Introduction

Individuals with type 2 diabetes mellitus (T2DM) are at a high risk of developing non-alcoholic fatty liver disease (NAFLD), and evidence suggests that poor glycemic control is associated with a higher risk of developing NAFLD [1]. NAFLD comprises a group of increasingly common liver abnormalities that range from simple steatosis to non-alcoholic steatohepatitis (NASH), which may lead to cirrhosis and hepatocellular carcinoma (HCC) [2]. Its prevalence is steadily increasing among people who are obese or have type 2 diabetes (T2DM), and NAFLD is strongly associated with obesity, T2DM, and other features of the metabolic syndrome [3].

NAFLD is a noteworthy public health problem that requires the attention of clinicians and researchers. Understanding the baseline determinants of QoL in the context of NAFLD is critical for patient-centered outcomes and cost-effectiveness research [4, 5]. When compared to other liver diseases caused by alcohol, viruses, autoimmune, or cholestatic hepatopathies, NAFLD patients have lower QoL [6]. NAFLD has a significant impact on QoL as well as societal and financial burdens. Hence, clinicians have recently focused their attention on this topic [7,8,9,10]. QoL results are generally viewed as clinical and scientific end-points in order to treat the disease and make life better [11]. Hence, we aimed to evaluate the QoL in T2DM patients affected by NAFLD, sincere that the study findings would be useful for healthcare providers.

Methodology

It is a hospital-based, single-center, case–control study conducted at Bhargavi Gastro and Surgical Hospital, Warangal, Telangana, with 358 subjects, from November 2019 to October 2021 (24 months). A 358 of cohort type-2 diabetes mellitus (T2DM) subjects were recruited with 1:1 of NAFLD and without NAFLD.

Study subjects

Inclusion criteria

Subjects of either gender of ≥ 18 years of age, with a history of more than 1 year of T2DM, were recommended for ultrasonography (USG). Fatty liver was diagnosed based on histological or imaging findings, and the study participants who did not have a history of alcohol consumption (> 140 g/week) were included in the study.

Exclusion criteria

The exclusion criteria are based on the following: subjects with significant alcohol consumption of > 140 g/week for men and 70 g/week for women, subjects who were on lipid-lowering or any other known causes of long-lasting liver disease such as viral or autoimmune hepatitis, and subjects who are on hepatotoxic medications. The subjects with a history of cancer, renal, respiratory, or hepatobiliary disease; gout; and other rheumatologic diseases are excluded. Subjects with type-1 diabetes, gestational diabetes, or acute complications of diabetes are also excluded. All study participants provided informed written consent prior to participating in the study.

Socio-demographic variables

The data on different socio-demographic variables, including age, gender, education, and annual income, is obtained from the subjects. Anthropometrics like body mass index (BMI) and waist circumference (WC) were measured. BMI was calculated as weight in kilograms divided by the square of height in meters.

Measurement of biochemical parameters

The fasting blood samples were received from each subject to measure biochemical parameters. The parameters include fasting plasma glucose (FBS), glycated hemoglobin (HbA1c), serum uric acid (SUA), total proteins, albumin (A), globulin (G), A/G ratio, serum glutamate pyruvate transaminase (SGPT), serum glutamate oxaloacetate transaminase (SGOT), SGOT:SGPT ratio, alkaline phosphates (ALP), nitrogenous end product-blood urea nitrogen (BUN), serum creatinine (Cr), and fasting lipid profiles that comprise of triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol. All the biochemical parameters were analyzed through standard protocols with the aid of an automated immunochemical analyzer (Abbott AxSYM).

USG of abdomen

Considering the ultrasound imaging reports, the fatty liver was categorized into grades ranging from grade 0 to grade 3. Grade 0 indicates no steatosis with normal echogenicity of the liver; Grade 1 indicates mild steatosis and echogenicity of the liver was greater than that of the right renal cortex, but the echogenic wall of the main portal vein was greater than that of the right renal cortex; Grade 2 indicates moderate steatosis, impaired echogenicity of the main portal vein wall; and Grade 3 represents severe or impaired echogenicity of the main portal vein wall and impaired visualization of the posterior hepatic parenchyma or the diaphragm [12, 13].

Evaluation of QoL

A validated and self-reported questionnaire SF-36 (short form) (Version 1) [14, 15], which contains eight distinct domains was administered to the subjects. Those included (1) the physical functioning (PF) domain assesses how well the patients’ physical activities were restricted due to the subject’s health, (2) the role physical (RP) domain assesses the impact of physical health of the patient in job and routine activities, (3) the bodily pain (BP) domain assesses the subjects’ pain-related limitations, (4) the subject’s personal health and its potential for decline were estimated by the use of general health (GH) domain, (5) the vitality (VT)/energy domain evaluates the sleepiness of the patient, (6) the degree of physical or emotional problems interfere with the normal social activities of the subjects was measured using social functioning (SF) domain, and (7) the influence of patients’ emotional problems on their work and daily activities was estimated by role emotional (RE) domain whereas (8) mental health (MH) domain estimates the state of emotional feeling. The patient’s responses for all the domains were calculated using Likert scales of different sizes ranging from 2 to 6 and then averaged and converted to a range 0–100. A higher score represents the absence of limitations in social functioning that indicate better health and functioning. The above questionnaire scale was (SF-36) translated into the local language (Telugu) by the language experts.

Sample size calculation

The sample size was calculated by using epi info software, assuming the expected alpha error probability to detect the difference in quality of life between non-alcoholic liver disease (NAFLD) subjects and without non-alcoholic liver disease (non-NAFLD) was to be about 5% and the anticipated odds ratio = 2, assuming 95%confidence interval and 90% Power (1-β), expected probability of exposure in control = 36, and expected probability of exposure in case = 52.9. Hence, the minimum sample required was 179 in each group, i.e., case and control. The total sample size was 358.

Statistical analysis

The SPSS v.25 software was used to analyze all of the data. The variables are represented as the mean and standard deviation. The categorical data were analyzed using the chi-square test. A comparison of the two groups was performed with a t test. The relationship between anthropometric and biochemical parameters and the SF-36 domain score was assessed using Spearman correlation. The p values less than 0.05 were considered as statistically significant.

Results

Subjects’ demographic characteristics

The sociodemographic characteristics of subjects with and without NAFLD are represented in Table 1. In the present study, male (61.4%) patients outnumbered female (38.5%) patients. The mean age of NAFLD subjects was 48.94 ± 7.9 years, and without NAFLD, it was 49.28 ± 11.15 years. The literacy levels were significantly high (p 0.021) in patients without NAFLD. Most of the patients were on low incomes. Most of the NAFLD patients disregarded their diabetic dietary modifications advised by the health professionals. The investigations of anthropometric and biochemical investigations between the two groups revealed notable differences. The subjects with NAFLD had higher levels of SGOT and SGPT. In aspects of blood lipids, T2DM-NAFLD displayed significantly higher levels of total cholesterol, LDL, triglycerides, HBA1c, and SUA, and there was no difference in HDL levels (p > 0.05). Diabetics without NAFLD had a high level of FBS and aberrant waist circumference. Lying in the normal ranges, the blood urea levels in both groups were depicted significantly.

Table 1 Socio-demographic and clinical characteristics of study subjects
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Correlation between clinical parameters and SF-36 score

Tables 2 and 3 represent the demographic and clinical variables correlated with the SF-36 score among the subjects. The significance of biochemical parameters on QoL was assessed by correlation analysis between biochemical investigations and eight domains of SF-36 questionnaires in subjects with NAFLD and without NAFLD. The linear dependence of SGPT with respect to physical domain and role was found to be statistically significant in NAFLD subjects (Table 1). We applied Spearman’s correlation coefficient as the variables were measured on an ordinal scale. The study observed an inverse relationship between the raised blood sugar levels (FBS) and the role of physical (p = 0.01) and emotional well-being (p = 0.01) of the subjects. There was a positive correlation between BMI and social function, and general health showed a positive correlation, whereas triglycerides showed a positive correlation with the pain domain. In the NAFLD group, HbA1c had a positive correlation with the physical domain, whereas in the non-NAFLD group, it showed a negative correlation with the mental and a positive correlation with the social function domain. The determination of QoL using SF-36 and NAFLD, being an underlying complication of diabetes, exhibited a strong correlation with biochemical parameters.

Table 2 Correlation between biochemical parameters and quality of life in with NAFLD subjects
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Table 3 Correlation between biochemical parameters and quality of life in subjects without NAFLD
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The general measure of health-related QoL was used again in patients without NAFLD. Pearson’s correlation analysis in this group revealed that LDL has a positive correlation with social function. There was a negative correlation between HDL and the physical domain and a positive correlation with mental health. The SF-36 questionnaire consists of eight domains that are potentially relevant to disease conditions. The questionnaire focused on health-related measures and findings in non-NAFLD subjects who only had diabetes. In this study, we noticed that correlation analysis did not show any significance in most of the eight domains of SF-36 (Table 3).

SF-36 score

QoL was assessed with SF-36 questionnaire scores, and all domains of SF-36 indicated a poor QoL in diabetics with NAFLD, in contrast to diabetic subjects without NAFLD. Among all eight domains of SF-36, lower scores of QoL for physical (51.9), energy (49.78), mental health (55.96), and pain (57.88) were noted in the subjects with NAFLD (Table 4). NAFLD impacted the subject’s energy levels and physical functioning, as evidenced by the lowest subscale; however, even in subjects without NAFLD, general health was impacted. The internal consistency for a response to the SF-36 questionnaire by the subjects was estimated for NAFLD and without NAFLD groups using the Cronbach alpha coefficient. In both groups, the energy, emotional well-being, social functioning, pain, and general health domains revealed poor internal consistency (Table 5). The values of physical function, physical health, and emotional problems were indicated as stable and were unaffected by physiological changes.

Table 4 SF-36 questionnaire score in overall and eight domains
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Table 5 Cronbach alpha coefficient for SF-36 scales in case and control populations
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Table 6 represents the QoL between different grades of fatty liver among 179 NAFLD subjects, 154 subjects had grade 1 fatty liver, and 25 subjects had grade 2 fatty liver. In this study, we observed no difference in the QoL between grade 1 and grade 2 fatty liver subjects. The mean, SD of SF-36 scores, showed no significant difference in contrast to HbA1c among both groups (p > 0.05) (Table 7).

Table 6 Comparison of quality of life between Grade 1 and Grade 2 NAFLD subjects
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Table 7 Comparison of quality of life in type-2 diabetics with and without NAFD based on HbA1c levels
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Discussion

With new recommendations from the American Diabetes Association emphasizing the need for a “patient-centered” approach to the care of T2DM patients in terms of QoL, avoidance of diabetes complications, and accomplishment of glycemic objectives, QoL is increasingly gaining relevance. In contrast to the Indian situation, however, there is extremely little research accessible. Patients with NAFLD have decreased quality of life in the physical domain, indicating poor physical health.

In our study, age did not show a significant correlation with QoL in either group. This finding was similar to other studies reported earlier [16]. In steatohepatitis, elevated transaminases indicate liver injury [17]. In our study, the elevation of transaminases affects the QoL. Similar findings were noticed in previous studies [16, 18]; apart from these results, there was an association between abnormal lipid levels and NAFLD in our study population, and these findings are similar to Persian cohorts [19]. In T2DM patients, the SF-36 questionnaire showed that general health, physical functioning, and energy were most affected. A significant difference in mental health was observed between NAFLD and non-NAFLD patients. Comorbidities affected QoL in five domains in NAFLD patients. Fatty liver, metabolic syndrome, and depression are more common in obese people [20]. Diabetes with NAFLD showed poor QoL in this present study. The mental health domain of the SF-36 questionnaire was negatively affected by NAFLD findings and diabetes alone lowers mental health QoL. This might be due to a lack of adherence to medications, lifestyle, and physical exercise. Mental health issues worsened NAFLD. Both groups had uncontrolled FBS and HbA1c. Hyperglycemic patients are affected by bodily pain the most. The bodily pain score also changed. Diabetes causes nerve function and central pain processing changes in diabetics. The results indicated a deeper insight into pain perception in NAFLD patients [21]. Obesity hinders mental and physical health [22] and decreases QoL [23, 24]. In the present study, body weight was linked with QoL. However, in some studies, no association between BMI and QoL [22]. Unlike QoL in diabetics with NAFLD, several studies support our findings in patients without diabetes [25]. Physical activity was declined with diabetes [26]. In our study, the elevation of FBS levels is significantly high in the non-NAFLD group. In the NAFLD cohort, FBS and QoL were also associated with the RLP domain. NASH patients had lower HRQoL in CRN study [9]. Grade 1 fatty liver had better QoL than grade 2 in our study. T2DM-NAFLD had higher SGOT, SGPT, SUA, blood urea, total cholesterol, LDL, and triglycerides than those without NAFLD. Our study confirmed previous findings that T2DM-NAFLD patients had abnormal liver enzymes and lipid levels [27, 28]. Most of the patients in the NAFLD group had elevated transaminases and dyslipidemia. Furthermore, the present study observed that patients with NAFLD did not follow diabetic dietary recommendations suggested by the physician. They also reported lower physical health, mental health, and pain and energy scores in SF-36 domains when compared to subjects without NAFLD. A positive correlation between BMI, social function, and general health was observed. The SGPT and physical domain were positively correlated in T2DM-NAFLD.

Conclusion

The study observed that NAFLD and an increase in liver enzymes are major determinants of poor QoL in T2DM patients. Fasting blood sugar levels are negatively correlated with role physical and role emotion. Hence, clinicians should be constantly vigilant for NAFLD in T2DM patients, and regular NAFLD screening in type 2 diabetics should be considered and also advocate liver biopsy if the patients are with grade 3 fatty liver.

Limitations of the study

The first limitation is the diagnosis by US that has several limitations; it is subjective and operator-dependent, shows poor sensitivity for the detection of mild steatosis, and is a poor tool for quantifying the steatosis; however, abdominal ultrasonography is currently the most common method employed for qualitative assessment of hepatic steatosis because it is non-invasive, widely available, cheap, and provides useful information. Secondly, the current study was conducted in a single center, which limits the generalizability of the findings. Owing to the study design, causal conclusions cannot be drawn. The shortage of subjects with diabetes and NAFLD in the Indian scenario has made it difficult to compare and interpret different variables used in the study. For generalization of the findings, we recommend prospective multicenter studies with large sample sizes.