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]


Introduction
============

In recent years, diabetes mellitus (DM) has become a national health epidemic. The Centers for Disease Control and Prevention reports that West Virginia has one of the highest rates of diabetes in the United States with more than 11% of the population affected. The Framingham Heart Study revealed that the risk of heart failure is up to 5 times higher in diabetics than non-diabetics, when controlling for other risk factors. Diabetic Cardiomyopathy (DCM) is an established complication of diabetes [@B1]-[@B6] that involves abnormal relaxation of the ventricles, referred to as diastolic dysfunction, with concurrent hypertrophy of cardiomyocytes [@B6], [@B7]. Diastolic dysfunction is thought to be the first functional abnormality in DCM and can be seen in 40-60% of asymptomatic diabetic patients through echocardiographic imagining studies [@B2], [@B6]. Diabetic patients with subclinical diastolic dysfunction have a 5-year mortality rate of 30.8% compared to 12.1% for diabetic patients with no diastolic dysfunction [@B4]. As DCM enters its later stage, it progresses from diastolic dysfunction to overt stage C heart failure with preserved ejection fraction, which has no proven effective treatment [@B7], thus validating the importance of identifying biomarkers that can improve detection of DCM prior to the onset of irreversible complications.

Diabetes impairs glucose uptake and results in an increase in fatty acid (FA) metabolism in cardiac tissue [@B3], [@B8], [@B9]. In diabetes, decreased insulin signaling activates transcriptional signaling pathways that induce the expression of genes involved in stimulating FA uptake; however the uptake of FAs exceeds metabolic demand and results in triglyceride and cholesterol accumulation in the myocardium which impairs diastolic function [@B8]-[@B11]. A study by McGavock et al compared normoglycemic individuals with diabetic patients and confirmed a positive correlation between impaired glucose tolerance and myocardial triglyceride content and found that triglyceride accumulation preceded the onset of ventricular dysfunction [@B11]. Abnormal FA metabolism also leads to depressed levels of high-density lipoprotein (HDL) [@B3].

Multiple studies have established a link between damage induced by oxidative stress and DCM [@B12], [@B13]. Damage from oxidative stress due to the chronic mitochondrial overproduction of reactive oxygen species (ROS) plays a crucial role in inflammation and results in irreversible fibrosis and cardiomyocyte death [@B2], [@B12], [@B14], [@B15]. Inflammation in the myocardium is mediated by pro-inflammatory cytokines including TNFα and interleukin-6 [@B16]. Isoprostanes are formed by the peroxidation of polyunsaturated FAs and are considered an accurate reflection of the extent of oxidative damage [@B17]. Amelioration of oxidative stress on a molecular level can be achieved through induction of antioxidant agents, and studies have shown that enhancing mitochondrial ROS scavenging systems mitigates diabetes-induced cardiac dysfunction [@B2], [@B12], [@B18]-[@B21]. Bilirubin, a product of heme catabolism, is a potent antioxidant and under normal physiological conditions may attenuate many ROS-derived complications of DCM [@B22], [@B23]. Adiponectin is a hormone secreted by adipose tissue that regulates metabolic processes and functions as an antioxidant; the low plasma levels of adiponectin seen in diabetes contribute to the oxidative damage seen in DCM [@B24], [@B25].

Structurally, the progression of DCM has been linked to cardiomyocyte hypertrophy and increased fibrosis [@B26]-[@B29]. The presence of cardiomyocyte hypertrophy was supported by data from The Framingham Heart Study, which revealed left ventricular mass was higher in diabetics compared to non-diabetics independent of covariates [@B30]. Hyperglycemia facilitates the reaction of glucose with collagen to form advanced glycation end-products (AGEs) that promote the crosslinking of collagen molecules to produce fibrosis [@B26]. Insulin-like growth factor binding protein 7 (IGFBP7) is a modulator of insulin-like growth factors, which actively regulate insulin consumption and receptor activity [@B27]. IGFBP7 has been identified as a biomarker for diastolic dysfunction associated with cardiac hypertrophy, myocyte fibrosis, and vascular remodeling, and an association exists between serum IGFBP7 levels and echocardiographic abnormalities describing diastolic heart dysfunction independent of systolic function [@B28]. In diabetics, increased ROS production causes the subsequent release of the profibrotic marker transforming growth factor beta (TGF-β) [@B29]. TGF-β levels correlate with the degree of cardiac fibrosis [@B26]. The reliable early detection of fibrotic changes in DCM patients by IGFBP7 and TGF-β is clinically pertinent since current experimental therapies designed to prevent progression of DCM include targeting collagen deposition and fibrosis through AGE inhibitors and AGE cross-link breakers [@B1], [@B6].

The aim of this study was to investigate a panel of biomarkers, including fibrotic markers, antioxidants, markers of oxidative stress and a metabolic profile that can be used to detect DCM prior to the presentation of clinical signs. Early stage DCM is initially asymptomatic and reversible; however, DCM is a progressive disease and at the later stage prognosis becomes poor. Analysis of biomarkers would be a simple, noninvasive diagnostic tool with a high predictive value for clinical use in patients with the potential to develop DCM, which would allow for the implementation of therapeutic strategies to be started in time to delay and mitigate the disease progression.

Materials and Methods
=====================

Patients
--------

This study comprised of 100 patients, divided into 4 categories based on predetermined clinical diabetic and cardiac parameters: 1. Control; 2. Diabetes with normal diastolic function; 3. Diastolic dysfunction without diabetes; 4. Diabetes and diastolic dysfunction. Contraindications to inclusion included any systolic dysfunction observed through echocardiography, evidence of prior myocardial infarction, known valvular or congenital heart disease or other significant comorbidities, including malignancy, renal failure, or significant psychiatric illness. The study was approved by the Ethics Committee of Cabell Huntington Hospital, West Virginia. All of the subjects signed a written informed consent form.

Blood Samples
-------------

Venous blood was drawn from an antecubital vein in heparinized and Vacutainer tubes for serum levels of TNFα, IL-6, Isoprostane, leptin, adiponectin, bilirubin, IGFBP7, TGF-β and lipid and glucose profiles. Blood was processed after collection and serum collected for analysis. Serum was collected by spinning down the vacutainer tube at 2000 rpm for 5 minutes at 4°C and stored at -80°C until analysis.

Analysis of Biomarkers
----------------------

Serum samples frozen at -80°C were analyzed for levels of the following biomarkers through enzyme-linked immunosorbent-assay (ELISA): TNFα, IL-6, adiponectin and leptin (EMD Millipore, Temecula, Ca), Isoprostane (Cayman Chemical Company, Ann Arbor, MI), bilirubin (Sigma-Aldrich, St. Louis, MO), triglycerides (Abcam, Cambridge, MA), and IGFBP7 and TGF-β (kits from R&D Systems, Minneapolis, MN). ELISAs were conducted in accordance to the manufacturer\'s protocol.

Echocardiography
----------------

Patients underwent echocardiography with 2D, Doppler, and color flow imaging within the year prior to enrollment. Studies were performed on a Philips IE 33 with an S5 transducer by one of 2 certified echo technicians in an ICAEL- accredited laboratory. Measurements were obtained in accordance with guidelines set forth by the American Society of Echocardiography [@B31] 2D imaging was used to assess ejection fraction (EF), septal wall thickness, posterior wall thickness and left atrial size. Relative wall thickness was calculated with posterior and septal wall thickness by (2×PWTd)/LVIDd [@B32]. Mitral valve inflow Doppler was recorded, including E and A waves. Tissue Doppler was used for E\' and A\'. Echocardiographic images were read by physicians who were blinded to the rest of the study.

Statistical Analysis
--------------------

Data were analyzed using Graphpad Prism 4.0. Equal variance was assured by Bartlett\'s test for each biomarker within each of the four patient categories. ANOVA was conducted to identify statistically significant differences in the mean serum levels for the different biomarkers. The Tukey post-hoc test was used to indicate which patient groups showed statistically significant differences for the biomarker level measured.

Results
=======

Echocardiography
----------------

All patients underwent transthoracic echocardiography to evaluate left ventricular diastolic filling pressure. Patients were identified as normal or as having diastolic dysfunction (DD), with DD encompassing a spectrum of severities including impaired relaxation, pseudonormal and restrictive diastolic dysfunction (Fig. [1](#F1){ref-type="fig"}). Results from echocardiographic imaging studies were used in conjunction with the diabetic status of the patients to establish the 4 patient categories.

TNFα, isoprostane and IL-6 levels
---------------------------------

TNFα, isoprostanes, and IL-6 are inflammatory mediators that correlate with the extent of oxidative damage in tissues. TNFα and IL-6 were found to be elevated in DM, DD and DM+DD patients when compared to the control group (Fig. [2](#F2){ref-type="fig"}A and B respectively; p \< 0.05). Levels of TNFα and IL-6 were significantly higher in patients with DM+DD than the levels found in either the patients with DM or the patients with DD alone (p \< 0.05). Patients with DM and patients with DM+DD had increased levels of isoprostane compared to the control (Fig. [2](#F2){ref-type="fig"}C). Additionally, isoprostane levels in patients with DM+DD were higher than levels observed in patients with DD alone (p \< 0.05).

Leptin, adiponectin and bilirubin levels
----------------------------------------

Leptin is an adipose-derived hormone that stimulates energy expenditure and is proportional to the amount of body fat. Leptin was significantly higher in the DM, DD and DM+DD patients when compared to the control group and was also significantly increased in the DM+DD patients when compared to the patients with DD alone (Fig. [3](#F3){ref-type="fig"}A; p \< 0.05). Adiponectin was significantly lower in DM, DD, and DM+DD patients when compared to control groups (Fig.[3](#F3){ref-type="fig"}B). Adiponectin was also significantly decreased in DM+DD patients when compared to patients with DM and DD alone (p \< 0.05). Bilirubin, a potent antioxidant, was found to be lowered in the DM+DD group compared to the control (Fig. [3](#F3){ref-type="fig"}C; p \< 0.05).

TGF-β and IGFBP7 levels
-----------------------

TGF-β correlates to the extent of fibrosis and IGFBP7 has been linked to cardiomyocyte hypertrophy and fibrosis. In concordance with our hypothesis, TGF-β levels were elevated in DM, DD and DM+DD patients when compared to the control group (Fig. [4](#F4){ref-type="fig"}A; p \< 0.05). DM+DD patients did have significantly higher TGF-β levels than the DM group alone. IGFBP7 was found to be increased in the DM, DD and DM+DD groups compared to the control. IGFBP7 was also higher in patients with DM+DD compared to patients with DM or DD alone (Fig. [4](#F4){ref-type="fig"}B; p \< 0.05).

Discussion
==========

This study demonstrated that a panel of biomarkers in conjunction with echocardiography plays an important role in the detection of DCM prior to the manifestation of symptoms and irreversible complications. Our data indicated that early stage DCM can be characterized by elevated levels of inflammatory markers, decreased antioxidant markers and, most importantly, increased levels of the fibrotic markers IGFBP7 and TGF-β. These results, which are summarized in the schematic diagram (Fig. [5](#F5){ref-type="fig"}), identified potential biomarkers that can be used clinically to improve early diagnosis of DCM.

The clinical parameters studied include BMI and levels of triglycerides, HDL, glucose and HbA1C. A comprehensive national study, which substantiates the correlation between BMI and diabetes, found that individuals with diabetes were more likely to have a high BMI [@B33]. In concordance with established studies, our results demonstrated elevated blood glucose levels and HbA1C levels in both DM patients and patients with DM+DD. As expected, neither glucose nor HbA1C was elevated in DD patients, because DD is not specific to DCM but can result from hypertension, aortic stenosis, myocardial infarction, or systolic heart failure [@B31]. Triglycerides are elevated in diabetic patients due to an increased reliance on fatty acid metabolism, and our data demonstrated DM+DD patients had significantly elevated triglyceride levels. Additionally, our data supported the accepted trend that diabetes is closely associated with reduced levels of HDL.

Elevated levels of the inflammatory cytokines TNFα and IL-6 are associated with diabetes, and previous studies have indicated their role in cardiac remodeling, fibrosis and cardiomyocyte apoptosis [@B34]. In corroboration with these findings, our study demonstrated elevated levels of TNFα and IL-6 relative to the control in DM, DD, and DM+DD patients, with the highest levels in DM+DD patients, supporting their utilization in the early detection of DCM and amelioration of disease progression. Isoprostane, an established marker of oxidative stress [@B11], was increased in patients with DM+DD. Our results also demonstrate a decreased level of the antioxidant bilirubin in patients with DM+DD, which supports previous findings that bilirubin is negatively associated with diabetes and its complications.

Leptin is closely associated with obesity and its comorbidities [@B35], and was highest in patients with DM and patients with DM+DD, which were the groups with the highest BMIs. There is an inverse relationship between adiponectin and leptin levels, with low adiponectin levels indicating metabolic and cardiovascular disease [@B36]. Adiponectin has antidiabetic effects and antagonizes the progression of DCM by exhibiting potent anti-hypertrophic activity in cardiomyocytes and diminishing cardiac cell death by inhibiting inflammation [@B36]-[@B38]. Consistent with previous studies, our data identified decreased levels of adiponectin in DM patients and DD patients and significantly lower adiponectin levels in DM+DD patients, which indicated a diminished ability to offset the DCM complications and progression.

IGFBP7 is an established marker for diabetes, and is associated with the severity of diastolic dysfunction [@B28]. IGFBP7 also has been implicated in fibrogenesis in diabetic patients, and increasing concentrations correlate with increased collagen deposition, which, in cardiac tissue, may contribute to diastolic stiffness [@B39]. Diabetes-induced ROS generation up-regulates the expression of the profibrotic marker TGF-β, leading to increased extracellular matrix synthesis and fibrosis [@B26]. Increased collagen deposition and interstitial fibrosis are key pathogenic components of DCM that result in myocardial stiffness, which manifests as diastolic dysfunction [@B2]. In concordance with previous studies, our data indicated elevated IGFBP7 and TGF-β levels in DM, DD and DM+DD patients, with DM+DD patients displaying the highest values. Therefore, these markers play an important role in the early detection of DCM and cardiac fibrosis, enabling early intervention to attenuate disease progression.

Conclusion
==========

In conclusion, our study identified a panel of biomarkers that can complement echocardiographic imaging studies to detect the diabetes-induced changes in cardiac structure and function present at the early stage of DCM. The progression of DCM from subclinical diastolic dysfunction to overt heart failure, and the associated high morbidity and mortality in the later stage of the disease, validates the need for reliable early diagnostic tools. The results of our clinical study can improve early diagnosis of DCM and help successfully implement preventative strategies that may slow or prevent development of the disease.

Our study advances the understanding of the clinical use of biomarkers in the diagnosis of DCM in a rural Appalachian population. Diabetes is a chronic disease with high prevalence in West Virginia, and its comorbidities present a significant public health concern. Therefore, West Virginia patients present the ideal population to study strategies for earlier detection of DCM. Positive results from this study will benefit this underserved population by enabling an earlier and more reliable diagnosis of DCM so that therapy can be administered in time to attenuate disease progression.

This work was supported by the National Institutes of Health Grants HL 109015, HL 071556 and HL 105649 (J.I.S.).

![Mitral inflow velocities representing normal physiology as well as the spectrum of diastolic dysfunction. **A:** Normal. **B:** Impaired Relaxation. **C:** Grade II diastolic dysfunction (pseudonormalization).**D:**Restrictive](ijmsv13p0161g001){#F1}

![Assay of serum concentrations of (A) TNFα, (B) IL-6 and (C) Isoprostane in Control, DM, DD and DM+DD patient groups. Values represent means ± SEM. \* *p*\<0.05 vs CTR; \# *p*\<0.05 vs DM, + *p*\<0.05 vs DD.](ijmsv13p0161g002){#F2}

![Serum levels of (A) Leptin, (B) Adiponectin and (C) Bilirubin in Control, DM, DD and DM+DD patient groups determined by ELISA. Values represent means ± SEM. \* *p*\<0.05 vs CTR; \# *p*\<0.05 vs DM, + *p*\<0.05 vs DD.](ijmsv13p0161g003){#F3}

![Assay of serum concentrations of (A) TGF-β and (B) IGFBP7 in Control, DM, DD and DM+DD patient groups. Values represent means ± SEM. \* *p*\<0.05 vs CTR; \# *p*\<0.05 vs DM, + *p*\<0.05 vs DD.](ijmsv13p0161g004){#F4}

![Schematic depicting the progression from diabetes to DCM and the role of biomarkers to detect early stage DCM and attenuate disease progression.](ijmsv13p0161g005){#F5}

###### 

Demographic Profile

  Groups       Control       Diastolic dysfunction (DD)   Diabetes mellitus (DM)   Diabetes mellitus with diastolic dysfunction (DM+DD)
  ------------ ------------- ---------------------------- ------------------------ ------------------------------------------------------
  Age (yrs.)   41.67±11.79   44.27±8.31                   48±11.78                 45.69±8.42
  Sex (M/F)    43%/57%       50%/50%                      52%/48%                  57%/43%

###### 

Patient Clinical Profile.

            BMI               Triglycerides (mg/dL)   HDL (mg/dL)       Glucose (mg/dL)     HbA1C (mg/dL)
  --------- ----------------- ----------------------- ----------------- ------------------- -----------------
  Control   23.89 (±1.01)     61.12 (±11.02)          67.60 (±3.96)     91.50 (±4.50)       5.20 (±0.11)
  DM        39.79\* (±1.82)   85.72 (±8.62)           43.60\* (±2.04)   186.28\* (±3.97)    8.04\* (±0.12)
  DD        31.42\# (±2.70)   92.16 (±8.10)           44.42\* (±3.85)   107.23\# (±24.14)   5.72\# (±0.50)
  DM+DD     39.44\* (±2.68)   113.05\* (±9.39)        39.50\* (±1.97)   172.9\* (±13.66)    7.36\*+ (±0.29)

Values represent means ± SEM. \* *p*\<0.05 vs Control; \# *p*\<0.05 vs DM; + *p*\<0.05 vs DD.

[^1]: \* Both authors contributed equally

[^2]: Competing Interests: The authors have declared that no competing interest exists.