GH-METHODS

Math-Physical Medicine

NO. 382

A clinical case on the relationship examination between urinary albumin-to-creatinine ratio and thyroid stimulating hormone using GH-Method: Math-physical method

Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.

Abstract
This study applies the traditional statistical method of “Correlation Coefficients” (R) to calculate the strength of connectivity between two biomarkers by concentrating on the mutual influence and connectivity only, not the cause versus effect.

The author focuses on investigating the strength of connectivity or association between urinary albumin creatinine ratio (ACR) and thyroid stimulating hormone (TSH) along with the degrees of their inter-connections in the following 12 biomarkers in 5 categories:

  1. Lipid: triglycerides (TG), high-density lipoprotein (HDL-C), low- density lipoprotein LDL-C, and total cholesterol (TC)
  2. Glucose: fasting plasma glucose (FPG) and HbA1C (FPG & postprandial plasma glucose (PPG))
  3. Blood pressure: SBP and DBP
  4. BMI: Weight
  5. Others: TyG-A and TyG-B (logarithm of TG and FPG), along with TSH

The findings, using 13 datasets within 7+ years duration of his personal health examination records, clearly shows that there is a strong correlation coefficient of 58% existing between the urinary ACR and TSH.  This result confirms with conclusions from many of previously published medical research papers. Many of those research work used a much larger number of patients’ data within various timespans; however, this study only uses the health data from one single patient, himself, with a homogeneous environmental conditions over a long timespan of 7+ years.  Nevertheless, a larger study involving a diverse population from multiple medical centers can provide further useful information on the true behaviors and characteristics of the thyroid hormone abnormalities.  Of course, this type of complex study can sometimes provide contradictory observations as well.  The author’s research work on thyroid is a non-stop and ongoing task which will continue.

Introduction
This study applies the traditional statistical method of “Correlation Coefficients” (R) to calculate the strength of connectivity between two biomarkers by concentrating on the mutual influence and connectivity only, not the cause versus effect.

The author focuses on investigating the strength of connectivity or association between urinary albumin creatinine ratio (ACR) and thyroid stimulating hormone (TSH) along with the degrees of their inter-connections in the following 12 biomarkers in 5 categories:

  1. Lipid: triglycerides (TG), HDL-C, LDL-C, and total cholesterol (TC)
  2. Glucose: FPG and HbA1C (FPG & PPG)
  3. Blood pressure: SBP and DBP
  4. BMI: Weight
  5. Others: TyG-A and TyG-B (logarithm of both TG and FPG), along with TSH.

Methods
1. Background of GH-Method: MPM
To learn more about the author’s GH-Method: math-physical medicine (MPM) methodology, readers can refer to his article to understand his developed MPM analysis method in Reference 1.

2. Diabetes, ACR, & TSH
The author self-studied diabetes and its complications over the past 11 years.  The following information are excerpts from multiple articles he has read and collected (examples are References 3, 4, and 5).

Diabetes mellitus is a lifelong, incapacitating disease affecting multiple organs. Worldwide figures estimate that there are 422 million diabetic patients in 2014.  National Diabetes Statistics Report 2020 of CDC in USA also estimates that there are 34.2 million Americans, just over 1 in 10, have diabetes in 2020.  

The American Diabetes Association (ADA) released research on March 22, 2018 estimating the total costs of diagnosed diabetes have risen to $327 billion in 2017 from $245 billion in 2012.  This figure represents a 26% increase over a five-year period.  The total estimated 2017 cost of diagnosed diabetes of $327 billion includes $237 billion in direct medical costs and $90 billion in reduced productivity.  

The largest components of medical expenditures are:

  • Hospital inpatient care (30% of the total medical cost)
  • Prescription medications to treat complicationsof diabetes (30%)
  • Anti-diabetic agents and diabetes supplies (15%)
  • Physician office visits (13%)

People with diagnosed diabetes incur average medical expenditures of $16,752 per year, of which about $9,601 is attributed to diabetes (57% of total expenditures).  On average, they have medical expenditures approximately 2.3 times higher than what expenditures would be in the absence of diabetes.  

Diabetes is associated with devastating chronic complications including coronary heart disease and stroke (macro-vascular disease) as well as microvascular disorders leading to damage of the small blood vessels of the kidney (nephropathy), eye (retinopathy) and peripheral nerves (neuropathy).  These complications impose an immense burden on the quality of life of the diabetes patients.  

Urinary albumin-to-creatinine ratio (ACR) is a biomarker of diabetic nephropathy (kidney complications) and microvascular damage. Metabolic-related traits are observationally associated with ACR, including glycemic, lipid, and adiposity traits.  Specifically, insulin resistance (for example, two TyG biomarkers including logarithm of both TG and FPG) which are associated with elevated ACR levels and microvascular damage.

The thyroid stimulating hormone (aka thyrotropin or thyrotrophin) is produced by the pituitary gland.  It works sort of like the master of the hormones, and rules the production of T3 and T4 from its control center.  If you have too much TSH, it might mean that your thyroid gland isnt making enough T3 or T4.  Remember, the TSH is supposed to stimulate the thyroid gland.  But if the gland isnt responding, then we will have too much TSH in our system.  On the other hand, if our TSH levels are too low, it may mean that our thyroid gland is making too much thyroid hormone.  This excessive thyroid production could actually suppress the TSH to a low level.  

My friend, Dr. Nelson Hendler, also advised the following:

For evaluation of thyroid function, you need to consider not only the thyroid gland but the pituitary and immune system.  So measurements of Thyroid Stimulating Hormone and T4 are not enough.  You need to look at mitochondrial and autoimmune features seen in Hashimoto’s thyroiditis.

  • #1. Thyroid Stimulating Hormone (TSH)
  • #2. Free T3 (Free Triiodothyronine)
  • #3. Free T4 (Free Thyroxine)
  • #4. Total T3
  • #5. Reverse T3 (Reverse Triiodothyronine)
  • #6. Sex Hormone Binding Globulin
  • #7. Thyroglobulin Antibodies (TGAb) & Thyroid Peroxidase Antibodies
  • #8. Thyroid Stimulating Immunoglobulins (TSI)
  • #9  Thyroid binding globulin.”

3. Article of ACR and TSH
Recently, the author read an article from a Saudi Arabia hospital, titled “Association of Serum Thyroid Stimulating Hormone and Free Thyroxine with Urinary Albumin Excretion in Euthyroid Subjects with Type 2 Diabetes Mellitus” (Reference 2).

Here is an excerpt from the paper:

Our study demonstrated that abnormalities in thyroid function occur in patients with T2DM and proteinuria. Specifically, TSH levels were higher and FT4 levels were lower in patients with albuminuric diabetic renal diseases (higher ACR). The interplays between thyroid and kidney have been recognized in many disease states. Thyroid dysfunction can influence kidney through the immune-mediated pathway and thyroid hormones [18]. The thyroid disorder and coincident nephropathy have been reported mainly in the patients presented with albuminuria [19,20]. In this situation, thyroid hormone may influence glomerular and tubular functions through pre-renal and intrinsic renal effects. Thyroid hormones influence renal development, kidney structure, renal hemodynamics, glomerular filtration rate, the function of many transport systems along the nephron and sodium and water homeostasis. These effects of thyroid hormone are due to direct renal actions and in part are mediated by cardiovascular and systemic hemodynamic effects that influence kidney function. Disorders of thyroid function have also been linked to development of immune mediated glomerular injury and alterations in thyroid hormones and thyroid hormone testing occur in patients with kidney disease.

The TSH level is often elevated in CKD in response to TSH from pituitary as a result of uremic effect [22]. In agreement to our study, patients with severely increased albuminuria has the worst HbA1c compared to NA (normal albuminuria) and moderately increased albuminuria patients, 9.2 ± 2.1 versus8.6 ± 2.2 and 7.9 ± 2.1 respectively, p < 0.0001 [23]. TSH also loses its circadian rhythm along with compromised bioactivity due to poor glycosylation. The Wolffe Chaikoff effect has been cited as a causative phenomenon behind the rise of this disorder in diabetic kidney disease patients [24].

In agreement to our study, we found that patients with severely increased albuminuria were significantly have lower FT4 than patients with NA and moderately increased albuminuria, 15.3 ± 1.9 versus16.1 ± 2.5 and 15.4 ± 2.6 respectively, p = 0.045 [12]. The role of albuminuria is confirmed by the significant positive correlation between TSH and albuminuria, r = 0.08, p = 0.01 and nonsignificant negative correlation between FT4 and albuminuria, r = -0.07, p = 0.2. Proteinuria is a hallmark of renal diseases. Severe albuminuria results in hypoalbuminaemia. Albumin is the most abundant protein in serum and urine. In patients with albuminuria many other proteins beside albumin are lost in the urine. Among these are hormones and hormone-binding proteins. Several studies have documented urinary loss of thyroid hormones and thyroxin-binding globulin (TBG) in patients with albuminuria [2528]. In patients with the nephrotic syndrome, loss of thyroid hormones may lead to low free thyroid hormone levels unless production is increased under the influence of TSH. Furthermore, loss of albumin and TBG may reduce the binding capacity for thyroid hormones, resulting in a decrease in total T4 concentrations.

We conclude that despite the limitations of this hospital-based retrospective study, high TSH and low FT4 levels are highly prevalent in cohort of Saudis with albuminuria and T2DM. The majority of our patients in our finding were predominantly females. These two observations remain to be validated by population-based studies.  

4. Article of ACR using Mendelian Randomization (MR) method
Professor Jessica Terrell has conducted a study regarding the ACR and published a paper: “A Mendelian Randomization Study Provides Evidence That Adiposity and Dyslipidemia Lead to Lower Urinary Albumin-to-Creatinine Ratio, a Marker of Microvascular Function” (Reference 6).

Here is an excerpt from their paper:

Here, we confirmed ACR as a marker of microvascular damage and tested whether metabolic-related traits have causal relationships with ACR. The association between ACR and microvascular function was tested in the SUMMIT study. Two-sample Mendelian randomization (MR) was used to infer the causal effects of 11 metabolic risk factors, including glycemic, lipid, and adiposity traits, on ACR.  MR was performed in up to 440,000 UK Biobank and 54,451 CKDGen participants.  ACR was robustly associated with microvascular function measures in SUMMIT.  Using MR, we inferred that higher triglyceride (TG) and LDL cholesterol (LDL-C) levels caused elevated ACR.  A 1 SD higher TG and LDL-C level caused a 0.062 (95% CI 0.040, 0.083) and a 0.026 (95% CI 0.008, 0.044) SD higher ACR, respectively.  There was evidence that higher body fat and visceral body fat distribution caused elevated ACR, while a metabolically favorable adiposity” phenotype lowered ACR. ACR is a valid marker for microvascular function.  MR suggested that seven traits have causal effects on ACR, highlighting the role of adiposity-related traits in causing lower microvascular function.

The urinary albumin-to-creatinine ratio (ACR), a marker of diabetic nephropathy, is used as a proxy for damage to the systemic microcirculation (1) and predicts first myocardial infarction and mortality in those with diabetes, poststroke, and in the general population (2–4). There is evidence linking metabolic-related traits, including adiposity, dyslipidemia, and insulin resistance with elevated ACR levels and microvascular damage (5,6). It is well accepted that tight glucose control in patients with type 2 diabetes (T2D) reduces the risk of microvascular retinal complications (7,8), and there is evidence that adiposity per se is associated with increased ACR. For example, population studies suggest that microalbuminuria is associated with central adiposity (9), and results from the Framingham Heart Study show that visceral but not subcutaneous fat is associated with increased albuminuria (10).  Not all evidence linking metabolic-related traits comes from randomized control trials, and in absence of these, the next best evidence of causality comes from genetic studies using a technique known as Mendelian randomization (MR).

5. Mendelian randomization study
The following is a simple explanation of the Mendelian randomization (MR) study from Wikipedia:

Mendelian randomization (MR) is a method that allows one to test for, or in certain cases to estimate, a causal effect from observational data in the presence of confounding factors. From a statistical perspective, Mendelian randomization (MR) is an application of the technique of instrumental variables” with genotype acting as an instrument for the exposure of interest.  The method has also been used in economic research studying the effects of obesity on earnings, and other labor market outcomes.  Accuracy of MR depends on a number of assumptions: That there is no direct relationship between the instrumental variable and the dependent variables, and that there are no direct relations between the instrumental variable and any possible confounding variables. 

6. Input biomarker data of this study
The author was diagnosed with severe metabolic disorder for over 25 years, including T2D (HbA1C at 10%), hyperlipidemia (TG at 1161 in 2010), hypertension (SBP 150, DBP 92), five cardiac episodes (during 1994-2008) , kidney complications (ACR at 160 in 2010), foot ulcer (infected toe for over 3 months), diabetic retinopathy (micro-vascular complications), and hypothyroidism (TSH 6.32).  His two calculated TyG values in 2010 were extremely high, TyG-A at 6.18 and TyG-B at 6.52.  As a result, during the period of 2002 through 2010, he has suffered all of the known diabetic complications except for a stroke.

During the past 8 years, he has maintained a routine of having his medical examination conducted at the same medical laboratory or hospital on a quarterly basis.  However, his main focus has been on controlling his diabetes conditions; therefore, he has paid more attention to his diabetes directly related biomarkers, particularly HbA1C.  For the past 8 years from 2013 to 2020, he has had 36 medical examinations which include 19 examinations containing TG data and 13 examinations containing ACR data.  That is why he can only apply these 13 cases with his collected ACR data for this study.  As a long-term patient of chronic diseases and a dedicated endocrinology research scientist, he concentrated on his health conditions first in order to save his life.  As a result, he is most familiar with his own body situations and health conditions through his 11 years of self-study on medicine and his persistent medical research efforts using his 2 million collected data.

This particular study period covers 7+ years, actually 87 months, where he utilized 13 datasets of consistent biomarkers with an average of 6.7 months for each sub-period between two adjacent medical examinations.  From a macro-viewpoint, all of these 13 biomarkers should be more or less stabilized (i.e., near-constant) during each sub-period of 6.7 months.

7. Pearsons correlation coefficient
The calculated results of correlation coefficient (R) between ACR/TSH versus the other 12 biomarkers used the formula in the following table from Wikipedia:

8. Two TyG (triglycerides and glucose index) equations
Here are the two equations for TyG-A and TyG-B, as well as the equation of Delta i.e., the difference between these two TyG values (Reference 10).  It should be noted that TyG-A is defined and published by the medical community with many biomedical clinical cases as a statistical backup, while TyG-B and Delta equations are defined by the author, who tried to find an easier and practical expression while still possessing a high accuracy in comparison with results from using TyG-A equation.

  • TyG-A = (ln(TG) + ln(FPG)) / 2
  • TyG-B = ln(TG+FPG) – ln (2)
  • Delta = (TyG-B) – (TyG-A) or
  • Delta = (ln(((TG+FPG)/2)²/(TG*FPG)) / 2

Results
Figure 1 shows two raw input data tables of these 13 biomarkers, including ACR/TSH and the other 11 biomarkers during the 7+ year period from 8/9/2013 through 10/21/2020.  The bottom row of the data table lists the 24 calculated correlation coefficients of ACR/TSH versus the other 11 biomarkers.

Figure 1: Raw input data of 13 biomarkers on 13 medical examination dates and 12 calculated correlation coefficients of ACR/TSH versus other 12 biomarkers

Figure 2 displays two bar chart diagrams of the resulting correlation coefficients of ACR/TSH versus the other 11 biomarkers.  

In this particular study, he focused on investigating the ACR versus TSH and their degrees of inter-connectivity with the other 11 biomarkers within the following 5 categories of metabolic traits:

  1. Lipid: triglycerides (TG), high-density lipoprotein (HDL-C), low- density lipoprotein LDL-C, and total cholesterol (TC)
  2. Glucose: fasting plasma glucose (FPG) and HbA1C (FPG & postprandial plasma glucose (PPG))
  3. Blood pressure: SBP and DBP
  4. BMI: Weight
  5. Others: TyG-A and TyG-B (logarithm of TG and FPG), along with TSH

This article emphasizes the discussion and biomedical interpretations of the high correlation coefficient of 58% between ACR versus TSH.

In his previous research note, No. 381, the author identified two questions. He was unable to provide reasonable biomedical interpretations because he is not a medical doctor and lacks the background and formal academic training in biology and chemistry.  Over the past 11 years, he relied solely on his strong academic background in mathematics, physics, engineering, and computer science to conduct his needed medical research work to understand his body health conditions.  

In the first question, his study includes TSH, a thyroid biomarker, which has a strong correlation coefficient of 58% with ACR.  Why does TSH relate to ACR so close?  Hopefully, in this research note, No. 382, he can discover some of the answers or biomedical interpretations to this inquiry.  

For the second question, why do the correlation coefficients between ACR versus the other 3 lipid biomarkers, LDL-C, HDL-C and TC, have extremely low negative values: -3%, -19%, and -14%? He will continue to search for answers and proper biomedical interpretations regarding this question.

Figure 2: Two Bar chart diagrams of calculated correlation coefficients of ACR/TSH versus other 12 selected biomarkers

Figure 3 shows two diagrams with curves of both ACR versus TSH and ACR versus TSH*10 (i.e. TSH times 10).  The reason he chose a curve of TSH*10 is that, by amplifying the TSH values 10 times from a single digit expression to a double digits expression, the visual impact of graphic comparison is stronger and the relative correlation between curves is clearer since ACR is expressed with double digits.  However, this numerical amplification process does not change the calculated correlation coefficient between them, so they remain at 58% for both cases as shown in Figure 3.  

Figure 3: Two curve diagrams of calculated correlation coefficients and graphic comparison with ACR vs. TSH and ACR vs. TSH*10

Figure 4 provides an application example of predicting fasting plasma glucose (FPG) using body weight via the correlation coefficient method.  In 2016, he discovered the high correlation (68%) existing between his weight and FPG.  Therefore, he applied this relationship finding to develop his predicted FPG model.  By using his 5+ years data, from 4/11/2015 to 12/20/2020, the correlation coefficient between his predicted FPG versus his actual FPG is even higher at 86%.  More amazingly, it is with an extremely high 99.2% prediction accuracy rate of average value of predicted FPG over the average value of actually measured FPG.  This example illustrates the usefulness of correlation coefficient method of statistics. 

Figure 4: Example of 68% correlation coefficient between weight and FPG which resulted into an extremely high 99.2% prediction accuracy of FPG based on weight input (with 86% correlation)

Conclusions
The findings, using 13 datasets within 7+ years duration of his personal health examination records, clearly shows that there is a strong correlation coefficient of 58% existing between the urinary ACR and TSH.  This result confirms with conclusions from many of previously published medical research papers. Many of those research work used a much larger number of patients’ data within various timespans; however, this study only uses the health data from one single patient, himself, with a homogeneous environmental conditions over a long timespan of 7+ years.  Nevertheless, a larger study involving a diverse population from multiple medical centers can provide further useful information on the true behaviors and characteristics of the thyroid hormone abnormalities.  Of course, this type of complex study can sometimes provide contradictory observations as well.  The author’s research work on thyroid is a non-stop and ongoing task which will continue.

References

  1. Hsu, Gerald C., eclaireMD Foundation, USA, No. 310: “Biomedical research methodology based on GH-Method: math-physical medicine”
  2. Khalid S Aljabri, MD, FRCPC, FACP1*, Ibrahim M Alnasser, MD1, Facharatz2, Samia A Bokhari, MD, SBEM1, Muneera A Alshareef, MD, SBIM1, Patan M Khan, MD, MRCP1, Abdulla M Mallosho, MD1, Hesham M AbuElsaoud, MD1, Mohammad M Jalal, MD1, Rania F Safwat, MD1, Rehab El Boraie, MD1, Nawaf K Aljabri, MLT3, Bandari K Aljabri, MS4, Arwa Y Alsuraihi, MS4 and Amjad I Hawsawi, MS4; 1Department of Endocrinology, King Fahad Armed Forces Hospital, Jeddah, Kingdom of Saudi Arabia;  2Department of Radiology, King Fahad Armed Forces Hospital, Jeddah, Kingdom of Saudi Arabia;  3Department of Laboratory, Northern Armed Forces Hospital, Haffr Albatin, Kingdom of Saudi Arabia;  4College of Medicine, Umm Al Qura University, Makkah, Kingdom of Saudi Arabia: “Association of Serum Thyroid Stimulating Hormone and Free Thyroxine with Urinary Albumin Excretion in Euthyroid Subjects with Type 2 Diabetes Mellitus”
  3. “Thyroid Disease and Diabetes”; Diabetes Spectrum 2002 Jul; 15(3): 143-143; https://doi.org/10.2337/diaspect.15.3.143
  4. “Thyroid Disease and Diabetes”; By Gary Gilles Medically reviewed by Richard N. Fogoros, MD Updated on March 17, 2019
  5. Paul W. Ladenson, MD; Peter A. Singer, MD; Kenneth B. Ain, MD; et al; “American Thyroid Association Guidelines for Detection of Thyroid Dysfunction”, June12, 2000,
  6. Terrell, Jessica, Corresponding author: (tyrrell@exeter.ac.uk) Diabetes 2020 May; 69(5): “A Mendelian Randomization Study Provides Evidence That Adiposity and Dyslipidemia Lead to Lower Urinary Albumin-to-Creatinine Ratio, a Marker of Microvascular Function”, Francesco Casanova1, Andrew R. Wood, Hanieh Yaghootkar, Robert N. Beaumont, Samuel E. Jones, Kim M. Gooding, Kunihiko Aizawa1, W. David Strain, Andrew T. Hattersley, Faisel Khan, Angela C. Shore1, Timothy M. Frayling and Jessica Tyrrell, 1072-1082. https://doi.org/10.2337/db19-0862
  7. ADA-American Diabetes Association, “The cost of diabetes”
  8. “Microalbuminuria as an Early Marker for Cardiovascular Disease”, Dick de Zeeuw, Hans-Henrik Parving and Robert H. Henning, JASN August 2006, 17 (8) 2100-2105; DOI: https://doi.org/10.1681/ASN.2006050517
  9. “Urinary albumin to creatinine ratio as potential biomarker for cerebral microvascular disease”, Amanda L StricklandHeidi C Rossetti, Roland M Peshock, Myron F WeinerPaul A NakoneznyRoderick W McCollKeith M HulseySandeep R DasKevin S King, Affiliations expand: PMID: 24875487 PMCID: PMC4795813 DOI: 2174/1567202611666140530130327, Published: 13 July 2016, https://www.ncbi.nlm.nih.gov/
  10. “Microvascular endothelial dysfunction is associated with albuminuria and CKD in older adults”, Stephen L. Seliger, Shabnam Salimi, Valerie Pierre, Jamie Giffuni, Leslie Katzel & Afshin Parsa
  11. “Microalbuminuria as an Early Marker for Cardiovascular Disease”, Dick de Zeeuw, Hans-Henrik Parving and Robert H. Henning, JASN August 2006, 17 (8) 2100-2105; DOI: https://doi.org/10.1681/ASN.2006050517
  12. “Comparative effects of microvascular and macrovascular disease on the risk of major outcomes in patients with type 2 diabetes”, Kamel Mohammedi, Mark Woodward, […], and John Chalmers, https://www.google.com/url?sa=t&source=web&cd=&ved=2ahUKEwjL_cPvmd_tAhXSu54KHRPCDWAQFjABegQIDhAL&url=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpmc%2Farticles%2FPMC5530952%2F&usg=AOvVaw0FGrMAPPMR0FVFM4l3T0U7
  13. Hsu, Gerald C., eclaireMD Foundation, USA, No. 034:  “Risk Probability of Kidney Complications Resulting from Chronic Diseases (Math-Physical Medicine)”
  14. Hsu, Gerald C., eclaireMD Foundation, USA, No. 380:      “A summary report of two biomarkers for triglyceride and glucose index (TyG) and accuracy sensitivity analysis in estimating the insulin resistance status based on GH-Method: math-physical medicine”