Insulin resistance alters cortical inhibitory neurons and microglia to exacerbate Alzheimer’s knock-in mouse phenotypes

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1. 

   PREreview

   Dec 18, 2025

   This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17980807.

   Summary

               A growing body of work suggests that metabolic dysfunction, such as diabetes mellitus, can aggravate the cognitive decline of patients with Alzheimer's disease (AD); however, the molecular basis for the overlap in metabolic dysfunction in both Alzheimer's and diabetes mellitus is uncertain. Here, the authors investigate the contribution of metabolic dysfunction in a double knock-in (DKI) mouse model for Alzheimer's disease with mice expressing mutant human APP and wild-type tau protein.

               Using their DKI model, the authors compared two models of hyperglycemia, streptozotocin, which impairs insulin production, and the high-fat high-sugar (HFHS) diet, which …

   This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17980807.

   Summary

               A growing body of work suggests that metabolic dysfunction, such as diabetes mellitus, can aggravate the cognitive decline of patients with Alzheimer's disease (AD); however, the molecular basis for the overlap in metabolic dysfunction in both Alzheimer's and diabetes mellitus is uncertain. Here, the authors investigate the contribution of metabolic dysfunction in a double knock-in (DKI) mouse model for Alzheimer's disease with mice expressing mutant human APP and wild-type tau protein.

               Using their DKI model, the authors compared two models of hyperglycemia, streptozotocin, which impairs insulin production, and the high-fat high-sugar (HFHS) diet, which induces insulin resistance. Metabolic and cognitive tests revealed that streptozotocin was insufficient to induce obesity or exacerbate AD-related phenotypes. The HFHS diet was sufficient to induce obesity, and it decreased cognitive performance in DKI mice. These data support the authors' claim that metabolic stress driven by insulin resistance, rather than hyperglycemia, can exacerbate AD.

               Microglial morphology revealed greater infiltration in DKI mice, but there was no difference in DAM activation between diets. Instead, the authors identified AD-related DAM morphology remodeling in DKI mice, but this was mildly increased in DKI-HFHS mice. Trem2 was upregulated in both DKI mice, but it was concentrated in puncta only within DKI-HFHS mice. There was no difference between DKI-Lean and DKI-HFHS mice with respect to Aß deposition or tau phosphorylation. These data suggest that metabolic stress could alter microglial morphology and Trem2 expression, expanding our knowledge of the role Trem2 plays in neurodegeneration.

               The authors claim to identify a glial metabolic impairment state within DKI-HFHS mice that they term metabolic impairment in neurodegeneration (MinD). Single-nucleus RNA-seq identified a microglial sub-cluster that was predominantly enriched for DKI-HFHS microglia and 62 DEGs that were specific to the compound effects of diet and AD. A MinD-like state was observed in astrocytes and oligodendrocytes as well. Of the MinD state genes, the authors emphasized Nrg3 and claimed that its diet-and-disease-induced upregulation was responsible for decreased inhibitory synaptic density. These data provide strong support for the MinD state identified, but the evidence for Nrg3-mediated synaptic modulation is circumstantial.

               Most transcriptional changes found within neuronal populations were attributed to diet, because DEGs were commonly shared between WT-HFHS and DKI-HFHS mice. Using pathway analysis, the authors claimed that diet-induced stress alters inhibitory neuron excitability, but transcriptional data is insufficient to support this claim. These inhibitory neurons, later classified as L2/3 inhibitory neurons, showed dysregulated Meis2, a transcription factor important for pancreatic glucose dysregulation. Additionally, the authors focused on L2/3 excitatory neurons and found overlapping pathways suggestive of a diet-induced transcriptional program to alter synaptic function.

               In summary, the authors expand our understanding of the role of metabolic stress in AD pathology, but the current data is incomplete to support some of the authors' conclusions, such as the effects of Nrg3 upregulation, altered neuronal excitability, and the role of Meis2 in the proposed MinD state. Provided below are recommendations for further experimentation and textual clarification.

   Major Points

   ·         The title claims that insulin resistance exacerbates AD phenotypes through inhibitory neurons and microglia. The authors provide strong evidence of this diet-and-genotype interaction within microglia, but the majority of DEGs from inhibitory neurons are shared between WT- and DKI-HFHS nuclei. Please consider revising this title to more accurately represent the data in this report.

   ·         In Ext Figure 1, the authors claim that insulin deficiency is not sufficient to exacerbate AD-related phenotypes in this Alzheimer's knock-in mouse model. The authors could strengthen this claim by citing literature showing whether there is no increase in AD risk within T1DM individuals.

   ·         In Figure 2 / Ext Figure 3, the authors claim that metabolic stress alters microglial morphology. The effect sizes in the Scholl analysis provided were small, and the differences between mice were subtle. Providing more information about this experimental design in the methods, including the blinding scheme, will improve the interpretability of these results.

   ·         In Figure 4, the authors link the decrease in synaptic density to the upregulation of Nrg3 in glial cells and ErbB4 in inhibitory neurons. While this is believable, the data presented are insufficient to define a causal relationship between Nrg3/ErbB4 upregulation and decreased synaptic density. The authors could strengthen this claim by determining whether independent overexpression of Nrg3 is sufficient to decrease inhibitory synaptic density.

   ·         In Figure 5, the authors identified a HFHS-induced altered transcriptional program within inhibitory neurons that suggests altered excitability. While this is an exciting observation, this claim can be further supported by providing neurophysiological data to confirm altered excitability within these neurons.

   ·         In Figure 6, the authors link Meis2 upregulation to both pancreatic and brain metabolic impairment. While this could be supportive of the overall claim of the paper, the data provided is insufficient without the context-dependent impact of Meis2 dysregulation. Because Meis2 is a transcription factor, the authors could strengthen their claim by verifying whether the canonical targets of Mes2 are also dysregulated within the brain

   Minor Points

   ·         The authors make no reference to Figure 1 panels I and J within the text of the Results section. It appears that these data are important for displaying the combined effects of diet and genotype on AD phenotypes. Please provide an explanation for the data presented in these panels.

   ·         In Figure 2 panel D, the four separate lines on the graph are too thin and too faint to easily visualize the data. Please consider revising the formatting of this graph to improve readability.

   In Ext Figure 1, the authors explained that they tested the effects of streptozotocin on WT and DKI mice, but the figure legends are incorrectly labeled. For example, panels A, B, and C refer to HFHS diet conditions, and panel D references App^NLF/hTau-WT mice (presumably App^NLF/hTau-STZ). Please revise this figure to improve clarity.

   Competing interests

   The authors declare that they have no competing interests.

   Use of Artificial Intelligence (AI)

   The authors declare that they did not use generative AI to come up with new ideas for their review.

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2. 

   PREreview

   Dec 17, 2025

   This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17965991.

   In this manuscript, Nicholson et al., investigate how systemic metabolic dysfunction alters brain function in Alzheimer's disease (AD). Building on prior evidence that both hyperglycemia and insulin signaling deficits contribute to AD risk, a key question addressed by this study is whether it is insulin resistance itself or hyperglycemia alone that drives cognitive decline in AD, or vice versa.

   To address this question, the authors use two distinct metabolic perturbations to induce hyperglycemia in human mutant APP and wild-type tau knock-in (DKI) AD mouse model. One is administration of streptozotocin (STZ) to reduce insulin production, and the other is high-fat, high-sugar (HFHS) …

   This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17965991.

   In this manuscript, Nicholson et al., investigate how systemic metabolic dysfunction alters brain function in Alzheimer's disease (AD). Building on prior evidence that both hyperglycemia and insulin signaling deficits contribute to AD risk, a key question addressed by this study is whether it is insulin resistance itself or hyperglycemia alone that drives cognitive decline in AD, or vice versa.

   To address this question, the authors use two distinct metabolic perturbations to induce hyperglycemia in human mutant APP and wild-type tau knock-in (DKI) AD mouse model. One is administration of streptozotocin (STZ) to reduce insulin production, and the other is high-fat, high-sugar (HFHS) diet-induced insulin resistance. They demonstrate that both STZ and HFHS treatments induce hyperglycemia, but only HFHS-induced insulin resistance leads to learning and memory deficits. Although overall Aβ deposition and tau phosphorylation remain unchanged, HFHS diet can induce microglia morphological remodeling in DKI mice.

   To further investigate the glia regulation under HFHS, they perform single-nucleus RNA-seq to identify a unique glial metabolic impairment state (MinD), while L2/3 Meis2+ inhibitory neurons and excitatory neurons display convergent transcriptional changes affecting synaptic organization and trans-synaptic signaling. These results suggest HFHS-induced insulin resistance, rather than hyperglycemia alone, may exacerbate AD-related functional deficits through cell type specific transcriptional remodeling in both glia and neurons, with consequences on synaptic organization and inhibitory signaling, independent of classical Aβ or tau pathology.

   Overall, this study provides a valuable and detailed descriptive framework for how metabolic dysfunction modulates AD progression. The identification of glial MinD states and Meis2⁺ inhibitory neuron sensitivity represents an advance, but the evidence linking insulin resistance to cognitive decline remains primarily correlative. Thus, the strength of support for this conclusion is suggestive but not definitive, and additional mechanistic studies would help clarify mechanistic insights and strengthen the central claims.

   Major comments:

   1.     Behavioral testing was performed at a single timepoint, and snRNA-seq was done after behavioral deficits, which is reasonable to capture transcriptional changes associated with the phenotype, but it limits the ability to determine whether these changes are causal or secondary. Multiple timepoints would better clarify causality and dynamic progression (Fig. 1, Ext Fig. 1 and Ext. Fig. 2). Additionally, the authors used one behavior assay, which may give a limited view of cognitive function. Adding a second assay, such as novel object recognition, would help confirm whether the impairment is specific to one assay and could help strengthen the conclusions.

   2.     Immunofluorescence analysis was performed only in the cortex and did not include the hippocampus. Given that the hippocampus is critical for the cognitive functions assessed, including hippocampal immunofluorescence data, or briefly discussing why it was not examined would improve the study (Fig. 2a-e).

   3.     The rationale for using different control groups for comparisons in Fig. 3 is unclear. Fig. 3d-g analyses use DKI-HFHS vs DKI-Lean to identify 62 uniquely altered genes in DKI-HFHS microglia, whereas Fig. 3h compares DKI-HFHS vs WT-Lean. The authors should clarify the rationale for using different reference groups and how these choices affect the interpretation of results, which would help the reader interpret which transcriptional changes are attributable to HFHS diet alone, AD pathology alone, or combined metabolic. This clarification would enhance the internal consistency of the analysis and reduce potential ambiguity about how HFHS-specific signatures are defined.

   4.     Fig. 4h, inhibitory synapse density is only compared between WT-HFHS and DKI-HFHS. Including DKI-Lean or WT-Lean would clarify the effect of HFHS diet alone. Clarifying why different comparisons are used across Fig. 4a–e versus Fig. 4g–h would help the reader distinguish the contributions of HFHS diet versus AD pathology to synaptic changes, improving the interpretability of the data and supporting conclusions about the specific impact of insulin resistance-induced metabolic stress on inhibitory synapses.

   5.     Neuregulin 3 (Nrg3) emerged as a candidate linking glia transcriptional shifts to inhibitory synaptic signaling. Since Nrg3 primarily binds to ErbB4, which is enriched at inhibitory synapses, the authors may consider manipulating either Nrg3 or ErbB4 to test whether Nrg3-ErbB4 signaling mediated the synaptic deficits observed in Fig. 4. or briefly discuss this pathway as a potential direction for future mechanistic investigation.

   6.     Fig. 6 shows that HFHS induces Meis2 upregulation in Layer 2/3 inhibitory neurons, but it is unclear whether this change contributes to cognitive deficits. The authors may consider manipulating Meis2 expression specifically in these neurons and assess effects on synapse and behavior. The experiment is not essential for the current descriptive analysis, but it could clarify the causal link between metabolic stress and cognitive impairment. Alternatively, adding a short discussion about whether Meis2 may play a functional role in metabolic vulnerability could further contextualize the findings.

   7.     Electrophysiology recordings targeting L2/3 Meis2+ neurons and excitatory neurons would significantly strengthen the causal inference between HFHS and synaptic deficits shown in Fig. 6 and Fig. 7. While not necessary for the current manuscript, the authors could briefly discuss this as a future direction to test functional consequences of transcriptional changes.

   Minor comments:

   1.     Ext Fig1, Panels a-c appear mislabeled regarding WT-Lean, WT-HFHS, DKI-Lean and DKI-HFHS, AppNLF/hTau WT label also appears incorrect in Ext Fig 1d. To improve readability, the authors should consider verifying these labels.

   2.     The figure referenced for the statement on line 17 of page 3"Performance in the visible platform task remained consistent across all groups (Fig. 1d)" appears to be incorrect. Please verify and update the figure citation to accurately reflect the data supporting this claim.

   3.     Ext Fig. 5f, comparing WT-Lean to DKI-Lean would clarify whether DKI mice exhibit synaptic deficits independent of HFHS diet. Performing statistical comparisons across all groups (WT-Lean, WT-HFHS, DKI-Lean, DKI-HFHS) would allow the authors to distinguish the contributions of genotype versus diet and better communicate which changes are specifically driven by HFHS-induced metabolic stress versus AD pathology. This would improve the interpretability of the synaptic data and strengthen the link between metabolic perturbation and synaptic alterations.

   4.     Nrg3 is upregulated in microglia, astrocytes, and oligodendrocytes based on snRNA-seq data. Immunofluorescence staining validation to confirm the protein expression changes with cell-specific localization is suggested.

   5.     The figure citation for the statement on line 40 of page 4 "First, we focused on microglia to identify transcriptional changes that may underlie the observed Trem2 alterations, despite the equal number of microglial nuclei in the DKI-Lean and DKI-HFHS groups" appears to be incorrect. Please verify and update the figure reference (currently listed as Extended Data Fig. C) to accurately reflect the relevant data.

   6.     The figure citation for the statement on line 1 of page 5, "We next cross-referenced gene expression within the MG3 cluster against a curated list of microglia state markers, which were stratified by WT and DKI groups based on disease-associated activation (Extended Data Fig. d)" is incorrect. Please doublecheck.

   7.     The authors should cite relevant literature regarding whether patients with Type 1 diabetes have cognitive decline or an increased risk of AD (10.1038/s41598-024-53043-x; 10.1007/s10654-023-01080-7; 10.1002/brb3.3533), which is important to clarify whether STZ-induced findings in AD mouse model are specific to mouse or supported by human data. The authors should provide a discussion addressing this point, integrating existing data to contextualize their findings.

   Competing interests

   The author declares that they have no competing interests.

   Use of Artificial Intelligence (AI)

   The author declares that they did not use generative AI to come up with new ideas for their review.

   Read the original source
3. 

   PREreview

   Dec 17, 2025

   This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17969041.

   In this manuscript, Nicholson et al. investigated whether hyperglycemia caused by decreased insulin vs insulin resistance drives AD pathology. The interest in addressing this question stems from the observation that type 2 diabetes and people with metabolic syndrome (MetS) show early onset of Alzheimer's disease (AD). To address this question, the authors performed two types of interventions, separately, in a double knock-in clinical-relevant AD mouse model: 1) the administration of streptozotocin (STZ) to destroy beta cells and mimic type 1 diabetes, and 2) a high-fat, high sugar (HFHS) diet to mimic type 2 diabetes. This design allows them to parse out the respective contributions of …

   This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17969041.

   In this manuscript, Nicholson et al. investigated whether hyperglycemia caused by decreased insulin vs insulin resistance drives AD pathology. The interest in addressing this question stems from the observation that type 2 diabetes and people with metabolic syndrome (MetS) show early onset of Alzheimer's disease (AD). To address this question, the authors performed two types of interventions, separately, in a double knock-in clinical-relevant AD mouse model: 1) the administration of streptozotocin (STZ) to destroy beta cells and mimic type 1 diabetes, and 2) a high-fat, high sugar (HFHS) diet to mimic type 2 diabetes. This design allows them to parse out the respective contributions of hyperglycemia and metabolic dysfunction to AD pathology. The authors then performed an extensive comparison across these models to define the role of metabolic dysfunction in contributing to the cognitive deficits independent of hyperglycemia. Using single-nucleus RNAseq, they identified transcriptional changes in glial populations that they call metabolic impairment in neurodegeneration (MinD) state which is really interesting, and this provided some insight on a few pathways (including Nrg3-pathway) in metabolic dysfunction that could be contributing to neurodegeneration. We have several recommendations for the authors to strengthen the conclusions drawn from the evidence presented and improve data presentation.

   Major:

   1. The conclusion regarding slower learning in STZ-treated mice (Ext fig.1) in the Morris Water Maze test is unclear. The authors state that "STZ-treated WT and AD mice exhibited longer latencies to locate the hidden platform during initial acquisition and reversal learning (Extended Data Fig. 1d, g)." However, in Ext Fig 1d, g, the latency difference is mainly observed between WT (blue and yellow) and AD mice (green and pink). Based on the figure, STZ treatment does not appear to induce changes in either WT mice or AD mouse model. To help clarify, the authors are encouraged to evaluate their statistical approach to see if it is the best fit for the data presented. A repeated measures ANOVA may be a good fit.

   2. The authors conclude that "cognitive outcomes are shaped by metabolic stress driven by insulin resistance, rather than hyperglycemia being the primary factor" based on the observations that mice with HFHS diet showed increased body weight but mice with STZ did not, although both groups exhibited hyperglycemia (Fig 1a, ext Fig 1a). While the results show that hyperglycemia alone doesn't induce cognitive deficits, claiming that metabolic stress (rather than hyperglycemia) drives cognitive decline requires more direct evidence. Specifically, the conclusion that STZ mice do not exhibit metabolic stress needs evidence. The authors could consider either revise their wording to make the claim more precise or provide additional evidence demonstrating that STZ mice did not experience metabolic stress despite having hyperglycemia.

   3. The HFHS diet interaction with Trem2 is quite interesting, since Trem2 has been shown as PD risk factor. However, the absence of significant difference in synaptic density between DKI-HFHS vs DKI-lean mice raises some concern about statistical power. Since the effect size between WT and DKI appear small in this case, could the authors estimate or perform a power calculation to see how many samples would be needed to detect such a small difference between either DKI-lean vs WT or DKI-lean vs DKI-HFHS.

   4. In both Fig3 f and Ext. Fig 7d, when the authors showed DEGs for DKI-HFHS and MinD State, the DKI-HFHS changes are going in the opposite direction of changes in either WT-HFHS or DKI-lean mice. These data raise the question of whether these MinD genes play a protective or disease-exacerbating role. The authors should consider discussing what role these genes played.

   5. When the authors look into the Meis2_ IN population, the data support an enrichment of Meis2+ neurons (Fig 6e). However, when examining the specific role that these neurons contribute to the AD pathology, it is important to compare the unique changes detected in DKI-HFHS vs WT-HFHS mice to gain insight into whether and how this population is altered between healthy and disease states in response to HFHS challenge.

   6. The authors mentioned that it is known that MEIS2 is a transcription factor that regulates gene expression upon glucose dysregulation. To strengthen their conclusion, the authors can assess some of the target genes that are highly regulated by MEIS2. The authors can analyze the binding motif for MEIS2 to identify targets.

   7. Gene ontology analysis cannot conclude that excitability is a defining feature of InN3 neurons under metabolic stress without additional experimental data. To address this point, the authors can either comment on this limitation in the discussion or support it with electrophysiological data to show the excitability change.

   8. The argument regarding MG3 cluster increase needs to be supported with a statistical test (for example: propeller method) for the percentage of MG3 in the microglial population. This method would account for high sample-to-sample variability from single cell data.

   9. Since the authors use mouse data to understand human disease, it would be helpful for the authors to provide context from the existing literature in the discussion about the link between T2DM and AD risk.

   10. Lastly, the authors conclude that the HFHS diet compromises cognition independent of Abeta pathology and that cognitive loss is purely secondary to metabolic stress (in the second result and conclusion). This conclusion should be qualified to acknowledge the limitation of mouse model. In humans with T2DM or metabolic syndrome, patients are known to have small vessel disease that could either aggravate the effects or might lead to APP pathology only in the human context (PMID:30106209, PMID: 29686024). The authors could discuss how these human-specific vascular pathologies may limit the translatability of their findings and revise their conclusion to reflect that the observed independence from Aβ pathology may not fully capture the complexity of metabolic syndrome-associated cognitive decline in human patients.

   Minor:

   1. Can the authors clarify the "homozygous App^NL-F/Mapt^hMAPT" notation? From what I interpreted, this represents a double knock-in. If the mice are homozygous for both knock-in alleles, consider specifying the full genotype as App^NL-F/NL-F;Mapt^hMAPT/hMAPT to improve clarity.

   2. There are a lot of places where figures are referenced incorrectly throughout the manuscript. The authors need to review and correct these references. Specific instances are given below:

   a. In ext Fig1, the label for panel a indicates "WT-lean, WT-HFHS, DKI-lean and DKI HFHS". Based on the text, it seems that STZ manipulation is independent of the HFHS diet manipulation. Could the authors clarify whether this is a typo in the legend, or whether these mice were indeed treated with both STZ + HFHS diet?

   b. In ext Fig1 d, the pink legend. Should this be App/hTau STZ rather than WT? Please double check this labeling.

   c. The authors state that "All groups performed similarly in the visible platform test, confirming intact visual acuity and motor function (Fig. 1d)." However, Fig 1d shows blood glucose levels, not behavioral performance data.

   d. My comments are indicated in brackets within the quoted text:

   "We next assessed the phenotypic effects of diet-induced insulin resistance in the HFHS cohort (Fig. 1a [Figure reference issue: 1a shows the graphic design only, the citation should likely be entire Fig. 1] and Extended Data Fig. 2). Unlike STZ-treated mice, both WT and homozygous AppNL-G-F/MapthMAPT (DKI) mice on a chronic HFHS diet exhibited significant weight gain compared to lean-diet-fed controls (Fig. 1b [Fig.1b is just a picture, 1c is the data]). Within four weeks, body weights were significantly different from those of lean-diet mice and continued to rise over 16 weeks, accompanied by elevated resting glucose levels (Fig. 1c [1d is the glucose, 1c is body weight]), indicating insulin resistance-induced hyperglycemia. Glucose tolerance tests showed impaired glucose clearance in HFHS-fed mice compared to lean-fed mice (Fig.1e), confirming the establishment of a type 2 diabetes phenotype in HFHS diet mice" [lacking statistical comparison].

   e. Fig 1 i and j are not mentioned in the text.

   f. "First, we focused on microglia to identify transcriptional changes that may underlie the observed Trem2 alterations, despite the equal number of microglial nuclei in the DKI-Lean and DKI-HFHS groups (Extended Data Fig. C)." Which Figure? Ext Fig 6C? I also suggest referencing Ext Fig. 6C when first mentioning the number of glial population frequencies for clarity.

   g. "We next cross-referenced gene expression within the MG3 cluster against a curated list of microglial state markers33, which were stratified by WT and DKI groups based on disease-associated activation (Extended Data Fig. d)."

   Please specify the figure number.

   3. In different experiments, either APPNL-F mice or APPNL-G-F mice were chosen to combine with the MapthMAPT mice. For clarity, a sentence mentioning the rationale for their mouse model choice would be helpful.

   4. When the authors discuss the MapMyCell results (Page4), the referenced supplemental table (supplemental table 1) is incorrect. Please double check the reference and link the correct table.

   5. When showing DEG numbers (where pink refers to upregulated genes) and in heatmaps (where pink refers to downregulation), it would be more consistent if the authors could use the same color scheme throughout.

   6. There is no mention of whether the experimenter is blinded to the genotype when performing the Sholl analysis in Fig. 2d-e; Extended Data Fig. 3a.

   7. Moreover, the snRNAseq data argue against Trem2 being different due to the diet (The MG3 is low in Trem2 expression). Could the authors elaborate further if they believe the microglial morphology and the shift in Trem2 location are independent of the Trem2 level?

   Competing interests

   The authors declare that they have no competing interests.

   Use of Artificial Intelligence (AI)

   The authors declare that they did not use generative AI to come up with new ideas for their review.

   Read the original source
4. 

   Version published to 10.1101/2025.09.05.674487 on bioRxiv

   Sep 11, 2025