You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Lucia Chehade and Alexandra Tierney authors are contributed equally to the work.

**Funding information**

RK was supported by Cure SMA/Families of SMA Canada; Muscular Dystrophy Association (USA) (grant number 575466); and Canadian Institutes of Health Research (CIHR) (grant number PJT‐156379). The Vanderbilt Mouse Metabolic Phenotyping Center was supported by National Institutes of Health grant DK59637. MOD was supported by Frederick Banting and Charles Best CIHR Doctoral Research Award.

Introduction {#acn350920-sec-0001}
============

Spinal muscular atrophy (SMA) is a devastating neurological disease affecting patients of all ages.[1](#acn350920-bib-0001){ref-type="ref"} Commonly affecting young infants, SMA results in motor neuron loss leading to generalized weakness and paralysis.[1](#acn350920-bib-0001){ref-type="ref"} The genetic basis of SMA is deletion or mutation in the ubiquitously expressed *Survival motor neuron 1* (*SMN1*) gene,[2](#acn350920-bib-0002){ref-type="ref"} which encodes a protein (SMN) involved in housekeeping functions, including RNA metabolism and splicing.[3](#acn350920-bib-0003){ref-type="ref"}

Over time, defects in multiple non‐neuronal cell types have been identified in SMA.[4](#acn350920-bib-0004){ref-type="ref"}, [5](#acn350920-bib-0005){ref-type="ref"} Metabolic defects were suspected even prior to the identification of the SMA disease gene.[6](#acn350920-bib-0006){ref-type="ref"} In early clinical studies, the presence of dicarboxylic aciduria, reduced beta‐oxidation capacity, and a case report of fatty liver highlighted the potential deficits in fatty acid metabolism.[6](#acn350920-bib-0006){ref-type="ref"}, [7](#acn350920-bib-0007){ref-type="ref"}, [8](#acn350920-bib-0008){ref-type="ref"} Recently, we identified the increased susceptibility of SMA patients and preclinical models to develop dyslipidemia and fatty liver.[9](#acn350920-bib-0009){ref-type="ref"} More specifically, 13% of a SMA patient cohort showed alterations in more than three common measures of dyslipidemia (total cholesterol, low‐density lipoprotein (LDL), high‐density lipoprotein (HDL), triglycerides (TGs), and non‐HDL cholesterol), which is a greater prevalence than in the normal population.[10](#acn350920-bib-0010){ref-type="ref"}, [11](#acn350920-bib-0011){ref-type="ref"} Moreover, about one‐third of patients\' autopsies showed liver steatosis (commonly known as fatty liver), a feature that less than 0.7% of 2‐ to 4‐year‐old normal children harbor.[12](#acn350920-bib-0012){ref-type="ref"} In accordance with these clinical findings, all *Smn* ^2^ *^B/−^* mice, a commonly used model of SMA that lives about 25 days of age, developed a nonalcoholic fatty liver phenotype and dyslipidemia.[9](#acn350920-bib-0009){ref-type="ref"} These findings raise concerns for additional comorbidities such as metabolic syndrome, cardiovascular disease, and stroke in a subset of SMA patients as they age.

Here, we show that low fat diets were sufficient to double lifespan in *Smn^2B/−^* mice, without any reduction in the increased hepatic TG content, which suggests that there was no significant change in the fatty liver pathology. Liver function, such as protein output, also showed no amelioration. Some improvements were identified in liver damage markers, and in glucagon‐like peptide 1 (GLP‐1) and ketone levels, which trended toward normal in low fat and high‐sucrose diet (HSD). Altogether, this work highlights the need for additional controlled nutritional preclinical and clinical research. Nutritional guidelines specific to SMA should then be further developed from these evidence‐based studies.

Materials and Methods {#acn350920-sec-0002}
=====================

Mouse models {#acn350920-sec-0003}
------------

The *Smn^2B/−^* (C57BL/6J background)[13](#acn350920-bib-0013){ref-type="ref"} mice were housed at the University of Ottawa and cared for according to the Canadian Council on Animal Care. Tissues were collected ad libitum between 9 and 11 am to limit the effect of the circadian rhythm.

Diet modulation {#acn350920-sec-0004}
---------------

Dams were provided excess normal chow (NC) (Teklad Global 18% Protein Rodent Diet), high‐fat diet (HFD) (Research Diets D12492), low‐fat diet (LFD) (Research Diets D12450J), or high‐sucrose/low‐fat diet (HSD) (Research Diets D12450B) at the bottom of the cage 2 weeks after putting the breeding pair together, which was changed every 2 days. Following weaning, mice were provided with the diets until endpoint. At least three males and three females were included in each group to account for sex differences.

Gene expression studies {#acn350920-sec-0005}
-----------------------

RNA extraction, cDNA reverse transcription, and qPCR were performed as previously published.[9](#acn350920-bib-0009){ref-type="ref"} Primers are as follows: Ywhaz forward 5' AAG ACA GCA CGC TAA TAA TGC 3', reverse 5' TTG GAA GGC CGG TTA ATT TTC 3', hprt1 forward 5' CCC AGC GTC GTG ATT AGT GAT G 3', reverse 5' TTC AGT CCT GTC CAT AAT CAG TC 3', and plin2 forward and reverse primer were ordered from primePCR (Biorad ‐ qMmuCIP0033479). Results were normalized with two genes identified as appropriate stable internal reference given M value below 0.5 and coefficient of variance below 0.25.

Immunoblotting {#acn350920-sec-0006}
--------------

Western blots were performed as previously described.[9](#acn350920-bib-0009){ref-type="ref"} Results were normalized to total protein.

Lipid quantification and blood chemistry {#acn350920-sec-0007}
----------------------------------------

Tissue lipid analysis was performed at Vanderbilt Mouse Metabolic Phenotyping Center.[9](#acn350920-bib-0009){ref-type="ref"} All blood collected in this study was sampled randomly (i.e., no fasting period) between 9 and 11 am. Alanine aminotransferase (ALT), total protein, and non‐esterified fatty acids (NEFA) were assessed at Comparative Clinical Pathology Services, LLC., Columbia, Missouri. Glucagon and GLP‐1 quantification were outsourced to Eve Technologies Corp. (Calgary, Alberta).[9](#acn350920-bib-0009){ref-type="ref"} Ketone and glucose were analyzed with Freestyle Precision Neo Blood Glucose and Ketone monitoring system.

Statistics {#acn350920-sec-0008}
----------

Data are presented as the mean ± standard error of the mean. One‐way ANOVA analysis was used with Tukey as posttest. Survival curves were compared using a Mantel‐Cox test. Statistical analysis was performed in GraphPad Prism. Grubbs test (alpha = 0.05) was performed to identify outliers, which were removed from the analysis. Significance was set at *P* ≤ 0.05 for \*, *P* ≤ 0.01 for \*\*, *P* ≤ 0.001 for \*\*\*, and *P* ≤ 0.0001 for \*\*\*\*. *N* number for each experiment is as indicated in the figure legends.

Data and materials availability {#acn350920-sec-0009}
-------------------------------

Raw data can be provided upon request.

Results {#acn350920-sec-0010}
=======

low‐fat diet leads to increased survival without a correction of hepatic triglyceride or liver function {#acn350920-sec-0011}
-------------------------------------------------------------------------------------------------------

We have previously shown that the *Smn^2B/−^* mice develop fatty liver, with over 25‐fold more TGs in their liver than control mice.[9](#acn350920-bib-0009){ref-type="ref"} At this time, it is unclear whether this is contributing to disease severity and whether it could be modulated through pharmacological or dietary means. Dietary intake plays an important part in liver lipid accumulation.[14](#acn350920-bib-0014){ref-type="ref"}, [15](#acn350920-bib-0015){ref-type="ref"} Additionally, many families find subjective clinical benefit for SMA children in adhering to a low‐fat diet called the "amino acid diet" (<http://www.aadietinfo.com>). As such, we tested three different diets: HFD, LFD, and HSD, which were compared to standard NC (Fig. [1](#acn350920-fig-0001){ref-type="fig"}). Given the short lifespan of *Smn* ^2^ *^B/−^* mice, diets were administered to the dams until the pups could freely feed on their own. It has previously been shown that milk of rat dams fed HFD had higher fat content than those on normal chow by 21 days postpartum, while their offspring showed increased weights, change in body composition, and metabolic abnormalities.[16](#acn350920-bib-0016){ref-type="ref"} Surprisingly, introduction of LFD and HSD doubled life expectancy of *Smn^2B/−^* mice in comparison to normal chow (median lifespan of 38 and 39 vs. 21 days, respectively) (Fig [2](#acn350920-fig-0002){ref-type="fig"}A). Note, both LFD and HSD have similar fat and carbohydrate proportion as energy source but differ significantly in the type of carbohydrate they provide (Fig. [1](#acn350920-fig-0001){ref-type="fig"}). HFD (median lifespan of 22 days) did not lead to improvement (Fig. [2](#acn350920-fig-0002){ref-type="fig"}A). Interestingly, the weight of the mice, SMN levels, or hepatic fat content (TG levels and plin2 mRNA levels[17](#acn350920-bib-0017){ref-type="ref"}) were only marginally changed regardless of diet (Fig. [2](#acn350920-fig-0002){ref-type="fig"}B--E). Hyperglucagonemia was previously observed in the *Smn^2B/−^* mice.[18](#acn350920-bib-0018){ref-type="ref"} While plasma glucagon levels remain constant (Fig. [2](#acn350920-fig-0002){ref-type="fig"}F), a reduction of GLP‐1 in the HSD group, a resultant protein from the proglucagon produced in the intestines, was observed (Fig. [2](#acn350920-fig-0002){ref-type="fig"}G). We have previously noticed sustained low glucose[9](#acn350920-bib-0009){ref-type="ref"} and high circulating NEFA, a product from peripheral lipolysis of white adipose tissue (data not shown). We next speculated that LFD and HSD may simply restore appropriate proportions of energy substrate for usage, by reducing circulating fat and increasing glucose. Indeed, we noted a modest increase in plasma glucose levels but sustained NEFA in *Smn^2B/−^* mice on the HSD regimen (Fig. [2](#acn350920-fig-0002){ref-type="fig"}H,J). More importantly, plasma ketones were reduced in both LFD and HSD cohorts, implying reduced beta‐oxidation and a lower reliance on fatty acids as an energy source (Fig. [2](#acn350920-fig-0002){ref-type="fig"}I). Interestingly, liver damage was diminished in *Smn^2B/−^* fed LFD and HSD diets in comparison to *Smn^2B/−^* mice on normal chow, as assessed by plasma ALT levels (Fig. [2](#acn350920-fig-0002){ref-type="fig"}K). However, low hepatic function remained as total protein production (Fig. [2](#acn350920-fig-0002){ref-type="fig"}L) and *Igf1* mRNA production (data not shown) were unchanged amongst the different *Smn^2B/−^* diet cohorts. Altogether, diet modulation can significantly improve the lifespan of *Smn^2B/−^* mice, likely by altering mitochondrial bioenergetic substrate utilization, without influence on hepatic fat accumulation.

![Composition of diets used in this study. Energy sources for normal chow (A), high‐fat diet -- Research Diets D12492 (B), high‐sucrose diet -- Research Diets D12450B (C), and low‐fat diet -- Research Diets D12450J (D) are presented. The diet content is shown for normal chow (E), high‐fat diet -- Research Diets D12492 (F), high‐sucrose diet -- Research Diets D12450B (G), and low‐fat diet -- Research Diets D12450J (H).](ACN3-6-2340-g001){#acn350920-fig-0001}

![Low fat diets enhance survival by switching cell metabolism away from fat as an energy source. (A) LFD and HSD lead to a median survival of 38 and 39 days for *Smn^2B/−^* mice in comparison to 21 and 22 days when fed NC and HFD, respectively. (B--E) Weight, hepatic SMN, hepatic triglycerides, and plin2 mRNA levels were unchanged by the introduction of different diets in *Smn^2B/−^* mice. (F) Glucagon was further elevated by HFD diet in *Smn^2B/−^* mice while no change was observed by the introduction of other diets. (G) GLP‐1 was considerably diminished upon administration of HSD but not with other diets. (H) Glucose was mostly unchanged by diet modulation, albeit slightly elevated in HSD group. (I) Levels of ketone bodies were enhanced further by introduction of HFD, significantly diminished by LFD diet, while back to normal with the HSD diet in *Smn^2B/−^* mice. (J) Non‐esterified fatty acids were unchanged upon diet modulation, albeit slightly reduced in the HSD cohort. (K) Significant reduction in plasma ALT levels was observed in LFD and HSD cohort, supporting reduced hepatocyte damage. (L) Liver function, as shown by total protein output, was not restored in LFD and HSD cohorts**.** qPCR data were normalized with HPRT1 and Ywhaz in (E) (*N* value for each experiment is as follows: *N* = 10--19 for A‐B, 3‐6 for C--G & J--M, 10--20 for H‐I, one‐way ANOVA with Tukey\'s correction, Grubbs test (alpha -- 0.05) for outliers, *P* ≤ 0.05 for \*, *P* ≤ 0.01 for \*\*, *P* ≤ 0.001 for \*\*\* and *P* ≤ 0.0001 for \*\*\*\*).](ACN3-6-2340-g002){#acn350920-fig-0002}

Discussion {#acn350920-sec-0012}
==========

Consensus on standards of care in SMA[19](#acn350920-bib-0019){ref-type="ref"}, [20](#acn350920-bib-0020){ref-type="ref"} repeatedly highlighted the need for more research in SMA metabolism. Despite this, optimal nutritional guidelines for SMA patients are still lacking. Currently, many SMA patients are either malnourished, underfed, or overfed.[21](#acn350920-bib-0021){ref-type="ref"}, [22](#acn350920-bib-0022){ref-type="ref"} Over the years, many SMA families have adopted the "amino acid diet." This diet was developed purely based on observations and experiences of caregivers, and consists of reduced fat intake consumption (10--20%) and elemental free amino acid formula amongst other components. Nevertheless, scientific and preclinical evidence on the benefit of this diet is lacking.

In our study, it was intriguing that low‐fat/low‐sucrose diet or low‐fat/HSD led to doubling of the lifespan in a mouse model of SMA. Interestingly, it was previously speculated that a high‐carbohydrate/low‐fat diet would provide energy substrates that do not depend on fatty acid oxidation, keep the level of free fatty acids under control, and diminish production of dicarboxylic acid in the circulation -- factors thought to have toxic potential to SMA patients.[7](#acn350920-bib-0007){ref-type="ref"}, [23](#acn350920-bib-0023){ref-type="ref"} This is consistent with some aspects of the "amino acid" diet. A LFD/high‐carbohydrate diet was tested in 13 SMA patients in the 1990s.[24](#acn350920-bib-0024){ref-type="ref"} While the authors claimed a beneficial outcome, there was controversy about the classification scheme used, given the lack of genetic diagnostic tools.[7](#acn350920-bib-0007){ref-type="ref"} Hence, the interpretation of the results is difficult[7](#acn350920-bib-0007){ref-type="ref"} and the study has not since been reproduced. One previous study concluded that higher fat content may confer protective benefits.[25](#acn350920-bib-0025){ref-type="ref"} However, the diets used consisted of two different chows with many differences amongst them, did not control exclusively for the fat content, consisted of only a 5% fat difference, and showed marginal difference in survival (3 days).[25](#acn350920-bib-0025){ref-type="ref"} In our study, the use of "research diets" minimized the effect of other substances while maximizing the differences in fat intake or sucrose, especially fat content. Nevertheless, it is unknown whether the full extent of the nutritional content of each diet was carried in the milk of the dams. On another note, it was interesting to note that hepatic TGs did not change significantly upon diet modulation. However, it is thought that dietary lipids only contribute to 15% of lipid accumulation in the liver in nonalcoholic fatty liver disease.[26](#acn350920-bib-0026){ref-type="ref"} As such, it appears that diet modulation simply decreases fatty substrate availability coming directly from dietary intake, diminishing the load on fatty acid oxidation, as shown by normalization of ketone bodies. While we were able to identify a clear difference between high‐ and low fat diets (LFD and HSD vs. HFD), there is a more subtle difference apparent between the effects of LFD and HSD. This may be attributed to the different composition of carbohydrate in the respective diets, even though the carbohydrate provided the same equivalence of energy. HSD contains sucrose as its major carbohydrate while the LFD major carbohydrate was cornstarch. This difference may impact the ease of use of the sugar and/or the underlying defects in the pancreas[18](#acn350920-bib-0018){ref-type="ref"} and the propensity for low blood sugar in the *Smn^2B/−^* mice and patients.[9](#acn350920-bib-0009){ref-type="ref"} Overall, this study suggests that supplementation of nonfatty substrates for energy use is a key beneficial determinant of survival. Our findings strongly suggest that clinical nutritional guidelines need to be established from evidence‐based research to provide better care for SMA patients.

Author Contribution {#acn350920-sec-0013}
===================

MOD designed study, performed experiments, main contributor for Figure [2](#acn350920-fig-0002){ref-type="fig"}. Analyzed data and created all figures. Wrote the manuscript. AT performed and provided support for experiments in Figure [2](#acn350920-fig-0002){ref-type="fig"}. LC provided support for experiments in Figure [2](#acn350920-fig-0002){ref-type="fig"}. AB provided support for experiments. RK designed study and prepared manuscript.

Conflict of Interest {#acn350920-sec-0015}
====================

Marc‐Olivier Deguise received honoraria and travel accommodations by Biogen for the SMA Summit 2018 held in Montreal and the SMA academy 2019 held in Toronto, Canada. RK and the Ottawa Hospital Research Institute have a licensing agreement with Biogen for the *Smn^2B/−^* mouse model. These COI are outside the scope of this study. All other authors have no competing interests to declare.

We extend our gratitude to Sabrina Gibeault, My Tran Trung, and Rebecca Yaworski, and the Animal Care and Veterinary Services staff of the University of Ottawa for their assistance with experiments. RK was supported by Cure SMA/Families of SMA Canada; Muscular Dystrophy Association (USA) (grant number 575466); and Canadian Institutes of Health Research (CIHR) (grant number PJT‐156379). The Vanderbilt Mouse Metabolic Phenotyping Center was supported by NIH grant DK59637. MOD was supported by Frederick Banting and Charles Best CIHR Doctoral Research Award.