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]


1. Background and Aims {#sec1}
======================

Diabetes mellitus (DM) is a chronic disease which can evolve towards devastating micro- and macrovascular complications. DM is the most frequent cause of chronic kidney disease (CKD). The diabetic chronic kidney disease (CKD) is a clinical syndrome characterized by persistent albuminuria (albumin/creatinine ratio in the spontaneous urine ≥ 30 mg/g) and/or a sustained decline of the estimated glomerular filtration rate (eGFR) below 60 mL/min/1.72 m^2^. If at least one of these values is still maintained within these abnormal limits after 3 months from the first measurement, the diagnosis of diabetic CKD may be established \[[@B1], [@B2]\].

Insulin resistance has been linked to the pathophysiology of type 2 diabetes mellitus (T2DM) and it has been proven to play an important role in the increase of the risk of cardiovascular complications. It has been recently shown that insulin resistance plays an important role in the natural history of type 1 diabetes, although it is generally known that it is mainly due to immune destruction of the *β*-pancreatic cells \[[@B3]\].

Since 1977, Ginsberg has studied the association of insulin resistance with type 1 diabetes mellitus (T1DM) \[[@B4]\]. Subsequent studies \[[@B5], [@B6]\] showed a significant decrease in glucose disposal mediated by insulin among patients with T1DM suggesting the presence of insulin resistance. After a decade, this combination has aroused the interest of many research studies \[[@B7], [@B8]\] to investigate the possible mechanisms of insulin resistance in T1DM. Insulin resistance is a feature present in T1DM \[[@B9]--[@B11]\]; clamp studies on T1DM confirm its growth in patients with microalbuminuria \[[@B12]\] while glomerular filtration rate decreases \[[@B13]\]. Insulin resistance is a progressive process increasing the urinary excretion of albumin and decreasing glomerular filtration rate \[[@B14]\].

The purpose of the study was to determine the prevalence of CKD in T1DM and the correlation with insulin resistance (IR) in patients with CKD.

2. Materials and Methods {#sec2}
========================

The study was conducted over a period of three years (2010--2013) and was comprised of patients with DM registered in the Clinical Centre of Diabetes, Nutrition and Metabolic Diseases of Dolj county.

The study design was an epidemiological, transversal, noninterventional type. Finally, the study group included 200 subjects with T1DM.

Anamnestic data have been analyzed (age, sex, duration of DM, history of arterial hypertension, dislipidemia, and hyperuricemia), as well as paraclinical data (urea, creatinine, uric acid, cholesterol, triglycerides, A1c, and urinary albumin-to-creatinine ratio). The estimated glomerular filtration rate (eGFR) has also been calculated according to the Modification of Diet in Renal Disease (MDRD) equation. The CKD stages have been established according to KDIGO 2012 definition. In predicting the risk for outcome of CKD, we used GFR and albuminuria category. GFR from 60 to 89 mL/min/1.73 m^2^ is classified as CKD stage 2, from 45 to 59 mL/min/1.73 m^2^ as CKD stage 3a, from 30 to 44 mL/min/1.73 m^2^ as CKD stage 3b, and from 15 to 29 mL/min/1.73 m^2^ as CKD stage 4. Stage 1 is defined by GFR over 90 mL/min/1.73 m^2^ and stage 5 by GFR under 15 mL/min/1.73 m^2^. Albuminuria categories were A1 \< 30 mg/g (normally to moderately increased), A2 = 30--300 mg/g (moderately increased), and A3 \> 300 mg/g (severely increased).

Insulin resistance was evaluated by the estimated glucose disposal rate (eGDR). Subjects with eGDR ≤ 7.5 mg/kg/min were considered with IR. The euglycemic-hyperinsulinemic clamp is the accepted standard for measurement of insulin sensitivity; however, it is not practical for use in the clinical setting. The eGDR can be calculated using routine clinical measures: the glycosylated hemoglobin (HbA1c), the presence of hypertension, and the waist circumference \[[@B15], [@B16]\]. We used the eGDR because it shows good correlation with IR measured by the euglycemic-hyperinsulinemic clamp and has been validated for the estimation of insulin sensitivity in individuals with type 1 DM.

2.1. Statistical Analysis {#sec2.1}
-------------------------

The recorded data have been analyzed using the Statistical Package for the Social Sciences (SPSS), version 17.00, software (IBM Corporation, Armonk, NY, USA). We performed analysis of the entire study population and separate statistics for each of the 3 groups. The methods used were *t*-test, Mann--Whitney test, Chi-square test, and Cramer test as appropriate. We used the following interpretation of *p* values: *p* \< 0.05, the difference between the two means is significant (S); *p* \< 0.01, the difference between the two means is highly significant (HS); *p* \< 0.001, the difference between the two averages is very highly significant (VHS); *p* \> 0.05, the difference between the two means is not significant (NS).

3. Results {#sec3}
==========

The study included 200 subjects with type 1 diabetes (116 men and 84 women). [Table 1](#tab1){ref-type="table"} summarizes the characteristics of the population studied. Data are presented as mean ± standard deviation.

Insulin resistance was found in 41.5% of patients with type 1 DM ([Figure 1](#fig1){ref-type="fig"}). CKD was found in 44% of the patients ([Figure 2](#fig2){ref-type="fig"}). Statistically analyzing the presence of CKD, there were statistically highly significant differences between the patients with CKD and those without CKD with regard to age and sex of the patients, duration of diabetes, HbA1c, eGDR, and the presence of hypertension, dyslipidemia, and hyperuricaemia ([Table 2](#tab2){ref-type="table"}).

In patients with CKD, the age and diabetes duration are significantly higher than in those who do not have this complication. CKD is more frequent in males than in females (50.9% men versus 34.5% women, *p* = 0.022). High blood pressure, hyperuricemia, and dyslipidemia are significantly increased in the diabetic patients with CKD.

eGDR value (expressed as mg·kg^−1^·min^−1^) is lower in patients with CKD than in those without CKD (15.92 versus 6.42, *p* \< 0.001), indicating that patients with CKD show higher insulin resistance than those without CKD ([Figure 3](#fig3){ref-type="fig"}).

By performing the logistic regression analysis of the stepwise type where we introduced all statistically significant parameters in [Table 2](#tab2){ref-type="table"}, we obtained the final regression model shown in [Table 3](#tab3){ref-type="table"}.

Analyzing the area under the ROC curve, used to assess the usefulness of studying the parameters from [Table 3](#tab3){ref-type="table"} as independent predictors for the occurrence of CKD in patients with type 1 diabetes, the best predictor appears to be the eGDR value followed by the duration of diabetes, age, hypertension, dyslipidemia, and blood pressure ([Table 4](#tab4){ref-type="table"} and [Figure 4](#fig4){ref-type="fig"}).

4. Discussions {#sec4}
==============

Older age and long evolution of DM are factors known and shown in numerous studies to be associated with the development and progression of chronic complications in diabetes, whereas this new concept of insulin resistance has appeared lately in type 1 DM and it has gained ground worldwide being accepted by doctors and researchers. Its association with the emergence of chronic vascular complications in particular triggered the development of several studies that try to explain the link between insulin resistance and type 1 DM.

In European countries such as Finland, the country with the highest rate of incidence and prevalence of diabetes type 1 in the world, insulin resistance is associated with older age, longer duration of diabetes, and an increased amount of fat tissue \[[@B17]\] with a family history of type 2 DM, with poor glycemic control \[[@B15]\] and increased serum lipid levels. In Asian countries, a low prevalence of this disease prevented the researchers from investigating the link between insulin resistance and type 1 DM. In general, the prevalence of insulin resistance in the Asian population both in diabetic and in nondiabetic populations is relatively small compared to the Caucasian race, this thing being attributed to lower rates of obesity in Asians.

In this study, we evaluated the prevalence of insulin resistance in patients with type 1 diabetes using clinical score eGDR, its results showing a prevalence of 41.5%, similar to other European countries where the prevalence cited in studies is 32--48%.

As in the studies conducted in Europe, older age, longer duration of diabetes development, hypertension, and dyslipidemia are more frequent characteristics among adults with type 1 DM with insulin resistance.

The small sample of 200 patients used in this study compared with the studies conducted in the European Union may explain some of the differences which appear. In addition, both groups have a large number of subjects under insulin treatment inadequately dosed due to the risk of hypoglycemia especially in the context given by the presence of CKD. The results of this study showed that insulin resistance, measured by eGDR, is more frequent in patients with CKD than in those without CKD.

5. Conclusions {#sec5}
==============

This study has shown that insulin resistance is associated with an increased risk of CKD, but, because of the cross-sectional design, the causal relationship cannot be assessed. However, the existence of this causality and the benefit of treatment for insulin resistance in type 1 DM are issues for further discussion.In conclusion, our study has shown that insulin resistance is a constant factor associated with CKD in type 1 DM and it may be useful in the future for CKD screening.

Conflicts of Interest
=====================

The authors declare that they have no competing interests regarding the publication of this paper.

Authors\' Contributions
=======================

All authors contributed equally to the preparation of this manuscript and thus share first authorship.

![Insulin resistance in type 1 DM patients.](JNME2017-6425359.001){#fig1}

![Graphic representation of CKD.](JNME2017-6425359.002){#fig2}

![Insulin resistance in CKD patients.](JNME2017-6425359.003){#fig3}

![Analysis of the area under the ROC curve for the eGDR in patients with CKD.](JNME2017-6425359.004){#fig4}

###### 

Characteristics of the studied population.

  Parameters                Value
  ------------------------- --------------------
  Sex (M/F)                 116 (58%)/84 (42%)
  Age                       37,57 ± 12,45
  Age at the onset of DM    21,49 ± 8,49
  Duration of DM            16,21 ± 8,97
  BMI (kg/m^2^)             23,84 ± 3,72
  Abdominal circumference   88,43 ± 10,21
  Hip circumference         96,34 ± 7,77
  Waist/hip index           0,91 ± 0,07
  eGFR                      92,39 ± 25,17
  eGDR                      10,06 ± 5,56
  Total cholesterol         170,35 ± 41,61
  HDL-cholesterol           61,35 ± 57,87
  Triglyceride              114,6 ± 66,68
  A1c                       8,4 ± 1,58
  CKD                       44%
  Hypertension              52,5%
  Dyslipidemia              55,5%
  Hyperuricemia             31%

###### 

Characteristics of patients with CKD.

  Variables                  With CKD        Without CKD     *p*
  -------------------------- --------------- --------------- --------------
  Number of patients         88 (44%)        112 (56%)       N/A
  Age (years)                43,78 ± 10,57   32,68 ± 11,64   *p* \< 0,001
  Sex (M/F)                  59/29           57/55           *p* = 0,022
  Duration of DM (years)     20,05 ± 6,62    13,2 ± 9,44     *p* \< 0,001
  BMI (kg/m^2^)              24,28 ± 3,68    23,49 ± 3,73    *p* = 0,057
  AC (cm)                    88,88 ± 10,83   88,08 ± 9,73    *p* = 0,671
  A1c (%)                    8,88 ± 1,71     8,02 ± 1,26     *p* \< 0,001
  eGDR (mg·kg^−1^·min^−1^)   6,42 ± 3,44     15,92 ± 5,22    *p* \< 0,001
  Dyslipidemia               59 (67%)        37 (33%)        *p* \< 0,001
  Hypertension               61 (69,3%)      34 (30,3%)      *p* \< 0,001
  Hyperuricemia              77 (87,5%)      14 (12,5%)      *p* \< 0,001

###### 

The final model of logistic regression for CKD.

                   OR       95% CI          *p*
  ---------------- -------- --------------- --------------
  Age              1,086    1,056--1,116    *p* \< 0,001
  Sex              0,509    0,286--0,909    *p* = 0,022
  Duration of DM   1,105    1,062--1,150    *p* \< 0,001
  A1c              1,450    1,186--1,774    *p* \< 0,001
  eGDR             0,744    0,684--0,810    *p* \< 0,001
  Dyslipidemia     10,714   5,354--21,442   *p* \< 0,001
  Hypertension     12,167   6,099--24,269   *p* \< 0,001
  Hyperuricemia    20,647   8,937--47,702   *p* \< 0,001

###### 

Area under the ROC curve analysis for statistically significant parameters associated with CKD.

                   Area under the ROC curve   95% CI
  ---------------- -------------------------- --------------
  eGDR             **0,829**                  0,770--0,888
  Age              **0,774**                  0,708--0,840
  Hypertension     **0,772**                  0,705--0,839
  Hyperuricemia    **0,771**                  0,701--0,841
  Duration of DM   **0,770**                  0,704--0,836
  Dyslipidemia     **0,755**                  0,687--0,824
  A1c              0,663                      0,585--0,741
  Sex              0,581                      0,501--0,660

[^1]: Academic Editor: C. S. Johnston