Clinical parameters correlated with the psoas muscle index in Japanese individuals with type 2 diabetes mellitus

Abstract

Aims

Muscle atrophy is a diabetic complication, which results in a deterioration in glycemic control in type 2 diabetes mellitus (T2DM) individuals. The psoas muscle mass index (PMI) is a reliable indicator for estimating whole-body muscle mass. We aimed to examine the relationship between clinical parameters and the PMI to clarify the mechanism underlying muscle atrophy in diabetes.

Methods

This retrospective, cross-sectional study examined 51 patients (31 men and 20 women) with T2DM and a mean HbA1c value of 9.9 ± 1.7%. These patients were admitted to Aichi Medical University Hospital and underwent abdominal computed tomography imaging from July 2020 to April 2021. Multiple clinical parameters were assessed with the PMI.

Results

In a multiple regression analysis adjusted for age and sex, the PMI was correlated with body weight, body mass index, serum concentrations of corrected calcium, aspartate aminotransferase, alanine aminotransferase, creatine kinase, thyroid-stimulating hormone (TSH), urinary C-peptide concentrations, the free triiodothyronine/free thyroxine (FT3/FT4) ratio, and the young adult mean score at the femur neck. Receiver operating characteristic curves were created using TSH concentrations and the FT3/FT4 ratio for diagnosing a low PMI. The area under the curve was 0.593 and 0.699, respectively. The cut-off value with maximum accuracy for TSH concentrations was 1.491 μIU/mL, sensitivity was 56.1%, and specificity was 80.0%. Corresponding values for the FT3/FT4 ratio were 1.723, 78.0, and 66.7%, respectively.

Conclusion

TSH concentrations and the FT3/FT4 ratio are correlated with the PMI, and their thresholds may help prevent muscle mass loss in Japanese individuals with T2DM.

Introduction

Diabetes mellitus causes microvascular complications, such as retinopathy, nephropathy, and polyneuropathy, and also promotes the onset and progression of macrovascular complications, such as cardiovascular and peripheral artery diseases. These diabetic complications, which will increase with our aging society, considerably reduce the patient’s quality of life [1, 2].

Muscle atrophy is also recognized as a diabetic complication [3], and it causes sarcopenia. Sarcopenia is a syndrome that is defined as age-related, progressive, and general loss of skeletal muscle mass and strength, leading to physical dysfunction and decreased quality of life. Some mechanisms of muscle atrophy in diabetes have been reported. First, insulin is important for muscle synthesis. The phosphatidylinositol 3 kinase/Akt pathway stimulates protein synthesis and phosphorylates forkhead box O transcription factors, which stimulate the proteolytic process. Protein synthesis and forkhead box O phosphorylation are decreased in people with diabetes because of diminished insulin signaling and phosphatidylinositol 3 kinase/Akt pathway activity [4]. A specific microRNA named miR-193b, which is increased in individuals with type 2 diabetes mellitus (T2DM) and negatively correlated with muscle mass, induces muscle loss and dysfunction in healthy mice by impaired protein synthesis through the downregulation of 3-phosphoinositide-dependent protein kinase-1 [5]. Diabetes increases pro-inflammatory cytokines, such as interleukin-6 and tumor necrosis factor-α, subsequently activates the nuclear factor-kappa B and signal transducer and activator of transcription 3 pathways, which cause muscle atrophy via upregulation of the ubiquitin/proteasome pathway[6]. Therefore, muscle atrophy is more likely to occur under the diabetic state.

Because the skeletal muscle is a major glucose-consuming organ in the human body, which processes approximately 70–80% of blood glucose by exercise, muscle loss promotes dysregulation of glucose metabolism [7]. Muscle atrophy also causes insulin resistance because of the reduced secretion of myokines [8], which counteracts the metabolic adverse effects of adipocytokines produced by adipose tissue. Therefore, diabetes and reduced muscle mass create a vicious circle together.

Among the various techniques and methods for the assessment of muscle mass, the psoas muscle mass index (PMI) is a reliable indicator for estimating whole-body muscle mass with a high objectivity and reproducibility[9]. The PMI is calculated by dividing the psoas muscle cross-sectional area measured by abdominal computed tomography (CT) at the third lumbar vertebral (L3) level by the square of height in meters. There is a strong correlation between the skeletal muscle mass obtained by bioelectrical impedance analysis and by CT [10]. Currently, the PMI is used in people on dialysis or in those with cancer to determine whether a low PMI is correlated with a poor prognosis of these diseases [11, 12] In research on diabetes and related fields, the PMI has been reported to correlate with the mortality of individuals with T2DM [13, 14] and the effectiveness of surgery for obesity[15], and be useful in screening for pre-diabetic people[16].

However, which parameters are correlated with the PMI in individuals with T2DM are still unclear, especially in those with uncontrolled T2DM. In this study, we examined the relationship between various clinical parameters and the PMI to clarify the mechanism underlying muscle atrophy in diabetes mellitus.

Materials and methods

Patients

This retrospective, cross-sectional study examined the records of 77 individuals with diabetes mellitus who were admitted to Aichi Medical University Hospital (Aichi, Japan) for the purpose of inpatient diabetes management from July 2020 to April 2021. Of these 77 individuals, 65 underwent CT imaging to evaluate diabetic complications and comorbidities. Finally, we analyzed 51 individuals, excluding those who had type 1 diabetes or a history of cancer. Demographic information and physiological and biological data were obtained from medical records in Aichi Medical University Hospital.

This study was approved by the Institutional Review Board of Aichi Medical University (Aichi Medical University Independent Ethics Committee approval number: 2019–133; approval date: 01/10/2020). This study was conducted in compliance with the ethical principles of the Declaration of Helsinki.

Assessment of muscle mass

A multidetector CT scanner (Aquarius iNtuition, ver.4.4.13.P3A; TeraRecon, Inc., Durham, NC, USA) was used to obtain all abdominal CT images taken during the hospitalization. The cross-sectional area of the bilateral psoas muscles at the L3 level was manually measured. The PMI was then calculated as the area of bilateral psoas muscle divided by height squared (Fig. 1). These measurements of muscle mass on CT images were performed by detecting the differences in Hounsfield units (HU), which are the signal intensity of X-ray CT of each tissue. The CT images were defined by the relative value of the absorption rate, with 0 HU for water and 100 HU for air. In this study, ≥ 200 HU was defined as bone, − 30 to − 190 HU was defined as fat, and 0 to 100 HU was defined as muscle. A specific region of muscle was set as the region of interest, and body composition was measured on the basis of the CT value of that region. We were also able to determine a more accurate size of the muscle mass by obtaining the volume of the muscle using multi-slice CT images. However, for reasons of cost, convenience, and low-dose radiation exposure, estimating and calculating the muscle mass of the whole body from the muscle area on a single image was preferable.

Fig. 1.

Method of measuring the bilateral psoas major muscle areas. The bilateral psoas muscle areas were measured by manual tracing using cross-sectional computed tomographic images at the third lumbar vertebral level. The bilateral psoas muscle areas are shown in green, and the other muscles are shown in blue

Statistical analysis

Categorical data are presented as the number or percentage, and continuous data are presented as the mean ± standard deviation. The correlations between the PMI and clinical parameters were analyzed using Spearman’s correlation coefficient for non-normally distributed data or Pearson’s correlation coefficient for normally distributed data. The Mann–Whitney U test was used to analyze the continuous variables nonparametrically. The parameters that were significantly correlated with the PMI were adjusted by age and sex. A multiple regression analysis using adjusted data was then performed.

The PMI cut-off level (6.36 cm²/m² for men and 3.92 cm²/m² for women) [10] was set to detect a low skeletal muscle mass, and patients were divided into a low PMI group and a group with other values. Receiver operating characteristic (ROC) curves were created using the low PMI group and TSH concentrations or the free triiodothyronine/free thyroxine (FT3/FT4) ratio. Then the area under the curve (AUC) was calculated. In this study, p < 0.05 was regarded as statistically significant. All statistical data were analyzed by IBM SPSS Statistics version 20 for Windows (IBM Corp., Armonk, NY, USA).

Results

Baseline characteristics of the patients

The present study population of 51 individuals consisted of 31 (60.8%) men and 20 (39.2%) women. The mean age was 64.0 ± 11.5 years. The mean duration of T2DM was 14.6 ± 12.9 years, and the mean HbA1c value was 9.9% ± 1.7%. The mean PMI was 6.9 ± 1.9 cm²/m² (men: 7.7 ± 1.6 cm²/m²; women: 5.6 ± 1.8 cm²/m²). The prevalence of diabetic retinopathy, nephropathy, and polyneuropathy was 25.5, 39.2, and 42.2%, respectively. Other physiological and biological data (under fasted condition) are shown in Table 1.

Table 1.

Baseline characteristics of the study population

Values	Mean ± SD
Sex (male/female)	31/20
Age (year)	64.0 ± 11.5
Duration of diabetes (year)	14.6 ± 12.9
Height (cm)	162.6 ± 8.9
Body weight (kg)	69.2 ± 16.5
BMI (kg/m2)	26.1 ± 5.5
sBP (mmHg)	136.0 ± 19.0
dBP (mmHg)	79.8 ± 11.8
Right grip strength (kg)	27.8 ± 9.8
Left grip strength (kg)	26.3 ± 10.0
Lumbar spine bone density (% YAM)	106.0 ± 20.8
Right femur bone density (% YAM)	95.5 ± 16.7
Left femur bone density (% YAM)	94.9 ± 18.5
PMI (cm2/m2)	6.9 ± 1.9
Male PMI (cm2/m2)	7.7 ± 1.6
Female PMI (cm2/m2)	5.6 ± 1.8
Tibial nerve NCV (m/s)	42.5 ± 4.2
Tibial nerve amplitude (mV)	15.8 ± 8.7
Sural nerve NCV (m/s)	44.8 ± 4.7
Sural nerve amplitude (µV)	9.1 ± 5.9
TP (g/dL)	6.7 ± 0.5
Albumin (g/dL)	3.9 ± 0.3
BUN (mg/dL)	14.7 ± 5.7
Creatinine (mg/dL)	0.8 ± 0.4
eGFR (mL/min/1.73m2)	80.2 ± 27.6
Serum Na (mmol/L)	140.0 ± 2.2
Serum K (mmol/L)	4.0 ± 0.3
Serum Cl (mmol/L)	103.5 ± 3.1
Serum corrected Ca (mg/dL) #	9.4 ± 0.3
Serum P (mg/dL)	3.5 ± 0.4
TC (mg/dL)	180.8 ± 35.3
HDL (mg/dL)	45.9 ± 12.3
LDL (mg/dL)	102.7 ± 30.0
TG (mg/dL)	152.8 ± 81.5
AST (IU/L)	29.2 ± 20.2
ALT (IU/L)	34.1 ± 27.9
γ-GTP (IU/L)	50.5 ± 52.3
CK (IU/L)	90.6 ± 79.6
FT3 (pg/mL)	2.4 ± 0.3
FT4 (ng/dL)	1.3 ± 0.2
FT3/FT4 ratio	1.9 ± 0.4
TSH (μIU/mL)	1.8 ± 1.3
Plasma glucose (mg/dL)	169.4 ± 47.0
HbA1c (%)	9.9 ± 1.7
Glycoalbumin (%)	26.3 ± 6.5
Insulin (μU/ml)	31.2 ± 55.1
Plasma C-peptide (ng/mL)	2.2 ± 1.2
Plasma pancreatic glucagon (pg/mL)	24.2 ± 13.2
Urinary C-peptide (μg/day)	48.3 ± 42.6
Urinary calcium (mg/dL)	8.3 ± 6.4
Intact PTH (pg/mL)	55.0 ± 22.9
Vitamin B12 (pg/mL)	358.1 ± 176.5
1.25-dihydroxyvitamin D (pg/mL)	51.2 ± 18.8

The data are shown as the mean ± standard deviation

BMI body mass index, sBP systolic blood pressure, dBP diastolic blood pressure, YAM young adult mean, PMI psoas muscle mass index, TP total protein, BUN blood urea nitrogen, eGFR estimated glomerular filtration rate, Na sodium, K potassium, Cl chloride, Ca calcium, P phosphorus, TC total cholesterol, HDL high-density lipoprotein, LDL low-density lipoprotein, TG triglyceride, AST aspartate aminotransferase, ALT alanine aminotransferase, γ-GTP γ-glutamyl transpeptidase, CK creatine kinase, FT3 free triiodothyronine, FT4 free thyroxine, TSH thyroid-stimulating hormone, HbA1c glycosylated hemoglobin, PTH parathyroid hormone

Relationship between the PMI and parameters

The PMI was significantly lower in women than in men, and significantly decreased with age (r =  − 0.357). Among the physical and osseous parameters, the PMI was positively correlated with height (r = 0.403), body weight (r = 0.619), body mass index ([BMI] r = 0.504), bilateral grip strength (right: r = 0.381, left: r = 0.404), value of the bone density of the percentage of Japanese young adult mean (%YAM) at the lumbar spine (r = 0.292), and the femur neck (right: r = 0.647, left: r = 0.682).

With regard to biochemical parameters, the PMI was correlated with serum concentrations of corrected calcium ([Ca] r =  − 0.386), aspartate aminotransferase ([AST] r = 0.366), alanine aminotransferase ([ALT] r = 0.443), creatine kinase ([CK] r = 0.438), free triiodothyronine ([FT3] r = 0.401), and thyroid-stimulating hormone ([TSH] r = 0.401), plasma pancreatic glucagon concentrations (r = 0.308), urinary C-peptide concentrations (r = 0.428), and the FT3/FT4 ratio (r = 0.465) (Table 2).

Table 2.

Correlation between the PMI and clinical parameters

Values	Correlation coefficient
Age (year)	− 0.357*
Duration of diabetes (year)	− 0.037
Height (cm)	0.403**
Body weight (kg)	0.619**
BMI (kg/m2)	0.504*
sBP (mmHg)	− 0.104
dBP (mmHg)	0.027
Right grip strength (kg)	0.381*
Left grip strength (kg)	0.404**
Lumbar spine bone density (%YAM)	0.292*
Right femur bone density (%YAM)	0.647**
Left femur bone density (%YAM)	0.682**
Tibial nerve NCV (m/s)	0.255
Tibial nerve amplitude (mV)	0.005
Sural nerve NCV (m/s)	0.157
Sural nerve amplitude (µV)	0.045
TP (g/dL)	− 0.011
Albumin (g/dL)	0.209
BUN (mg/dL)	0.064
Creatinine (mg/dL)	0.216
eGFR (mL/min/1.73m2)	0.026
Serum Na (mmol/L)	− 0.119
Serum K (mmol/L)	− 0.185
Serum Cl (mmol/L)	− 0.193
Serum corrected Ca (mg/dL)a	− 0.386**
Serum P (mg/dL)	− 0.07
TC (mg/dL)	− 0.119
HDL (mg/dL)	− 0.09
LDL (mg/dL)	− 0.122
TG (mg/dL)	0.15
AST (IU/L)	0.366**
ALT (IU/L)	0.443**
γ-GTP (IU/L)	0.258
CK (IU/L)	0.438**
FT3 (pg/mL)	0.401**
FT4 (ng/dL)	− 0.251
FT3/FT4 ratio	0.465**
TSH (μIU/mL)	0.401**
Plasma glucose (mg/dL)	0.013
HbA1c (%)	− 0.063
Glycoalbumin (%)	− 0.253
Insulin (μU/ml)	0.029
Plasma C-peptide (ng/mL)	0.233
Plasma pancreatic glucagon (pg/mL)	0.308*
Urinary C-peptide (μg/day)	0.428**
Urinary calcium (mg/dL)	− 0.044
Intact PTH (pg/mL)	0.104
Vitamin B12 (pg/mL)	− 0.032
1.25-dihydroxyvitamin D (pg/mL)	0.063

Correlation coefficients were analyzed using Spearman’s correlation coefficient for non-normally distributed data or Pearson’s correlation coefficient for normally distributed data

^*p < 0.05, **p < 0.01

BMI body mass index, sBP systolic blood pressure, dBP diastolic blood pressure, YAM young adult mean, TP total protein, BUN blood urea nitrogen, eGFR estimated glomerular filtration rate, Na sodium, K potassium, Cl chloride, Ca calcium, P phosphorus, TC total cholesterol, HDL high-density lipoprotein, LDL low-density lipoprotein, TG triglyceride, AST aspartate aminotransferase, ALT alanine aminotransferase, γ-GTP γ-glutamyl transpeptidase, CK creatine kinase, FT3 free triiodothyronine, FT4 free thyroxine, TSH thyroid-stimulating hormone, HbA1c glycosylated hemoglobin, PTH parathyroid hormone

^aSerum corrected calcium = serum calcium (mg/dL) + (4–albumin (g/dL)

Relationship between the PMI and parameters adjusted by age and sex

The relationship between each parameter and the PMI was adjusted by age and sex because a previous study by Hamaguchi et al. reported that the PMI was strongly correlated with these factors [10]. In a multiple regression analysis adjusted by age and sex, the PMI was correlated with body weight (β = 0.051, p = 0.002), BMI (β = 0.163, p < 0.001), serum concentrations of corrected Ca (β =  − 1.821, p = 0.023), AST (β = 0.025, p = 0.023), ALT (β = 0.024, p = 0.003), CK (β = 0.007, p = 0.011), and TSH (β = 0.561, p = 0.001), urinary C-peptide concentrations (β = 0.014, p = 0.007), the FT3/FT4 ratio (β = 1.494, p = 0.024), and YAM at the femur neck (right: β = 0.067, p < 0.001; left: β = 0.059, p < 0.001) (Table3).

Table 3.

Multiple regression analysis adjusted by age and sex

	Coefficients	95% CI	p value	Standardized coefficient
Body weight (kg)	0.051	0.02 to 0.081	0.002	0.015
BMI (kg/m2)	0.163	0.084 to 0.242	< 0.001	0.457
Serum corrected Calcium (mg/dL)	− 1.821	− 3.383 to − 0.259	0.023	-0.264
AST (IU/L)	0.025	0.004 to 0.047	0.023	0.262
ALT (IU/L)	0.024	0.009 to 0.039	0.003	0.339
CK (IU/L)	0.007	0.002 to 0.013	0.011	0.299
FT3/FT4 ratio	1.494	0.207 to 2.78	0.024	0.279
TSH (μIU/ml)	0.561	0.234 to 0.889	0.001	0.362
Urinary C-peptide (μg/day)	0.014	0.004 to 0.024	0.007	0.312
Right femur bone density (%YAM)	0.067	0.039 to 0.094	< 0.001	0.589
Left femur bone density (%YAM)	0.059	0.036 to 0.083	< 0.001	0.583

The parameters adjusted by age and sex that were correlated with the PMI in a multiple regression analysis are shown

CI confidence interval, BMI body mass index, Ca calcium, AST aspartate aminotransferase, ALT alanine aminotransferase, CK creatine kinase, TSH thyroid-stimulating hormone, YAM young adult mean

Among the correlated parameters that we detected, TSH, FT3, and FT4 may be treatable. Therefore, we used TSH concentrations and the FT3/FT4 ratio to create ROC curves for the diagnosis of a low PMI. In the ROC curve using TSH concentrations, the maximum accuracy cut-off value was 1.491 μIU/mL, and the AUC was 0.593. The sensitivity was 56.1% and the specificity was 80.0%, respectively (Fig. 2). In the ROC curve using the FT3/FT4 ratio, the maximum accuracy cut-off value was 1.723 and the AUC was 0.699. The sensitivity was 78.1% and the specificity was 66.7%, respectively (Fig. 3).

Fig. 2.

ROC curve for diagnosing a low psoas muscle mass index using thyroid-stimulating hormone. The AUC is the calculated area under the ROC curve. The cut-off value with maximum accuracy, sensitivity, and specificity are shown. ROC, receiver operating characteristics; AUC, area under the curve

Fig. 3.

ROC curve for diagnosing a low psoas muscle mass index using the FT3/FT4 ratio. The AUC is the calculated area under the ROC curve. The cut-off value with maximum accuracy, sensitivity, and specificity are shown. ROC, receiver operating characteristics; AUC, area under the curve; FT3/FT4, free triiodothyronine/free thyroxine

Discussion

In the present study, we examined the PMI in Japanese individuals with uncontrolled T2DM and assessed the factors correlated with PMI. We recruited only those who required inpatient management of diabetes in the current study. Therefore, the participants were relatively old (64.0 ± 11.5 years) and had poorly controlled HbA1c values (9.9 ± 1.7%). Diabetes is an established risk factor for sarcopenia [17, 18], which is characterized by loss of the muscle mass and its function, and it appears to worsen age-dependent muscle weakness [19, 20]. No established standard values for the PMI, especially in the Japanese, are available. Therefore, we selected 6.36 cm²/m² for men and 3.92 cm²/m²for women, which were determined from a healthy young Asian population [10], as cut-off values to define a low muscle mass. Using these criteria, we found seven (22.6%) men and three (15.0%) women with a reduced skeletal muscle mass among the patients.

The multiple logistic regression analysis showed that the PMI in individuals with T2DM was associated with age, height, body weight, BMI, bilateral grip strength, lumbar spine bone density (%YAM), bilateral femur bone density (%YAM), serum concentrations of corrected Ca, AST, ALT, CK, FT3, and TSH, plasma pancreatic glucagon concentrations, urinary C-peptide concentrations, and the FT3/FT4 ratio. After adjusting for age and sex, the correlations of the PMI with body weight, BMI, bilateral femur bone density (%YAM), serum concentrations of corrected Ca, AST, ALT, CK, and TSH, urinary C-peptide concentrations, and the FT3/FT4 ratio remained.

Diabetic polyneuropathy (DPN) is an important diabetic complication, which induces the loss of muscle mass. The severity of DPN associated with leg muscle atrophy [21] and muscle atrophy in individuals with DPN was found to be most prominent in the distal muscles of the lower limbs [22]. Nevertheless, we did not find any relationship between DPN and the PMI in the present study. This difference between studies could be explained by the fact that DPN more strongly affects the “distal” limb muscles than the “proximal” trunk muscles [23].

A positive correlation between the PMI and BMI has been reported in people with wasting diseases, such as renal failure under hemodialysis [11], heart failure [24], and lung cancer [25]. Our finding that the PMI was correlated with the BMI in individuals with poorly controlled T2DM, which is also a wasting disease, is consistent with these previous studies.

We also found that the PMI was positively associated with femur bone density in T2DM. However, spinal bone density did not show a relationship with the PMI after adjustment for age and sex. Although the reason for the difference in the association of the PMI with the femur and spine in our study is unclear, muscle mass and general bone density affect each other [26, 27], especially in T2DM [28]. Increasing evidence has shown that there are many common mechanisms underlying muscle atrophy and a reduction in bone minerals [29]. Furthermore, a decreased muscle mass causes abnormal bone metabolism via diminished muscle pressure [30] or insulin resistance and diabetes [31, 32].

AST isozymes are found in many tissues including the liver, heart, skeletal muscle, kidneys, brain, and red blood cells. ALT is primarily found in liver cells. Therefore, a positive correlation between the PMI and AST and ALT concentrations indicated that increased muscle mass coexists with hepatic disorders. Nonalcoholic fatty liver disease in T2DM, which causes an elevation in hepatic transaminases and insulin resistance in the liver [33], also induces glucagon resistance, subsequent hyperglucagonemia [34], and hyper-amino acidemia [35, 36]. This hyper-amino acidemia provoked by impaired glucagon action in liver might well contribute to the increased muscle mass in T2DM.

Our negative association between the PMI and serum Ca concentrations is perturbing because some previous studies have contradicted the relationship between muscle mass and serum Ca concentrations [37–39].

The serum CK concentration is an established marker of muscle damage because cellular injury can cause CK to leak from sarcous cells into the blood stream [40]. However, whether the serum CK concentration can reflect the steady-state muscle volume is still unclear.

Urinary C-peptide concentrations showed a positive correlation with the PMI. This result suggests that more muscle mass in an individual results in better beta-cell function. Some previous studies are consistent with our finding (i.e., insulin secretion declines under muscle atrophy) [41–43], but some are not (i.e., insulin secretion is increased or unchanged under muscle atrophy) [44, 45].

TSH is a pituitary hormone, which stimulates its intrinsic receptors in the follicular cells of the thyroid gland. This stimulation causes the uptake of iodine into the cytosol from the blood, synthesis of thyroglobulin (a precursor of thyroid hormones), and secretion of thyroid hormones, consisting of triiodothyronine (T3) and thyroxine (T4), into the blood stream [46]. In the normal situation, only approximately one fifth of T3 is produced directly from the thyroid grand, and the rest is derived from deiodination of T4 to T3 by the deiodinases D1 and D2 in other tissues [47]. An in vitro experiment [48] showed that TSH increased the secretion of thyroglobulin with an increased intrinsic ability to form T3, which may result in an increase in the FT3/FT4 ratio. There is a feedback loop, which results in an inverse relationship between TSH and thyroid hormones. In short, low T4 and high T4 concentrations are associated with increased and decreased TSH concentrations. Therefore, high TSH concentrations normally indicate hypothyroidism, and low TSH concentrations indicate hyperthyroidism. Furthermore, “subclinical” thyroid dysfunction, which is often observed in older adults, is defined as abnormal TSH concentrations with normal FT4 concentrations [49], regardless of whether they are symptomatic or asymptomatic [50]. Overt hyperthyroidism (increased thyroid hormone concentrations with decreased TSH concentrations) and hypothyroidism (decreased thyroid hormone concentrations with elevated TSH concentrations) are often treated with anti-thyroid drugs or levothyroxine, respectively. The treatment of subclinical thyroid dysfunction is still controversial.

Overt thyroid dysfunction effects the muscle. Hyperthyroidism, such as in Grave’s disease, can lead to skeletal muscle atrophy and cardiac myopathy. People with hypothyroidism suffer from myalgia, muscle weakness, and exercise intolerance [51]. Furthermore, even subclinical hyperthyroidism reduces muscle mass and function, especially in older people [52]. In the current study, low TSH concentrations and a decreased FT3/FT4 ratio were correlated with a loss in muscle volume. Although most (46/51) patients’ TSH concentrations were above the lower limit (i.e., their thyroid was diagnosed as neither overtly nor subclinically hyperfunctional), slightly low TSH concentrations (but almost within the normal range) were a risk factor for muscle atrophy in people with T2DM. Since such finding were not shown in non-diabetic subjects, we suspect that diabetes amplifies the effects of thyroid hormones and/or TSH on the muscle. Whether this finding might have been caused by relative hyperthyroidism or a direct effect of reduced TSH concentrations is unclear. The presence of biologically active TSH receptors in muscle cells has been shown in vitro [53, 54]. TSH receptor expression was also detected in fibroblast-like cells positioned within the muscles in vivo [55]. Taken together, these findings suggest that TSH may have positive effects on muscle growth and/or resistance to muscle atrophy.

Because T3 has higher biological activity than T4, a high FT3/FT4 ratio often suggests relative hyperthyroidism. Therefore, a positive relationship between the FT3/FT4 ratio and the muscle mass is complicated because it conflicts with our hypothesis about TSH mentioned above and previous research. A high FT3/FT4 ratio is a result of elevated TSH concentrations, which induce thyroglobulin with an intrinsic property to form T3. Although the mechanism underlying the relationship between the FT3/FT4 ratio and muscle is not clear, the FT3/FT4 ratio is still useful for identifying an individual who has a high risk of muscle atrophy.

In the present study, we proposed a unique threshold of TSH concentrations and the FT3/FT4 ratio for detecting therapeutic targets in people with T2DM who have a high risk of muscle mass loss. Future research is required on the prevention of muscle atrophy by elevated TSH concentrations (if possible, the FT3/FT4 ratio) using anti-thyroid drugs.

This study has several limitations. First, we assessed the clinical parameters at a single time point, which may not have been sufficient because these parameters change with the patient’s condition, especially glycemic controls. Second, although we confirmed that no individuals had a history of thyroid disease or intake of anti-thyroid agents and/or thyroid hormones, we had no information regarding thyroid autoantibodies, which may affect TSH receptors. Third, this was a cross-sectional study at a single center, and we were unable to show cause–effect relationships between an associated parameter and the PMI. Forth, we didn’t evaluate fat mass in this study, further studies are needed.

Conclusion

This study shows that the PMI is correlated with body weight, BMI, serum concentrations of corrected Ca, AST, ALT, and CK, the FT3/FT4 ratio, TSH, urinary C-peptide, and the YAM score of the femur neck. The cut-off values with maximum accuracy for the diagnosis of a low PMI were 1.491 μIU/mL for TSH and 1.723 for the FT3/FT4 ratio. These unique thresholds will help detecting the therapeutic targets in the people with T2DM who have a high risk of muscle mass loss and prevent them to spoil their quality of life.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by EA-H, YM, and TH. The first draft of the manuscript was written by EA-H, YM, and TH and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Declarations

Conflict of interest

Hideki Kamiya received lecture fees from Novo Nordisk Pharma, Sanofi, Sumitomo Pharma, Nippon Boehringer Ingelheim, Eli Lilly Japan, Daiichi Sankyo, Ono Pharmaceutical, Kissei Pharmaceutical, Mitsubishi Tanabe Pharma, Kowa, Novartis Pharma, MSD, and Sanwa Kagaku Kenkyusho. Jiro Nakamura received lecture fees from MSD, Novo Nordisk Pharma, Sanofi, Daiichi Sankyo, Ono Pharmaceutical, Novartis Pharma, Taisho Pharmaceutical, Takeda Pharmaceutical, and Terumo. Hideki Kamiya and Jiro Nakamura received research funding from Eli Lilly Japan, Ono Pharmaceutical, and Kissei Pharmaceutical. Hideki Kamiya and Jiro Nakamura received subsidies or donations from MSD, Ono Pharmaceutical, Sumitomo, Pharma, Takeda Pharmaceutical, Mitsubishi Tanabe Pharma, Japan Tobacco, Novo Nordisk Pharma, and Taisho Pharmaceutical. Hideki Kamiya and Jiro Nakamura received endowed departments by commercial entities from Ono Pharmaceutical, Abbott Japan, Sanwa Kagaku Kenkyusho, Kowa, and Terumo. The remaining authors (E Asano-Hayami, Yoshiaki Morishita, T Hayami, Y Shibata, T Kiyose, S Sasajima, Y Hayashi, M Motegi, M Kato, S Asano, H Nakai-Shimoda, Y Yamada, E Miura-Yura, T Himeno, M Kondo, S Tsunekawa, and Y Kato) declare that they have no conflict of interest.

Ethical approval

This study has been approved by the Institutional Review Board of Aichi Medical University (approval number: 2019–133, approval date: 01/10/2020). This study was conducted in compliance with the ethical principles of the Declaration of Helsinki. Informed consent was obtained by the opt-out methods on the website of Aichi Medical University Hospital (https://www.aichi-med-u.ac.jp/hospital/files/byoin/rinH_2019-149.pdf).