Drosophila glia is a significant determinant of sleep amount and occurs preferentially during sleep in glia of the blood-brain barrier (BBB). To identify metabolites whose trafficking is mediated by sleep-dependent endocytosis, we conducted metabolomic analysis of flies that have increased sleep due to a block in glial endocytosis. We report that acylcarnitines, fatty acids conjugated to carnitine to promote their transport, accumulate in heads of these animals. In parallel, to identify transporters and receptors whose loss contributes to the sleep phenotype caused by blocked endocytosis, we screened genes enriched in barrier glia for effects on sleep. We find that knockdown of lipid transporters LRP1&2 or of carnitine transporters ORCT1&2 increases sleep. In support of the idea that the block in endocytosis affects trafficking through specific transporters, knockdown of LRP or ORCT transporters also increases acylcarnitines in heads. We propose that lipid species, such as acylcarnitines, are trafficked through the BBB via sleep-dependent endocytosis, and their accumulation reflects an increased need for sleep.Endocytosis through  Drosophila (Drosophila blood/hemolymph-brain barrier (BBB) emerged as a new cellular locus of sleep regulation in this study, such that genetically manipulating endocytosis in these cells alone was sufficient to increase sleep. As the increased sleep appeared to reflect higher sleep need, we asked if sleep enhances endocytosis through the BBB and found that this was indeed the case. However, the nature of the molecules trafficked through the BBB in a sleep-dependent manner was not known.Although the regulation of sleep is normally studied on a behavioral and circuit level, there is increasing evidence for a role of basic cellular physiology. For instance, we found that disruption of endocytic and trafficking pathways in glia increases sleep in osophila . Glia ofDrosophila barrier glia populations share many conserved features with vertebrate barriers that employ endothelial and astrocytic populations , a dominant negative dynamin that blocks endocytosis, in all or BBB glia increases sleep. To attain an unbiased, global assessment of metabolites that may be relevant to the increased sleep seen in 1Repo-GAL4>20xShi.ts flies (hereafter referred to as 1Repo>Shi), we conducted LC-MS analysis. Heads of male and female 1Repo>Shi flies as well as GAL4 and UAS controls were collected on dry ice with sieves and immediately frozen at –80°C. Each sample contained 200 fly heads , with five samples per genetic condition.Our previous work showed that expression of As an initial analysis, raw signal was scaled per each metabolite in reference to other samples within the dataset, and comparisons were made between Repo >Shi flies and each control, as well as controls to each other by Welch’s t-test .Metabolites of interest were those for which signal from the experimental samples was significantly different, in the same direction, when compared to both controls, while controls compared to each were not significant. Of secondary interest were metabolites where a difference was seen in controls, but was proportionally smaller than consistent differences of each control to the experimental samples.1Repo>Shi flies to each of the parental controls for metabolites that had signal in at least three of five biological replicates for each genotype. Expression of 1Shi in glia increased abundance in fly heads of the following acylcarnitine species: C2, C16, C16:1, C17, C18:1, C18:2 , with a total of five samples per genetic condition. Expression of 1Shi in all glia increased abundance in fly bodies of the following acylcarnitine species: C4 butyryl, C4 isobutyryl, C4-OH butyryl, C4-OH isobutyryl, C12-OH, C14:1-OH, C14-OH,16:1-OH, C16-OH — to all neurons, and all glia , CG6386 (108502 KK), CG4462 (105566 KK), or lsd-2 (102269 KK) did not significantly alter sleep when expressed in either of the barrier glial populations (data not shown). Reduction of cyp6a20 in the PG population inconsistently reproduced the pan-glial sleep loss phenotype, so this was not pursued further (data not shown). The VMAT gene produces two isoforms, one of which is thought to be specific to glia (VMAT (TRiP HMC02346) in the PG increased total sleep , which is consistent with protein expression, as the VMAT-B antibody specifically marks the PG  are two LDL receptor-related protein members involved in the transport of lipid carrier proteins at the fly barrier  individually in the pan-glial screen did not significantly alter total sleep time  have been considered to be complementary   , but not or Rab9 . As with4 driver . Meanwhi4 driver . Also, acontrols .Lrp1 and Megalin in barrier glia at the adult stage and thereby avoid developmental confounds, we coupled drivers specific to these glia with the temperature-sensitive tubulin-Gal80  system that suppresses GAL4 expression at 18°C but allows it at 31°C. In these experiments, to verify their role in sleep, we sought to use multiple RNAi lines for these transporters and were able to obtain three lines for Megalin ; however, for Lrp1 only one line (8397 GD) had a chromosomal location that facilitated generation of double knockdowns. We combined each of the Megalin RNAi transgenes with the Lrp RNAi transgene and crossed them with the 9–137-GAL4 driver, which expresses in both PG and SPG  together with knockdown of Megalin, using either 105071 KK or Megalin 36389 KK, increased total sleep significantly in a temperature-dependent fashion, indicating an adult-specific role of these transporters in barrier glia (Lrp1 (8397 GD) with Megalin 27242 GD did not alter sleep  transporters are multi-substrate transporters whose substrates include carnitine , Orct and Orct2 are considered to be complementary  with Orct2 106681 KK RNAi and expressing them in adults using the 9–137-GAL4 driver with tstub-Gal80 significantly increased sleep during the day and night . Knockdown of Orct and Orct2 similarly enriched acylcarnitines in fly heads. In particular, acylcarnitine species C16, C16:1, and C20 were increased significantly , 2 , and 3 . Acylcarnitine Mix 1 contains 10 mg/mL of C0 and C2 in methanol, of which C2 was increased in heads of Repo>1Shi flies and of short-sleeping mutants fmn, rye, and sssP1  of experimental iso31 flies and similar concentrations of methanol or methanol and aqueous formic acid were added to the food of controls. Acylcarnitine Mix 1 was tested at five different concentrations of 2.5 mg/L, 5 mg/L, 10 mg/L, 25 mg/L, and 50 mg/L. As the LC-MS analysis indicated that the concentration of C2 is approximately 100–1000 times higher than that of the medium or long chain acylcarnitines, acylcarnitine Mix 2 and 3 were tested at concentrations of 0.025 mg/L, 0.05 mg/L, 0.10 mg/L, 0.25 mg/L, and 0.5 mg/L —in flies fed Mix 2 and 3 together  and 2. IOur previous work suggested that endocytosis through the BBB is a function of sleep, but the nature of the molecules trafficked remained unknown. Using a dual-pronged approach of a targeted genetic screen and unbiased metabolomic analysis, we report here that the passage of lipids through the BBB is important for sleep. Blocking such transport increases sleep in conjunction with an accumulation of acylcarnitines.VMAT isolated in the initial screen. The lipid and carnitine transporters we subsequently focused on also produced phenotypes through knockdown in a single barrier layer, although we also tested effects of knockdown in both layers. While there was redundancy at the level of the transporters, the two barrier layers did not compensate for each other.Through a pan-glial RNAi knockdown screen of candidate genes expressed in the fly barrier, we identified molecules that affect daily sleep amount. In initial follow-up experiments, we targeted knockdown to each barrier layer separately, which is subject to the concern that behavioral phenotypes requiring simultaneous knockdown in both barrier populations would be missed. Nevertheless, we consider this scenario to be less probable, as permeability through the populations is quite different, with smaller solutes likely passing through the PG but not the tight barrier of the SPG, lipophilic solutes, or xenobiotics potentially passing through each uninhibited, and larger solute requiring endocytic mechanisms that likely have to work in each population in tandem. Therefore, in most cases of knockdown, transport would either be inhibited by the one barrier population essential for it or would be interrupted by either population. This was indeed the case for the An additional consideration of preliminarily screening with pan-glial drivers is that if knockdown in multiple glial subtypes has opposing effects on sleep, we may have obscured a role for the barrier cells. Again, we attempted to minimize this risk by primarily selecting genes whose expression is both highly abundant and specifically enriched in the barrier populations, as opposed to the transcriptome dataset of all glial cells . The assVMAT) has previously been identified as a target of reserpine, which promotes sleep in the fly  increased sleep need, and so may be associated with high acylcarnitines at all times. Thus, acylcarnitines are generally associated with sleepiness rather than sleep. While this does not necessarily mean that they promote sleep, we show here that feeding flies low concentrations of medium and long chain acylcarnitines modestly increases sleep, although the fate of these acylcarnitines or where they act to affect sleep is not known. High concentrations have no effect, either due to some feedback mechanism or perhaps because high levels of the methanol solvent inhibit feeding. Regardless of their role in sleep regulation, we suggest that acylcarnitines are a key marker of sleep need across species, and could be exploited for this purpose. Sleep is likely required to reduce levels of acylcarnitines, perhaps as part of a broader role in metabolic homeostasis.Acylcarnitine accumulation in flies with blocked glial endocytosis or lipid transport could occur as a consequence of the high sleep in these animals. We believe this is unlikely as an increase in acylcarnitines appears to generally occur under conditions of sleep deprivation, that is conditions that would promote sleep. Carnitine conjugation of long chain fatty acids was reported in cortical metabolites of sleep-deprived mice, while short and medium chain fatty acids were reduced . ChangesRepo-GAL4/TM6c, Sb and UAS-Dicer; RepoGeneSwitch. SPG driver Moody-GAL4 and surface driver 9–137-GAL4 were shared by Roland Bainton, while PG driver NP6293-GAL4 was a gift of Marc Freeman.;;UAS-20xShi.ts1 (referred to as UAS1-20xShi) was shared by Gerald Rubin. RNAi lines were ordered from VDRC (KK and GD collection) and Bloomington (TRiP collection) stock centers, with the stock number provided in the below table.The initial screen was performed with lab stock drivers:;;Drosophila Activity Monitor (DAM) system, using the established definition of a minimum 5 min of inactivity. Data was analyzed in PySolo . The RNAs were resuspended in nuclease-free water about 300 ng/μL. cDNA libraries of RNAi fly lines and control iso31 were synthesized by using the High Capacity cDNA Reverse Transcription Kit and its protocol . cDNAs from Lrps and Orcts RNAi and iso31 were amplified using SYBR Green PCR mix  on an Applied Biosystems ViiA7 qPCR machine. Primers are listed below. We calculated relative transcript levels by ddCT.To determine the knockdown efficiency of target transcripts with , respectively. For feeding of acylcarnitine mixtures of Mix 2 and Mix 3, each of Mix 2 and Mix 3 was added at a concentration of 0.0125 mg/L, 0.025 mg/L, 0.05 mg/L, 0.10 mg/L, 0.25 mg/L, and 0.5 mg/L, as done for separate feeding of Mix 2 or Mix 3. For the sleep assay, ~7-day-old iso31 flies were housed individually in glass tubes containing acylcarnitine or methanol or methanol and aqueous formic acid control food in Percival incubators. As above, sleep was determined based upon activity recorded with the Trikinetics DAM system (http://www.trikinetics.com/). All flies were maintained in a 12 hr:12 hr light:dark cycle and behavior was monitored for 5 days, of which the last 3 were analyzed for sleep. Pysolo (http://www.pysolo.net) . These acylcarnitine mixtures, which used methanol or methanol plus formic acid as solvents, were added individually into the fly food (5% sugar and 1.8% agar) and the same concentrations of methanol or methanol and aqueous formic acid were added into control food. Iso31 flies were fed five concentrations of acylcarnitine Mix 1—2.5 mg/L, 5 mg/L, 10 mg/L, 25 mg/L, and 50 mg/L. Mix 2 or Mix 3 was added into food at concentrations of 0.025 mg/L, 0.05 mg/L, 0.10 mg/L, 0.25 mg/L, and 0.5 mg/Lolo.net)  was usedFor both behavioral and acylcarnitine metabolomics results, the experimental group was compared to two parental controls by one-way ANOVA with Holm-Sidak post hoc tests (in addition to specific tests mentioned in figure legends) with GraphPad Prism software. For the initial comparisons of metabolomic data, Metabolon performed Welch’s t-tests  on scale Drosophila blood-brain barrier play important roles in the regulation of sleep. The data are consistent with metabolite clearance, in particular, the sleep-dependent transport of acyl-carnitine across the BBB, being an important function of sleep.Through a convincing series of in vivo experiments, this work demonstrates that lipid- and carnitine transporters in glial cells of the  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]Drosophila blood brain barrier" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting the paper "Modulation of sleep by trafficking of lipids through the Comments to the Authors:eLife. However, we would be willing to consider an extensively revised manuscript if you choose to address the reviewers' concerns and resubmit to eLife as a new submission.We are sorry that, after consultation with the reviewers, we have decided that this work cannot be considered further for publication by Specifically, as you will see from the comments below, while all of the reviewers found the question and approach of this study to be very interesting, all felt that several additional experiments including routine controls and procedural enhancements as well as careful rewriting would be required. Particularly critical is to attempt experiments to establish causality of acylcarnitine build up and sleep regulation (e.g through acylcarnitine microinjection?). A subset of other issues raised in the reviewer discussions included the need for: (1) more complete description of used methods used; (2) description of all drivers as well as transgenic constructs used, with references as well as validation of driver expression via UAS-GFP if not previously described . (3) validation of RNAi efficacy via qRT (repo>RNAi and head or brain mRNA levels); (4) ideally, key experiments to employ more than one RNAi transgene to ensure that effects are not due to off-target effects, while acknowledging that sometimes these genetics are difficult (e.g. with TRiP lines).Reviewer #1 (Recommendations for the authors):Drosophila. In previous studies, the Sehgal lab found that disrupting endocytosis in glia of the fly blood-brain barrier is sufficient to increase sleep. Here, the authors sought to explore the mechanism(s) behind this phenomenon by measuring metabolites in the heads of Repo>shi1 flies. LC-MS found elevations in a variety of acylcarnitines in the heads of long-sleeping Repo>shi1 flies, then behavioral experiments found that RNAi-mediated knock-down of two lipid-transport related genes  or two carnitine transporters (Orct and Orct2) are sufficient to significantly increase sleep time. Finally, LC-MS measurements in each long-sleeping RNAi-knock down genotype  also showed increased levels of acylcarnitine species. Together, these studies nicely parallel findings of heightened acylcarnitine levels in the blood of sleep-deprived humans, indicating that the fly provides a tractable model for understanding the mechanistic link between fatty acid metabolism and sleep.Artiushin et al. use a combination of metabolomics and behavioral genetics to detail the relationship between sleepiness and lipid transport in While the authors' interpretations are consistent with their findings, the addition of a few key experiments, new analyses of behavioral data, and text edits could strengthen the manuscript.Main comments:The authors find sleep effects of manipulating fatty acid transport-related pathways in BBB glia, but it is not clear to me whether the acylcarnitines are moving into the brain or out of it. These possibilities each indicate different (and interesting) possibilities for sleep regulation and/or function. Along similar lines, the authors could test whether acylcarnitine levels are increased in the bodies of sleep deprived flies – this finding might help to distinguish whether these are sleep-promoting signals generated in the periphery or a metabolite that needs to be cleared from the brain.If the sample material for LC-MS experiments came from whole heads, then it is possible that the head fat bodies contributed to the fatty acid changes shown in Figures 1, 5 & 6. The authors should mention and discuss this possibility in the manuscript.Do the sleep changes in Figures2-4 alter sleep during the day or night (or both)? Do these RNAis alter sleep architecture or waking locomotor activity?The authors use Gal80TS to restrict the timing of Mgl/Lrp1 RNAi  and Orct/Orct2 RNAi . A clearer description of the temperature protocols should be included. Do the 18°C and 31°C represent different groups of flies that were tested in parallel? Or do they show the same flies before and after a temperature increase? In addition, the flies housed at 18°C (when RNAi expression should be blocked) show similar behavioral phenotypes as the 31°C condition and as flies expressing the same RNAis without Gal80TS . These data indicate either that the Gal80 repressor is not effectively blocking RNAi expression or that the sleep effects observed here cannot be solely attributed to the RNAi knock-downs.Does supplementing fly food with acylcarnitines alter sleep? If so, are flies expressing Mgl/Lrp RNAi or Orct/Orct2 RNAi in perineural glia sensitized to dietary acylcarnitines?Do all error bars indicate SEM as listed in the legends? Several of the behavioral plots appear to show overlapping error bars across comparisons that are designated as statistically significant. See Figures 3B, 3C, 4B, 4C and Figure 3 – —figure supplement 1.Reviewer #2 (Recommendations for the authors):Here, Artiushin et al. perform metabolomics on the heads transgenic mutants that exhibit long sleep due to inhibition of glial endocytosis (via repo driving shibire) compared to genetic controls. They identified multiple changes in metabolites; the most consistent and significant change was increased acylcarnitine levels. The authors performed a candidate screen by driving RNAi-mediated inhibition of 50 glia-enriched genes with pan-glial or sub-glial expression drivers. Knockdown of lipid transporter proteins in barrier glia led to increased acylcarnitines and increased sleep, suggesting that increased acylcarnitine levels may increase sleep. Previous results had shown that barrier glia and endocytosis were involved in sleep regulation; these data support the hypothesis that lipid metabolites, transported by barrier glia into the brain, mediate sleep regulation. Specifically, they point to the hypothesis that increased acylcarnitine levels in the brain are correlated with induction of sleep.The conclusions of this paper are mostly well supported but some specific aspects need to be clarified or tested.1) The n are low for several behavioral experiments and the number of trials is not clear for any experiments. Sleep and behavior are notoriously inconsistent from trial to trial. Experiments should be repeated with 16-32 flies per trial with 3 trials per experiment to demonstrate robustness of results.2) As negative controls for subglial drivers that drive RNAi expression and affect sleep behavior, the authors should also test a non-barrier sub-glial expression driver such as eye glial drivers for effects on sleep behavior.3) Are there other methods of increasing acylcarnitine levels in the body or brain that will increase sleep?4) What are acylcarnitine levels for short-sleeping mutants?This reviewer appreciates all the data and statistics listed for metabolomics. These data will be invaluable for other researchers in the field. Nevertheless, the precise n and trial number for each experiment should be listed.Reviewer #3 (Recommendations for the authors):Drosophila. In this new report, they document changes in metabolites that occurs when the fly BBB is disrupted, with acylcarnitines accumulating in particular. Finally, using RNAi, they show that disruption of candidate transporters in the BBB alter sleep.This manuscript is a follow up to a previous publication from the group that linked blood-brain-barrier function to sleep in Overall, the manuscript provides promising data with the potential to be very interesting; however, the current version of this manuscript is hastily assembled. For example, the introduction refers somewhat vaguely to this group's previous paper, of which the current work is an extension, without really giving the reader much context of the state of the field. Although familiar with that previous paper, I had to refer back to it at several points when reading the current document to make full sense of the current manuscript. Particularly poor are the documentation of the methods; an expert in the field would not be able to replicate the findings based on the methods. For just one example-unless I am mistaken (and I misread Flybase), the Shi.ts1 manipulation used to impair the BBB is a temperature sensitive allele, yet there is no mention of samples being placed in restrictive or permissive temperatures. They do an RNAi screen in the middle of this paper, but that screen and its methods are not described. Not even all the fly stocks that appear to be used in the manuscript appear listed in the Methods.More complete documentation of the experiments being performed would make it easier to properly evaluate the figures and conclusions.The paper needs a major rewrite of the methods. Other areas that need to be addressed:In Table 1 , many of the metabolites labelled as interesting appear to be upregulated to quite a degree when comparing the two control strains, which makes it hard to judge whether these metabolic changes are meaningful or not. They appear to use a t-test for each metabolite, which further confounds the interpretation-some kind of false discovery rate correction should be applied to such large lists of comparisons.On a related note, the manuscript could do a better job of guiding the reader to how the genetic manipulations are linked to the metabolite profile deemed important/different in the first figures. For example, comparing the results from Figure 5 and 6, it would appear that only C16 and C16.1 are showing differences that are found in Figure 1, but I could only find this upon careful examination myself when the authors should really do a better job to help us make sense of this data. Similarly, Figure 2's results don't seem in any way connected to the metabolite profiles, so I cannot understand why this data is included here.Other aspects that I found difficult to assess:– The RNAi screen, were more than one RNAi aimed at the same gene to confirm the effects?– The raw values for metabolites are labelled as "area under the curve" in Figure 1 (unclear what that means) and plotted as μM in Figure 5.– The n's for each experiment appear to be 13-16 per genotype. That sounds like a single tracking experiment to me. Were any of these experiments done as independent biological replicates (i.e. repeated on separate days?)Drosophila blood brain barrier" for further consideration by eLife. Your revised article has been evaluated by K VijayRaghavan (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Modulation of sleep by trafficking of lipids through the The reviewers have discussed their reviews with one another, and the Reviewing Editor requests you to consider responding appropriately to the remaining minor queries and comments.Reviewer #1 (Recommendations for the authors):Specific comments:1. Does Figure 1 represent data from the same experiments described in Table 1? It's not clear to me whether Figure 1 shows more detail from the same data points or represents additional replicates. Also, the legend for Figure 1 describes the use of T-tests for significance – these should be analyzed using ANOVA instead with post hoc comparisons for the significance of each fatty acid.2. The authors list the contents of each acylcarnitine type that is contained within the mixtures that are fed to flies, but a clearer discussion in the text that compares these lists with the lipids that were significantly changed in figure 1 might help the reader.3. Is sleep architecture changed in flies that sleep more with acylcarnitine feeding?Reviewer #2 (Recommendations for the authors):The authors have addressed all of my concerns from the first version.Reviewer #3 (Recommendations for the authors):The authors did great work in addressing the majority of prior reviewer comments, specifically by clarifying their Materials and methods and adding supplementary experiments that increase confidence in their findings. The authors find that many different types of gene manipulations in the blood-brain barrier are associated with both long sleep and high acylcarnitine accumulation in the brain; the number of these experiments strongly supports a relationship between these three events. Precisely how these different gene manipulations in the BBB lead to acylcarnitine accumulation in the brain and how/where/why acylcarnitine accumulation in the brain might cause long sleep (as opposed to resulting from long sleep) remain unclear. Thus, while the authors present sufficient and exciting evidence that sleep, BBB function, and acylcarnitines are related, due to the lack of a cohesive model or mechanism, the authors are right to be cautious in making more specific claims about the causal nature of these relationships.This manuscript was clearer and more well-written than the previous version. This reviewer did not go through with a fine-tooth comb to examine whether claims of causality were overstated; the authors should do this.I include here a few points that could be mentioned in the discussion, results, or methods sections as caveats or clarifying points:– Is there any evidence in the literature that acylcarnitines are removed from the brain via the BBB, as would be suggested by acylcarnitine accumulation after the loss of endocytosis or transporter function in BBB? Is this the authors' hypothesis?– The feeding experiments, though the effects were mild, go one step toward addressing the role of acylcarnitines in triggering sleep except that it's not clear if eating acylcarnitines leads to acylcarnitine accumulation in the brain or if this is a separate effect.– Another detail that the authors could add: the time of day of collection for metabolomics experiments. The authors might comment in the discussion on how this timing might relate to the mechanism. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]Comments to the Authors:We are sorry that, after consultation with the reviewers, we have decided that this work cannot be considered further for publication by eLife. However, we would be willing to consider an extensively revised manuscript if you choose to address the reviewers' concerns and resubmit to eLife as a new submission.Specifically, as you will see from the comments below, while all of the reviewers found the question and approach of this study to be very interesting, all felt that several additional experiments including routine controls and procedural enhancements as well as careful rewriting would be required. Particularly critical is to attempt experiments to establish causality of acylcarnitine build up and sleep regulation (e.g through acylcarnitine microinjection?). A subset of other issues raised in the reviewer discussions included the need for: (1) more complete description of used methods used; (2) description of all drivers as well as transgenic constructs used, with references as well as validation of driver expression via UAS-GFP if not previously described . (3) validation of RNAi efficacy via qRT (repo>RNAi and head or brain mRNA levels); (4) ideally, key experiments to employ more than one RNAi transgene to ensure that effects are not due to off-target effects, while acknowledging that sometimes these genetics are difficult (e.g. with TRiP lines).We have included (1) more complete methods; (2) description of drivers, including references for GFP expression of these; (3) validation of RNAi efficacy; (4) use of more than one RNAi transgene wherever possible. We have also conducted other additional experiments recommended by reviewers, as indicated below.Reviewer #1 (Recommendations for the authors):Drosophila. In previous studies, the Sehgal lab found that disrupting endocytosis in glia of the fly blood-brain barrier is sufficient to increase sleep. Here, the authors sought to explore the mechanism(s) behind this phenomenon by measuring metabolites in the heads of Repo>shi1 flies. LC-MS found elevations in a variety of acylcarnitines in the heads of long-sleeping Repo>shi1 flies, then behavioral experiments found that RNAi-mediated knock-down of two lipid-transport related genes  or two carnitine transporters (Orct and Orct2) are sufficient to significantly increase sleep time. Finally, LC-MS measurements in each long-sleeping RNAi-knock down genotype  also showed increased levels of acylcarnitine species. Together, these studies nicely parallel findings of heightened acylcarnitine levels in the blood of sleep-deprived humans, indicating that the fly provides a tractable model for understanding the mechanistic link between fatty acid metabolism and sleep.Artiushin et al. use a combination of metabolomics and behavioral genetics to detail the relationship between sleepiness and lipid transport in While the authors' interpretations are consistent with their findings, the addition of a few key experiments, new analyses of behavioral data, and text edits could strengthen the manuscript.Main comments:The authors find sleep effects of manipulating fatty acid transport-related pathways in BBB glia, but it is not clear to me whether the acylcarnitines are moving into the brain or out of it. These possibilities each indicate different (and interesting) possibilities for sleep regulation and/or function. Along similar lines, the authors could test whether acylcarnitine levels are increased in the bodies of sleep deprived flies – this finding might help to distinguish whether these are sleep-promoting signals generated in the periphery or a metabolite that needs to be cleared from the brain.1, which were the ones analyzed here, and not sleep-deprived flies. We collected bodies of RepoGal4>Shi1, flies and controls and report here that while they show changes in lipid content, the changes are different from those seen in heads. Please see the results in Figure 1- supplement Figure 1.This is a good idea, thank you. We assume the reviewer meant bodies of RepoGal4>ShiIf the sample material for LC-MS experiments came from whole heads, then it is possible that the head fat bodies contributed to the fatty acid changes shown in Figures 1, 5 & 6. The authors should mention and discuss this possibility in the manuscript.Good point. We have included discussion of this possibility in our revised manuscript.Do the sleep changes in Figures2-4 alter sleep during the day or night (or both)? Do these RNAis alter sleep architecture or waking locomotor activity?Day and night changes in sleep are now shown in Figures2-4. We have also characterized sleep architecture for all behavioral assays and now show this, along with activity index (waking locomotor activity), in Figure 2 and in several supplemental figures for Figures3 and 4. None of the RNAis altered waking locomotor activity.The authors use Gal80TS to restrict the timing of Mgl/Lrp1 RNAi  and Orct/Orct2 RNAi . A clearer description of the temperature protocols should be included. Do the 18°C and 31°C represent different groups of flies that were tested in parallel? Or do they show the same flies before and after a temperature increase? In addition, the flies housed at 18°C (when RNAi expression should be blocked) show similar behavioral phenotypes as the 31°C condition and as flies expressing the same RNAis without Gal80TS . These data indicate either that the Gal80 repressor is not effectively blocking RNAi expression or that the sleep effects observed here cannot be solely attributed to the RNAi knock-downs.A description of the temperature protocol has been added to the Methods and figure legend in the revised MS. The same flies were assayed before and after a temperature increase . We have done additional repeats of sleep tests and, as shown in Figures 3 and 4, the data indicate that the Gal80 repressor is effectively blocking RNAi expression.Does supplementing fly food with acylcarnitines alter sleep? If so, are flies expressing Mgl/Lrp RNAi or Orct/Orct2 RNAi in perineural glia sensitized to dietary acylcarnitines?iso31 flies mixes of acylcarnitines of different chain lengths and monitored sleep behavior. We found that acylcarnitines mixture 1 (C0 and C2) did not alter sleep amount, but low concentrations of acylcarnitine mixtures 2 and 3, which are composed of medium and long chain acylcarnitines, produced a small increase in sleep. Given that sleep is already increased with knockdown of Mgl/Lrp or Orct/Orct2 in perineural glia, we suspect that it would be difficult to detect a further increase with acylcarnitine feeding. Fly sleep easily gets to ceiling levels.To address the reviewer’s question, we fed Do all error bars indicate SEM as listed in the legends? Several of the behavioral plots appear to show overlapping error bars across comparisons that are designated as statistically significant. See Figures 3B, 3C, 4B, 4C and Figure 3 – —figure supplement 1.Sorry about this. Previous figures actually plotted SD in some cases, but we have now fixed this so error bars show SEM, as indicated in the legends of the revised MS.Reviewer #2 (Recommendations for the authors):Here, Artiushin et al. perform metabolomics on the heads transgenic mutants that exhibit long sleep due to inhibition of glial endocytosis (via repo driving shibire) compared to genetic controls. They identified multiple changes in metabolites; the most consistent and significant change was increased acylcarnitine levels. The authors performed a candidate screen by driving RNAi-mediated inhibition of 50 glia-enriched genes with pan-glial or sub-glial expression drivers. Knockdown of lipid transporter proteins in barrier glia led to increased acylcarnitines and increased sleep, suggesting that increased acylcarnitine levels may increase sleep. Previous results had shown that barrier glia and endocytosis were involved in sleep regulation; these data support the hypothesis that lipid metabolites, transported by barrier glia into the brain, mediate sleep regulation. Specifically, they point to the hypothesis that increased acylcarnitine levels in the brain are correlated with induction of sleep.The conclusions of this paper are mostly well supported but some specific aspects need to be clarified or tested.1) The n are low for several behavioral experiments and the number of trials is not clear for any experiments. Sleep and behavior are notoriously inconsistent from trial to trial. Experiments should be repeated with 16-32 flies per trial with 3 trials per experiment to demonstrate robustness of results.All the experiments have been repeated at least 3 times and additional numbers have been added.2) As negative controls for subglial drivers that drive RNAi expression and affect sleep behavior, the authors should also test a non-barrier sub-glial expression driver such as eye glial drivers for effects on sleep behavior.MZ0709-GAL4, NP2222-GAL4 and Eaat-GAL4 with tubgal80ts to express Lrps or Orcts RNAi in ensheathing glia, cortex glia and astrocyte-like glia respectively. None of these affected sleep .As suggested by the reviewer, we used non-barrier glial drivers 3) Are there other methods of increasing acylcarnitine levels in the body or brain that will increase sleep?iso31 and report increases in sleep with some of them . We also tried to inject acylcarnitines into fly heads but we could not control the amount and distribution of the injected material.We used acylcarnitines as a food supplement for 4) What are acylcarnitine levels for short-sleeping mutants?Fmn, Rye and SssP1 and found that acylcarnitines are increased in these . As we believe these mutants to be chronically sleep-deprived, i.e. they have the need to sleep but are unable to implement it, these data support our hypothesis that acylcarnitines reflect high sleep need.We have conducted metabolomic analysis of three short-sleeping mutants This reviewer appreciates all the data and statistics listed for metabolomics. These data will be invaluable for other researchers in the field. Nevertheless, the precise n and trial number for each experiment should be listed.All the experiments have been repeated at least 3 times and new data added.Reviewer #3 (Recommendations for the authors):Drosophila. In this new report, they document changes in metabolites that occurs when the fly BBB is disrupted, with acylcarnitines accumulating in particular. Finally, using RNAi, they show that disruption of candidate transporters in the BBB alter sleep.This manuscript is a follow up to a previous publication from the group that linked blood-brain-barrier function to sleep in Overall, the manuscript provides promising data with the potential to be very interesting; however, the current version of this manuscript is hastily assembled. For example, the introduction refers somewhat vaguely to this group's previous paper, of which the current work is an extension, without really giving the reader much context of the state of the field. Although familiar with that previous paper, I had to refer back to it at several points when reading the current document to make full sense of the current manuscript. Particularly poor are the documentation of the methods; an expert in the field would not be able to replicate the findings based on the methods. For just one example-unless I am mistaken (and I misread Flybase), the Shi.ts1 manipulation used to impair the BBB is a temperature sensitive allele, yet there is no mention of samples being placed in restrictive or permissive temperatures. They do an RNAi screen in the middle of this paper, but that screen and its methods are not described. Not even all the fly stocks that appear to be used in the manuscript appear listed in the Methods.More complete documentation of the experiments being performed would make it easier to properly evaluate the figures and conclusions.shibire flies for metabolomics at 30℃, even though sleep phenotypes are evident at permissive temperatures also  and we include details of the screen as well as a list of fly stocks in Methods.We apologize for the omissions. As noted, we have conducted additional experiments and also extensively re-written the manuscript to address these concerns. In particular, we now indicate that we maintained The paper needs a major rewrite of the methods. Other areas that need to be addressed:The methods section has been rewritten in the revised MS.In Table 1 , many of the metabolites labelled as interesting appear to be upregulated to quite a degree when comparing the two control strains, which makes it hard to judge whether these metabolic changes are meaningful or not. They appear to use a t-test for each metabolite, which further confounds the interpretation-some kind of false discovery rate correction should be applied to such large lists of comparisons.Short, medium, long and very long chain acylcarninites are shown in Table 1 and Figure 1. We have used one-way ANOVA with Holk-Sidak post-hoc comparisons to compare these in experimental and control groups in the revised manuscript .On a related note, the manuscript could do a better job of guiding the reader to how the genetic manipulations are linked to the metabolite profile deemed important/different in the first figures. For example, comparing the results from Figure 5 and 6, it would appear that only C16 and C16.1 are showing differences that are found in Figure 1, but I could only find this upon careful examination myself when the authors should really do a better job to help us make sense of this data. Similarly, Figure 2's results don't seem in any way connected to the metabolite profiles, so I cannot understand why this data is included here.We apologize for this oversight, and have added comparison of Figure 5-6 data to Figure 1. Regarding Figure 2, we acknowledge that these data are not connected to metabolite profiles, but they were important findings of our transporter screen and we feel they should be reported. If the reviewer feels these data should be in the supplement, we can move them.Other aspects that I found difficult to assess:– The RNAi screen, were more than one RNAi aimed at the same gene to confirm the effects?The revised manuscript reports the use of multiple RNAis for Lrp1/Mgl and Orct/Orct2. We have now also checked the knockdown efficiency of RNAi lines by qPCR .– The raw values for metabolites are labelled as "area under the curve" in Figure 1 (unclear what that means) and plotted as μM in Figure 5.The metabolite data were from two different facilities (Metabolon and The Penn Metabolomics Core). They used different methods, ’area under the curve’ by Metabolon and μM by the Penn Metabolomics Core to show raw values.– The n's for each experiment appear to be 13-16 per genotype. That sounds like a single tracking experiment to me. Were any of these experiments done as independent biological replicates (i.e. repeated on separate days?)We show additional data in the revised MS. The sleep data are from at least 3 independent trials and about 48 flies per genotype.[Editors’ note: what follows is the authors’ response to the second round of review.]Reviewer #1 (Recommendations for the authors):Specific comments:1. Does Figure 1 represent data from the same experiments described in Table 1? It's not clear to me whether Figure 1 shows more detail from the same data points or represents additional replicates. Also, the legend for Figure 1 describes the use of T-tests for significance – these should be analyzed using ANOVA instead with post hoc comparisons for the significance of each fatty acid.Yes. Figure 1 represents data from the same experiments described in Table 1. We did indeed use one-way ANOVA with Holk-Sidak post-hoc comparisons, and not T-tests, for significance in figure 1. Table 1 compares the experimental to each of the parental controls separately and so uses a T-test.2. The authors list the contents of each acylcarnitine type that is contained within the mixtures that are fed to flies, but a clearer discussion in the text that compares these lists with the lipids that were significantly changed in figure 1 might help the reader.We have added a comparison of acylcarnitine mixtures (from Σ) and the ones that are significantly changed in figure1.3. Is sleep architecture changed in flies that sleep more with acylcarnitine feeding?We have added the analysis of sleep architecture in flies fed acylcarnitines, in multiple additional supplemental figures.Reviewer #3 (Recommendations for the authors):The authors did great work in addressing the majority of prior reviewer comments, specifically by clarifying their Materials and methods and adding supplementary experiments that increase confidence in their findings. The authors find that many different types of gene manipulations in the blood-brain barrier are associated with both long sleep and high acylcarnitine accumulation in the brain; the number of these experiments strongly supports a relationship between these three events. Precisely how these different gene manipulations in the BBB lead to acylcarnitine accumulation in the brain and how/where/why acylcarnitine accumulation in the brain might cause long sleep (as opposed to resulting from long sleep) remain unclear. Thus, while the authors present sufficient and exciting evidence that sleep, BBB function, and acylcarnitines are related, due to the lack of a cohesive model or mechanism, the authors are right to be cautious in making more specific claims about the causal nature of these relationships.This manuscript was clearer and more well-written than the previous version. This reviewer did not go through with a fine-tooth comb to examine whether claims of causality were overstated; the authors should do this.I include here a few points that could be mentioned in the discussion, results, or methods sections as caveats or clarifying points:– Is there any evidence in the literature that acylcarnitines are removed from the brain via the BBB, as would be suggested by acylcarnitine accumulation after the loss of endocytosis or transporter function in BBB? Is this the authors' hypothesis?Drosophila BBB is composed entirely of glia). Please note too that although the role of the BBB is supported by the sleep phenotypes we get with knockdown of lipid/carnitine transporters specifically in the BBB, the metabolomic analysis was conducted with manipulations of all glia.This is an interesting point. We were not able to find anything in the literature that says acylcarnitines are removed from the brain via the BBB. It is possible that this uptake is specific for glial cells (– The feeding experiments, though the effects were mild, go one step toward addressing the role of acylcarnitines in triggering sleep except that it's not clear if eating acylcarnitines leads to acylcarnitine accumulation in the brain or if this is a separate effect.It is true that at this point we do not know if the fed acylcarnitines accumulate in the brain. We have acknowledged this point in the manuscript.– Another detail that the authors could add: the time of day of collection for metabolomics experiments. The authors might comment in the discussion on how this timing might relate to the mechanism.The flies were collected at ZT 0-3 for the metabolomics analysis. We have added discussion of how the timing relates to the results.


Causal Roadmap, we specify three designs to evaluate the difference in risk of major adverse cardiovascular events (MACE) with oral semaglutide versus standard-of-care: (1) the actual sequence of non-inferiority and superiority randomized controlled trials (RCTs), (2) a single RCT, and (3) a hybrid randomized-external data study.Increasing interest in real-world evidence has fueled the development of study designs incorporating real-world data (RWD). Using the The hybrid design considers integration of the PIONEER 6 RCT with RWD controls using the experiment-selector cross-validated targeted maximum likelihood estimator. We evaluate 95% confidence interval coverage, power, and average patient time during which participants would be precluded from receiving a glucagon-like peptide-1 receptor agonist (GLP1-RA) for each design using simulations. Finally, we estimate the effect of oral semaglutide on MACE for the hybrid PIONEER 6-RWD analysis.In simulations, Designs 1 and 2 performed similarly. The tradeoff between decreased coverage and patient time without the possibility of a GLP1-RA for Designs 1 and 3 depended on the simulated bias. In real data analysis using Design 3, external controls were integrated in 84% of cross-validation folds, resulting in an estimated risk difference of –1.53%-points (95% CI –2.75%-points to –0.30%-points).Causal Roadmap helps investigators to minimize potential bias in studies using RWD and to quantify tradeoffs between study designs. The simulation results help to interpret the level of evidence provided by the real data analysis in support of the superiority of oral semaglutide versus standard-of-care for cardiovascular risk reduction.The  Regulatory agencies around the world have increasingly considered how studies that incorporate real-world data (RWD) might inform the regulatory approval process . One parThese study designs require special considerations. First, each design relies on different causal identification assumptions – i.e., assumptions about the underlying trial and/or real-world processes that generate the data that will be used to estimate a treatment effect. For many designs, these assumptions are not testable, but the validity of inferences about treatment effects nonetheless relies on them. Second, while single-arm trials may be analyzed using traditional statistical estimators, special estimators have been developed for analyzing data from the second two types of external data designs ,4. If thCausal Roadmap [Causal Roadmap, which involves comparing alternative study designs using simulations. Our discussion of this complex example aims to facilitate comprehension of the Roadmap steps described in the companion paper.The purpose of this paper is to demonstrate how to apply a  Roadmap –9, descr Roadmap , to the Semaglutide, a glucagon-like peptide-1 receptor agonist (GLP-1RA), was developed as an antihyperglycemic agent and has been shown to improve multiple health outcomes for patients with type 2 diabetes mellitus (T2DM). For example, in the SUSTAIN-6 trial, injectable semaglutide decreased rates of MACE ) compared to placebo in patients with high cardiovascular risk (hazard ratio (HR) 0.74, 95% confidence interval (CI) 0.58–0.95) . SubsequOral semaglutide was later developed and shown to decrease glycated hemoglobin (HbA1c) and body weight compared to placebo  and multA superiority RCT is a standard choice for evaluating the effect of interest, yet RCTs may also have downsides. For example, clinicians treating placebo-arm patients are directed not to prescribe medications of the same class as the treatment under investigation –19. Yet Causal Roadmap [To evaluate these potential tradeoffs, we use the  Roadmap –10 to coCausal Roadmap steps found in the companion article [Table  article . As depi1  for the causal estimand.The question for all study designs was what would the difference in risk of MACE  within one year be if all patients in a target population consistent with the PIONEER 6 inclusion/exclusion criteria and timeframe , and witU, might affect both censoring and MACE. Measured pre-baseline covariates, including age, sex, race, HbA1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, estimated glomerular filtration rate (a marker of kidney function), prior MI, prior stroke or transient ischemic attack (TIA), prior heart failure, morbid obesity, and use of glucose-lowering medications, insulin, and CV medications, may account for some aspects of these underlying factors.Next, we specify a causal model for each design describing what variables might affect treatment, censoring, or outcomes using the causal graphs  shown in2 .In hybrid Design 3 Fig. b, particBeing in the RCT could also modify the effect of treatment or directly affect measured outcomes for reasons including closer monitoring, encouragement of adherence, variation in standard-of-care or placebo effect, or more accurate outcome measurement –28. The Fig. Next, we consider the data that will be observed. Designs 1–2 propose to use data from one or both of the following RCTs: PIONEER 6  and SOULThe external control arm considered in Design 3 could come from multiple sources, including the control arm of a previous trial such as SUSTAIN-6. While we would expect controls enrolled in a previous trial to be more similar to the current trial controls than participants in RWD, in this case study, we chose to consider RWD controls in order to discuss the many challenges that might arise in a hybrid study when a past trial’s control arm is not available. The RWD also has the advantage of being contemporaneous with the RCT, avoiding bias due to temporal differences between the RCT and external control arms. Specifically, the RWD considered in Design 3 came from Optum’s de-identified Clinformatics® Data Mart Database (CDM) (2007–2022), which is derived from administrative health claims for a geographically diverse population, spanning all 50 of the United States.Consistent with recommendations from the RCT DUPLICATE study, we used naive initiation of a DPP4i (defined as a new prescription following at least 90 days without a previous prescription based on AHFS codes) to enhance comparability of healthcare access and engagement among RWD compared to RCT controls . Time ze1, Appendix 7). Medical history variables that were part of these criteria or the baseline characteristics discussed in Step 1b were determined based on ICD-9/ICD-10 diagnosis and procedure codes. Medication use was identified through AHFS codes, where treatment at baseline corresponded to at least one prescription in the 180 days preceding time zero. To identify GLP1-RA and pramlintide usage for the exclusion criteria, we defined continuous treatment eras as consecutive prescriptions based on AHFS codes with no more than 90-day gaps between them. Laboratory measurements were identified through LOINC codes, where baseline values were identified as the most recent measurement within 180 days prior to time zero. For the primary and negative control outcomes, we identified nonfatal MI/stroke and fractures using ICD-9/ICD-10 codes for inpatient visits in the first diagnosis position . All-cause death was identified from external sources as provided by Optum.We applied as many of the PIONEER 6 inclusion/exclusion criteria as possible given the available CDM data  is identifiable from the observed data Step 2). When using RCT data alone (Designs 1–2), we assume no unmeasured common causes of treatment or censoring and MACE and adequate data support (positivity) ,34. Thes. When usIn contrast, rather than rely on these assumptions holding, Design 3 estimates the magnitude of bias that would be introduced by RWD before deciding whether to integrate RWD with RCT data. If violation of these assumptions results in meaningful bias, Design 3 is likely to reject the RWD controls. In selecting candidate external controls for Design 3, the objective is to satisfy these assumptions to the extent possible, thereby increasing the probability that RWD will be integrated into the hybrid analysis, decreasing the probability that a follow-up superiority trial will be necessary, and minimizing risk of lower than nominal confidence interval coverage.To improve the plausibility of identification assumptions, we selected RWD controls with a similar disease stage and healthcare engagement compared to the RCT controls based on those who were prescribed an active comparator medication (DPP4i) and had relevant baseline labs and medical history recorded. The aim was to minimize the impact of factors like health status and healthcare engagement on selection into the RCT versus the external cohort and on the definition of standard of care in the two studies. Yet this step also restricts the target population for the hybrid analysis to patients similar to those who actually enrolled in the RCT. Because DPP4i use was an exclusion criterion in PIONEER 6, we also require the assumption, supported by data, that DPP4is do not influence cardiovascular outcomes . We also1  provides statistical estimands that are equivalent to the treatment effects of interest, or causal estimands, under identification assumptions. Appendix 2  provides further discussion of these assumptions.As is generally true when observational data are considered, it remained unlikely that all causal identification assumptions held completely. Yet with the above considerations, it was plausible that the bias from integrating RWD would be small. Design 3 evaluates this claim empirically. Appendix Next, we chose a statistical estimator. In Designs 1–2, because censoring was negligible, we estimated the unadjusted risk difference between arms among persons with follow-up through one year. For Design 3, we used an estimator designed to analyze hybrid studies.et al. (2018) [et al. (2022) [Multiple hybrid-design estimators have been developed in recent years, with the common goal of evaluating whether to include (or how to weight) external data in a hybrid analysis based on the difference in mean outcomes, conditional mean outcomes, or effect estimates estimated using external data or estimated using RCT data alone. These estimators differ based on whether they take a Bayesian –38 or Fr. (2018)  and Ober. (2022)  for moreIn this case study, we use the experiment-selector cross-validated targeted maximum likelihood estimator (ES-CVTMLE)  as the e1, Appendix 3) [et al. (2022) describe how these two bias estimates are used to decide whether to integrate RWD or to analyze the RCT data alone [The ES-CVTMLE extends the cv-TMLE method to evaluate and integrate external data . First, endix 3) . ES-CVTMta alone .In this case study, we chose the ES-CVTMLE for Design 3 because it relies on few statistical assumptions, incorporates an estimate of bias based on an NCO, and adjusts confidence interval widths based on the estimated magnitude of bias. While the focus of this case study is on using simulations to compare study designs, similar simulations could also be used to compare different potential estimators. Appendices 3–6 provide further details about estimation.Causal Roadmap Step 6 is to conduct a sensitivity analysis to evaluate how violations of identification assumptions might impact conclusions. Because the topic of sensitivity analysis is complex, we do not cover this step in this case study but instead refer readers to the companion paper that reviews sensitivity analysis in detail.1, Appendix 4). The code to run these simulations may be found in Supplementary Material 2. For Designs 1 and 3, we simulate data from a small RCT aiming to mimic PIONEER 6 (RCT1). For Design 1, we use an unadjusted estimate of the difference in risk between arms of RCT1. For Design 3, we consider simulated RCT1 and a simulated “real-world” dataset aiming to mimic CDM, and we use the ES-CVTMLE to estimate the risk difference. For the sake of comparison to a more standard estimator, we also report the results of simulated Design 3 if a cv-TMLE with no assessment of bias had been used to analyze the pooled RCT and RWD. In both Designs 1 and 3, if the null hypothesis is rejected at this first stage, we use this initial estimate as our final effect estimate. If the null hypothesis is not rejected, then we simulate data from a larger trial aiming to mimic a superiority trial (RCT2) and estimate the risk difference using an unadjusted estimate. In Design 2, we use the unadjusted effect estimate from RCT2.We compare Designs 1–3 using simulations that mimic our true study designs  are violated and the empirical bias estimate based on the available finite sample is smaller than the true bias . Whether this risk is acceptable will depend on the context, including the magnitude of benefit to patients of Design 3 over Designs 1–2. While there are many potential ways to quantify benefit to patients, here we estimate the number of patient-years during which participants are precluded from starting a GLP1-RA (by being in an RCT control arm), averaged over 1000 iterations of this simulation. We also report power to detect the true effect using α = 0.05. Because participation in an RCT requires acceptance of potential risks and commitment of time to the study, it is important that any design that involves an RCT be adequately powered to answer the question of interest .5  shows the results of the same simulation both when the NCO is not considered and when the NCO is considered but the unmeasured factors causing bias for the relationship between semaglutide and MACE have no effect on the NCO, mimicking a worst-case scenario for violations of both the causal identification assumptions and the assumptions needed to measure bias using an NCO.We evaluate Design 3 when the magnitude of bias introduced by including simulated RWD is zero and when it is one of ten potential magnitudes in either direction up to ± 2.1%. In this primary simulation, the effect of unmeasured factors causing bias is the same on the relationship between semaglutide and MACE as it is on the relationship between semaglutide and the NCO. Appendix Fig. The tradeoffs between Design 3 and Design 1 depended on the direction of bias introduced by the RWD. With unbiased simulated RWD, Design 3 had coverage of 0.944, had power of 0.854, and resulted in an average of 290 fewer participant-years during which patients were precluded from starting a GLP1-RA compared to Design 1. In other words, on average, 6% fewer people would have spent one year during which their doctor avoided prescribing a GLP1-RA if Design 3 were chosen and unbiased RWD were available compared to if Design 1 were chosen. If simulated RCT1 and RWD had been pooled and analyzed with a cv-TMLE instead, Design 3 with unbiased RWD would have had coverage of 0.941, power of 0.862, and an average of 618 fewer participant-years during which GLP1-RA use was discouraged. However, the downsides of this approach, which relies on causal identification assumptions rather than evaluating them empirically, are apparent  represents scenarios in which the introduction of RWD lowers the estimated risk of MACE among control arm participants. This could happen if MACE were not well-recorded in the RWD. For Design 3, simulated positive bias led to coverage ranging from 0.943 to 0.948, power ranging from 0.825 to 0.834, and an average of 38 to 195 extra participant-years during which prescription of a GLP1-RA was discouraged compared to Design 1. The increase in person-years without GLP1-RA access occurred because RWD with bias toward the null was included in a small number of simulation iterations, triggering a second RCT. As shown in Fig. Negative bias (away from the null) represents scenarios in which the introduction of RWD raises the estimated risk of MACE among control arm participants. This could happen if the subjects whose data were collected in the RWD showed more severe health outcomes  than the trial participants due to differences described in Step 1b. Simulated negative bias led to coverage for Design 3 ranging from 0.929 to 0.942, power ranging from 0.834 to 0.860, and an average of 33 more to 461 fewer participant-years during which prescription of a GLP1-RA was discouraged compared to Design 1. For comparison, if one had used a naïve cv-TMLE estimator to analyze the pooled simulated RCT1 and RWD, coverage would have been as low as 0.123 , providing evidence in support of the superiority of oral semaglutide versus standard-of-care for the prevention of MACE. The two primary differences between the risk difference estimates from PIONEER 6 alone compared to the hybrid analysis are narrower confidence intervals and a small negative shift in the point estimate. Narrower confidence intervals resulted from the data-adaptive estimator accepting the CDM RWD for inclusion in the analysis in 84% of cross-validation splits, leading to increased efficiency.The small shift in the point estimate may have occurred for three main reasons. First, the magnitude of the shift is well within what might be expected by chance alone. Second, the shift may be due to subtle changes in the target population that arise from including external controls despite using the same eligibility criteria. Finally, the potential for some residual bias remains.Causal Roadmap to a hybrid randomized RWD trial. We discuss considerations for improving the plausibility of identification assumptions in this data fusion context. We also implement the extension proposed in the companion article [Causal Roadmap to compare different potential study designs using simulations.In this case study, we demonstrate an application of the  article  to use tBoth the FDA guidance on complex innovative trial designs  and the Yet with simulated bias toward the null, Design 3 had similar coverage but potentially more patient time without a GLP1-RA compared to Design 1, defeating the purpose of the hybrid design. It would thus be important to evaluate how likely this scenario would be before proposing Design 3. For example, a study could be conducted to compare outcomes recorded in CDM to outcomes recorded following an RCT protocol to assess the likelihood that bias toward the null would occur due to under-reporting of outcomes in the RWD.Compared to Design 1, Design 3 with bias away from the null generally resulted in less time during which a GLP1-RA was avoided, but coverage fell below 95% for intermediate magnitudes of bias. These results are consistent with simulations and theoretical assessments of other estimators for hybrid randomized-external data designs that have demonstrated that hybrid designs cannot both (1) improve power or mean squared error (MSE) compared to an RCT alone; and (2) guarantee 95% confidence interval coverage (or no decrease in MSE) regardless of the magnitude of bias introduced by the external data . The risCausal Roadmap supports a rigorous design process and reporting structure to ensure pre-specification of components needed to support the validity of causal inferences drawn from such designs. The current case study provides a detailed work example of this process.Our estimate of the difference in the risk of MACE by one year with oral semaglutide versus standard-of-care supports the superiority of oral semaglutide, but regulatory decisions regarding whether to extend the label of oral semaglutide to include the secondary indication of cardiovascular risk reduction will await the results of the SOUL trial. A key limitation of this study is that it was planned after PIONEER 6. RWD studies aiming to support policy and/or regulatory decision-making must pre-specify all design and analysis decisions before effects are estimated from any of the proposed data sources . The CauWhile this study aimed to quantify tradeoffs between three proposed designs, other approaches could be considered. Hybrid RCT-RWD designs may adapt the probability of randomization to active treatment based on the efficiency gains that are achieved by integrating RWD and also on the probability of superiority compared to placebo ,48,49, pCausal Roadmap for pre-specification of hybrid randomized-RWD designs.This study reports one of many potential metrics aimed at quantifying the benefits and drawbacks of different study designs from the perspective of patients. While recommendations have been proposed regarding the elicitation of patient perspectives to inform medical product development ,51, furtDang et al. supplementary material


Ethiopia has been challenged by the growing magnitude of diabetes in general and type-2 diabetes in particular. Knowledge extraction from stored dataset can be an important base for better decision on diabetes rapid diagnosis, suggestive on prediction for early intervention. Thus, this study was addressed these problem by application of supervised machine learning algorithms for classification and prediction of type 2 diabetes disease status and might provide context-specific information to program planners and policy makers so that, priority will be given to the more affected groups. To apply supervised machine learning algorithms; compare these algorithms and select the best algorithm based on their performance for classification and prediction of type-2 diabetic disease status (positive or negative) in public hospitals of Afar regional state, Northeastern Ethiopia. This study was conducted at Afar regional state from February to June of 2021. Decision tree; pruned J 48, Artificial neural network, K-nearest neighbor, Support vector machine, Binary logistic regression, Random forest, and Naïve Bayes supervised machine learning algorithms were applied using secondary data from the medical database record review. A total of 2239 sample Dataset diagnosed for diabetes from 2012 to April 22/2020 (1523 with type-2 diabetes and 716 without type-2 diabetes) was checked for its completeness prior to analysis. For all algorithms, WEKA3.7 tool was used for analysis purposes. Moreover, all algorithms were compared based on their correctly classification rate, kappa statistics, confusion matrix, area under the curve, sensitivity, and specificity. From the seven major supervised machine learning algorithms, the best classification and prediction results were obtained from random forest  which was followed by decision tree pruned J 48  and k-nearest neighbor . Random forest, Decision tree pruned J48 and k-nearest neighbor algorithms have better classification and prediction performance for classifying and predicting type-2 diabetes disease status. Therefore, based on this performance, random forest algorithm can be judged as suggestive and supportive for clinicians at the time of type-2 diabetes diagnosis. Four major NCDs: diabetes, cardiovascular diseases, cancer and chronic respiratory diseases; are responsible for over 80% of NCD deaths of which more than 40% are premature, and the rest will occur in people under 70 years of age which is the productive age of the population3.Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin (a hormone that regulates blood sugar) alternatively; when the body cannot effectively use the insulin it produces. The overall risk of dying among people with diabetes is at least double the risk of their peers without diabetes. Globally, this disease represents one of the major health and development challenges of the twenty-first century4.Currently, Ethiopia has been challenged by the growing magnitude of NCDs in general and diabetes in particular. Ethiopia is among the top four countries with the highest adult diabetic populations in sub-Saharan Africa. In 2019, the number of cases of this disease was 12,839,500 with a point prevalence of 10.2%6.In 2019, the international diabetes federation (IDF) also estimated that, in Africa (low and middle income countries), 19 million adults aged 20–79 years had diabetes, representing a prevalence of 5.2%. This region has also the highest proportion of previously undiagnosed diabetes; over two-thirds (67%) of people with diabetes being unaware they have the disease7. Rather, it requires a shift in healthcare priorities and up-to date data on the prediction and classification of type-2 diabetic disease status in Ethiopia, partially to help plan and priorities for health programmers. Such information can be an important base for policy on diabetes rapid diagnosis, real prediction for early prevention and treatment6.Patient attendance rates and medical admissions related to type-2 diabetes in major hospitals have been rising. This huge amount of data cannot be approached using the ordinal statistical methods to extract inferential information from it rather it needs a strong, complicated, and advanced model8.There is a strong motivation and demand in the use of machine learning methods in knowledge discovery and data mining to generate models of health-related implications. Data Mining is about explaining the past and predicting the future. It is a collaborative field which combines technologies like statistics and informatics . The health care organizations are then able to use the extracted knowledge for more clients, disease diagnosis, classification, and predictionThis study on data mining  for classification and prediction of type-2 diabetic disease status partially might have a significant contribution for physicians and clients. For physicians it will help to facilitate diagnostic activity with suggestive and informative diagnostic results. On the other hand, for clients, an early diagnosis with accurate results will lead to early intervention. As a result, it will avoid unwanted catastrophic diagnostic cost and immediate management for prevention of morbidity and further complications of type-2 diabetes. Therefore, the main aim of this study was to extract hidden and critical knowledge by applying supervised machine learning algorithms for classification and prediction of type-2 diabetic disease status in public hospitals of Afar regional state Northeastern Ethiopia 2021.2 and is geographically located between 39o34′ and 42o28′ East Longitude and 8o49′ and 14o30′ North Latitude . It has an estimated population of 1.2 million, of which, 90% are pastoralists  and 10% are agro-pastoralists.This study was conducted at public hospitals of Afar regional state Northeastern Ethiopia from February to June of 2021. Afar regional state is one of the nine federal states of Ethiopia located in the Northeastern part of the country 588 kms from Addis Ababa. The altitude of the region ranges from 1500 m above mean sea level (m.a.s.l) in the western highlands to − 120 m.a.s.l in the Danakil/Dallol depression. The total geographical area of the region is about 270,000 kmA retrospective cross-sectional study design using medical database and medical chart record review was used.All hospital clients who ever diagnosed or will be diagnosed and/or suspected for type-2 diabetes in public hospitals of Afar regional state.All clients who ever diagnosed for diabetes disease status, and confirmed as free from type-2 diabetes  and type-2 diabetic patient in public hospitals of Afar region starting from the year when the database for electronic health information record was fully functional up to the date of sample collection (2012 GC–April 22/2020) were considered as part of the study population.All clients who ever diagnosed for diabetes disease status and confirmed as free from type-2 diabetes  and type-2 diabetic patients in public hospitals of Afar region starting from 2012 GC to April 22/2020 GC.Patients who can unable to obtain required information due to incomplete or total absence of their record status; which cannot be found in their registration book, diabetic patient under follow-up with unknown start date and diabetic patient referred from other hospitals were excluded from the study population to avoid misleading of the machine learning algorithms.9. Based on this, the study has been conducted on a total of 2239 population.All patients who have been diagnosed for diabetes and confirmed as a type-2 diabetic patient and normal after standard diagnostic activities from 2012 GC up to April 22/2020 GC were used as a sample. The whole dataset used as a sample because data mining needs considerable amount of data for effective prediction and classification by reducing the probability of error to be committedFrom this record, the clients who ever diagnosed for diabetes disease status were extracted and collected with their required variables and some variables which are not available in the database and the parameters for the normal clients were searched from the medical record book by its medical registration number (MRN) because in DHIS database of public hospitals there is no recording site for normal individuals after every diagnosis.Data related to that have been confirmed as a type-2 diabetic patient and normal at public hospitals of Afar regional state from the date of database being fully functional (2012) up to data obtaining date (April 22/2020) GC were collected from DHIS database public hospitals. Clients who have been positive for type-2 diabetes were obtained from the database and those who were normal were collected from the medical registration book by medical chart review and used for comparative purposes. The parameters of these samples were collected from their first date of their diagnosis by cross-checking with their start date of their diagnosis to avoid the misleading of machine learning algorithms. From this, the essential variables were collected, therefore, in this study was used to classify and predict type-2 diabete disease status among clients of public hospitals for all ages and both sexes who were diagnosed for diabetes in the region.The dependent variable is type-2 diabetes disease status with dichotomous response to the question “tested and confirmed as type-2 diabetic patients” if yes = 1″ and “if no = 0”.Diastolic Blood Pressure (DBP)Systolic Blood Pressure (SBP)Fasting Blood Sugar Level (FBS)Random Blood Sugar Level(RBS)Body Mass Index (BMI)AgeSexThe above dependent variable was then being modeled to historical predictor variables and reasons that were selected based on existing evidences. The independent variables that have been assessed in this research were:-Accuracy Accuracy of classifier refers to the ability of classifier to predict the class label correctly, and it also refers to how well a given predictor can guess the value of the predicted attribute for a new data10.Classification Is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict  the target class for each case in the data11.Confusion Matrix Is a simple performance analysis tool typically used in supervised machine learning. It is used to represent the test result of a prediction model. Each column of the matrix represents the instances in a predicted class, while each row represents the instances in an actual class12.Kappa Statistics Are a metric that compares Observed Accuracy with Expected Accuracy (Random chance) that the samples randomly would be expected to reveal13.Receiver Operating Characteristic test (ROC) Are a plot of the true positive rate against the false positive rate for the different possible cut points of a diagnostic test. The area under the curve (AUC) is a measure of diagnostic accuracy8.Prediction Are a data mining approach that aims at building a prediction (for continuous independent variables) model for classifying new instances into one of the two class values (yes or no)14.Sensitivity Is the proportion of positive cases that were correctly identified, which is the most important thing in disease diagnosis15.Specificity Is defined as the proportion of negative cases that were classified correctly16.Data related to the outcome variable of type-2 diabetic disease status were selected and extracted from the dataset of DHIS database and from the medical registration book of public hospitals of the region. Furthermore, data cleaning, labeling, coding were done for all selected variables. On the data preprocessing phase, data manipulation and data transformation for incomplete data handling and missing value management were conducted to achieve the best data quality for prediction and classification of type-2 diabetes disease status.The raw (preprocessed) data were contained 13 independent; one dependent variable which is categorical variable with 2,239 samples (instances).Moreover, in this phase, the variable set which had few records (only 2 records) due to its expensiveness, i.e., glycated hemoglobin level, and the other variables which had no role on prediction of type-2 diabetes disease like (MRN and start date), the variables which have similarity among each other (height and weight with BMI) were removed from the final predictor list of variables.The processed variables were proceeding to data mining activity for prediction and classification of type-2 diabetes disease status and their missing values of each record were replaced with mean value of each variable. Finally, after the data were processed, a total of 2239 clients with their 7 major explanatory variables (attributes) based on variable selection method that were diagnosed for diabetes disease were included for classification and prediction of type-2 diabetic disease status using supervised machine learning algorithms Table .Table 1PData of clients for diabetes disease status were collected from DHIS database of public hospitals of the region, and then it was checked for its completeness prior to analysis to increase data quality. Data preprocessing was carried out before final analysis to treat missing values and incomplete records. This processed data was changed in to comma separated value (CSV) and attribute relation file format (ARFF) to be loaded in to WEKA-3.7 tool which was used for data analysis purposes. Then the major classification and prediction supervised machine learning algorithms were applied.On the descriptive part, appropriate descriptive statistical methods such as frequencies, percentages, tables, and graphs were used to summarize and present the findings. For the inferential part, the major supervised machine learning techniques of data mining algorithm for prediction of type-2 diabetes  were used. The performance evaluation based on their output for effective prediction and classification of type-2 diabetes disease status among these algorithms was assessed.12.Since the main objective of this study is applying supervised machine learning algorithms for classifying and predicting new clients’ whether they have type-2 diabetes or not using the information extracted from the diabetes’ dataset. The model building phase in the data mining process of this investigation was carried out under classification and prediction data mining approach. This classification and prediction tasks was conducted using the major supervised machine learning algorithms  for classifying and predicting the presence or absence of type-2 diabetes disease up on the 70% of the full dataset which was the training data set. To increase classification accuracy, it is better to use many of the dataset for training and few dataset for testing on percentage split of 70 : 30 division15. All of these tasks were implemented using WEKA 3.7 data mining tool .17.Commonly used variable selection methods available in commercial software packages, i.e., WEKA tool are Wrapper Subset Forward Selection, Best First -D 1 -N 5 variable selection, and linear forward selection methods. For this study, the Best First -D 1 -N 5 variable selection method was used at tenfold cross-validation and seed number 50 methods which is better for binary outcome studiesReceiver operating characteristics (ROC) curve was used to assess the general accuracy of the model to the dataset using the area under receiver operating characteristics (AUC). ROC curve is a commonly used measure for summarizing the discriminatory ability of a binary prediction model.18.The ROC curve describes the relationship between sensitivity and specificity of the classifier. Since the ROC curve cannot quantitatively evaluate the classifiers, AUC is usually adopted as the evaluation index. AUC value refers to the area under the ROC curve. An ideal classification model has an AUC value of 1, with a value between 0.5 and 1.0, and the larger AUC represents that the classification model has better performanceThe performance evaluation (model diagnosis) of different supervised machine learning techniques of data mining algorithms for prediction and classification of type-2 diabetes were carried out. This was being done using cross-validation and different confusion matrices.10. A confusion matrix is used to present the accuracy of classifiers obtained through classification. It is used to show the relationship between outcomes and predicted classes , then we were used for the supervised data mining techniques. The machine learning algorithms and model specifications for prediction and classification of diabetes disease were focused on high performance algorithm and from high dimensional medical dataset. These model comparisons were conducted according to the following table format Table .Table 4CClassification models have been used to determine categorical class labels, meanwhile prediction models were used to predict continuous functions, this was done in the following steps:- Data cleaning, Relevance analysis, and Data Transformation. The obtained datasets was preprocessed and split into 2 sets, training  and test data (30% of the dataset) . The following models were specified for data mining in classification and prediction of type-2 diabetes disease status accordingly.The study was approved by the Institutional Review Board of Samara University. A letter of support was obtained from Samara University. All results of this research were based on the use of secondary data and the data collection was performed prospectively. Therefore, an informed written consent form from the public hospital DHIS Database coordinator was obtained and the study was conducted in accordance with the ethical standards of the institutional and national research committee.The descriptive statistics of the dataset  are presented in Table In addition, the following clustered bar chart stated that out of 2239 total participants, almost half of them 1146 (51.1%) were male represented by green color, 1093 (48.9) were female represented by blue color and 1523 (68%) were positive for type-2 diabetes where as 716 (32%) of them were negative  of the sample was used as a training dataset of the supervised machine learning algorithms and 672 (30%) of the dataset was used as a testing dataset. This classification was conducted at tenfold cross-validation to prepare training and testing dataset. In this study, seven supervised machine learning algorithms were implemented for classification and prediction of type-2 diabetic disease status.From the seven supervised machine learning algorithms, the highest classification and prediction capacity was obtained from RF algorithm at 93.8% of CCI, followed by DT pruned J 48 and K-NN algorithm at 91.8%% and 89.8% of CCI rate respectively. The lowest prediction and classification capacity was obtained from SVM (RBF) algorithm at 85.5% of correctly classification instance rate Table .Table 6PThe other evaluation criteria for classification Algorithm Kappa Statistic were implemented for numeric value only. To choose the best algorithms for soaring performance; different algorithms were implemented and evaluated with respect to some evaluation criterion on the selected dataset. From the table sited below, the kappa statistics of RF algorithm showed that the best result (0.85) followed by DT pruned J48 (0.80) and K-NN 0.76)  and . This performance is followed by K-NN with 0.92% sensitivity and specificity (Table The figure stated below showed that the area under the ROC curve of random forest (RF) which was 0.97, which is the largest value with excellent performance. The ROC curve describes the relationship between sensitivity and specificity of the classifier. Since the ROC curve cannot quantitatively evaluate the classifiers, AUC is usually adopted as the evaluation index. AUC (area under ROC curve) value refers to the area under the ROC curve. An ideal classification model has an AUC value of 1, with a value between 0.5 and 1.0, and the larger AUC represents that the classification model has better performance Fig. .The performance of any classification algorithm is extremely depending on the nature of the training dataset used. In WEKA tool, confusion matrices which are generated after simulation of classification and prediction algorithms are very constructive for evaluating classifiers. The columns in the confusion matrix represent the predicted classification class, and the rows represent the actual classes.The table stated below showed that the confusion matrices of each classifier used tenfold cross-validations. From this, RF and DT pruned J48 algorithm showed the best prediction performance for tested positive instances out of 454 actual positive individuals, 446 and 438 of them had predict as tested positive, respectively. These prediction performances were followed by ANN and K-NN algorithms at 437 and 421 instances predicted as tested positive for type-2 diabetes out of 454 actual positive testing dataset participants (Table The study identified the prediction and classification accuracy of RF , DT pruned J48 ,K-NN .Furthermore, prediction and classification accuracy were obtained from SVM , logistic regression , ANN  and naïve Bayes .19 and the study conducted by Naqvi and his colleagues in 2018 found that RF (accuracy of 89.3%)as the best technique for prediction of diabetes20.From the seven major supervised machine learning algorithms, the best classification and prediction result for type-2 diabetes disease status was obtained from RF classification and prediction algorithm 93.8%ofcorrectly classification capacity. In the WEKA tool, the percentage of accurately classified instances is called the accuracy of the classifying model. RF algorithm also shows an excellent prediction capacity at 100% accuracy on other studies conducted by Tejashri and his colleagues in 2015 at Wagholi, Pune, India13.On the other hand, the best accuracy of this study obtained from random forest (RF) (93.8%) is higher than the accuracy which was obtained from the previous study (85.9%) by Iyer and his collageus. This might be due to the number of input variables used because our study used seven input variables while the other study used only four variables  as an input variable for classification and prediction of diabetes disease status3.Based on their accuracy, this algorithm (RF) was followed by DT-pr-une-d J48 and k-nearest neighbor (K-NN) algorithms at 91.8% and 89.8%, respectively. Another study carried out by Roobini and Lakshmi on 2018 showed that DT pruned J48 and K-NN accuracy of 91.72% and 91.14% respectively, which is consistent in accuracy with our research study21.Basically Pruned J48 Tree is fast DT learning and it builds a DT based on the information gain or reducing the variance. It examines normalized information gain (difference in entropy) that results from choosing an attribute as a split point. The highest normalized IG is used at the root of the tree. The DT pruned J48 generate a tree contained 18 number of leaves with 35 size of tree that gets an accuracy of 91.8%. This help to extract certain rules and reduce the risk of over fitting on the training data. From this, variable age was obtained as a root node with the highest information gain (870.0/148.0) when age is >= 36 year to be more likely tested positive for type-2 diabetes others as a leaves. This result is consistent with the result obtained from a study conducted by Amatul and his colleagues in 2017 from Indian diabetes dataset for prediction and classification of type-2 diabetes disease status which showed a diagnostic result of positive for type-2 diabetes disease was at the age of 40 years old22 showed that plasma glucose concentration as a root node. This difference may be due to this study considers both fasting and random glucose levels separately but the other study used in combination as a single variable.In addition, other rules which can be extracted from this schematic representation showed that age >= 36 years which is the root node, DBP > 81, FBSL > 115, RBSL > 211, sex = female and BMI > 25 more likely to be predicted as tested positive for type 2 diabetes. Another study entitled as “Diagnosis of diabetes using classification mining techniques” which was conducted by Aiswarya and his colleagues13. This inconsistent result might be due to the effect of pruning because pruning reduces the occurrence of events due to chance.The other alternatives that have been used in this study for performance comparison were kappa statistics. From this kappa statistics that compares observed accuracy with expected accuracy (random chance) out of the seven supervised machine learning algorithms, it was higher in RF algorithm which was 0.85 and followed by DT pruned J48 (0.80) and K-NN (0.76).The value of kappa statistics of the second best algorithms (DT = 0.80) is almost double of the value of kappa statistics of another study DT J48 algorithm which was 0.4715. Normally, the value of kappa statistics ranges from − 1 up to + 1, meaning the value 1 indicates perfect classification -1 indicates wrong classification, i.e., all the classifications are occurred due to chance. Based on this range, the result of this study obtained from random forest, DT, pruned J48, and K-NN for classification of type-2 diabetes disease status lies on excellent classification and prediction capacity.The study conducted at United Arab Emirates in 2015 was found the higher kappa statistics result from naïve Bayes algorithm 0.50 followed by J48 DT which was 0.4720.As can be seen from the result table of Table 23.In addition, a review paper reviewed by Chui and his colleagues in 2017 showed that RF algorithm had an excellent capacity on classification and prediction of type-2 diabetes at a ROC curve of 0.98, which was, exactly similar with our study24 showed that a better classification performance by DT J48 at 65.3% of sensitivity. This result is even lower than the true sensitivity of our result obtained from DT J48 algorithm which was 96% of sensitivity. This difference in prediction and classification performance may be due to the difference in sample size used for data mining and the attributes used to classify and predict type-2 diabetes disease status. In the previous study, they have been conducted on 768 instances with eight attributes where as in our research study we used 2239 samples with eight attributes. This indicated that an increase in sample size will lead to better classification and prediction capability of these supervised machine learning algorithms’11.Another research work conducted on Classification of Diabetes patient by using Data Mining Techniques in 2018 by Nidhi and his colleagues19.From the result of confusion matrix showed on Table This study was carried out using an appropriate methodological approach with sufficient amount of sample size for data mining. Methodologically, the study covered seven supervised machine learning algorithms . The best three classifier and predictor models were selected for prediction of the upcoming new client for type-2 diabetes diagnosis. As a result, these points are considered as the strength of the study.On the other hand, this study was having the following limitations: firstly, since the study was conducted on secondary data, some of the important variables for classification and prediction of type-2 diabetic disease like glycated hemoglobin level were missed. The reason behind the removal of this important predictor variable is the occurrence of very few (only 2) diagnostic results recorded for this variable due to its expensiveness to perform the test. Secondly, while some of the record values of certain instances are missed; has been replaced with mean which affects the accuracy of prediction and classification performance, so that this also considered as limitation.In this study, seven supervised machine learning algorithms were implemented; compare their performance of classification and prediction for type-2 diabetes disease status from DHIS database at public hospitals of Afar region in 2021 for better decision making. Supervised machine learning algorithm is one of the essential aspects of hidden and domain-specific knowledge extraction to facilitate diagnostic activity for rapid and suggestive diagnosis and early intervention in the study area in particular and future generation in general.In this research study, we are considered the dataset of diabetes patients which is further collected at DHIS database of public hospital of Afar region. The dataset has 2239 instances with eight different selected attributes. In this study, seven major supervised machine learning algorithms  were simulated and found that the random forest, DT pruned J48 and K-NN have the maximum accuracy  respectively for classifying and predicting type-2 diabetes disease whether they are tested positive or tested negative.The constructed model could assist healthcare providers to make better clinical decisions for type-2 diabetes disease diagnosis. Additionally, the model could be further developed for patient protection through rapid diagnosis and early intervention to avoid further complications occurred due to type-2 diabetes. In the future, the results can be utilized to create a control plan for type-2 diabetes patients because diabetes patients are normally not identified until a later stage of the disease or the development of complications.Random forest supervised machine learning algorithm can be implemented for facilitating the diagnostic activity and early intervention.These results findings might be utilized to create a control plan for type-2 diabetes to prevent development of complications.The techniques used may be helpful to clinicians to develop an accurate and effective tool to make a better and suggestive decisions about type-2 diabete disease status of clients.Further study should be conducted based on primary data and by incorporating other predictor variables which were not considered in this study to detect the exact accuracy of these machine learning algorithms.Therefore, based on the above finding and conclusion, the following recommendations are forwarded: