over a lifetime (300-fold more), reaching 50% of lifetime AUC at age 37 and 90% at age 62 years old. ConclusionsThe study quantifies for the first time the area under gamete mass in men and women through a nuanced calculation accounting for all components of post-natal gamete dynamics. We found a 300-fold excess is supported male gamete mass over a lifetime (100g*days vs. 30 Kg*days in females vs. males, respectively). Our methodology offers a framework for assessing other components of the reproductive system in a similar quantitative manner.

Incidence of type 2 diabetes is increasing at ages when education, work, family, and financial transitions are taking place, yet we lack robust evidence of whether earlier treatment changes life-course outcomes and over which time span this takes place. This paper uses the medical cutoff for diabetes diagnosis (HbA1c of 6.5 percent) as a natural experiment to study the effects of diabetes treatment using electronic health records (EHR) and panel data. This paper has three main findings. First, using EHR data, we find that there is a sharp increase in the probability of both diagnosis of diabetes and prescription when the HbA1c equals 6.5 percent. Second, we find that treating diabetes reduces HbA1c levels, weight, BMI, and blood pressure and increases the amount of care received, proxied by the number of HbA1c tests. Both the diagnosis and a prescription are independently able to produce positive changes in metabolic health, although a prescription is more effective in this regard. Third, we conclude that treating diabetes does not have a significant effect on life-course outcomes for a cohort of young Americans aged 24-32, although it does result in a reduction in HbA1c levels that are seen even eight years after the intervention. Taken together, these findings suggest that receiving a diagnosis and prescription are both effective treatments for diabetes, but they do not translate to significant alterations in the lives of young adults in the medium-term.