Association between prediabetes and sarcopenia risk: a systematic review and meta-analysis

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List of abbreviations

ADA—American Diabetes Association

ALM—appendicular lean mass

AMI—appendicular muscle index

AMM/ASM—appendicular muscle mass

ASMBMI—appendicular skeletal muscle mass

index adjusted for BMI

AWGS—Asian Working Group for Sarcopenia

BIA—bioelectrical impedance analysis

BMI—body mass index

DXA—dual-energy x-ray absorptiometry

EWGSOP2—European Working Group

on Sarcopenia in Older People

FNIH—Foundation for the National Institutes

of Health

FPG—fasting plasma glucose

IFG—impaired fasting glucose

IGT—impaired glucose tolerance

NGR—normal glucose regulation

NOS—Newcastle-Ottawa Scale

OGTT—oral glucose tolerance test

OR—odds ratio

PD—prediabetes

T2DM—Type-2 Diabetes Mellitus

BACKGROUND

Sarcopenia is a progressive and generalized musculoskeletal disorder characterized by the age-related decline in skeletal muscle mass, strength, and function, primarily affecting older adults [1]. According to the revised consensus by the European Working Group on Sarcopenia in Older People (EWGSOP2, 2019) and the Asian Working Group for Sarcopenia (AWGS, 2019), the diagnosis of sarcopenia is based on the presence of low muscle strength as a primary indicator, confirmed by low muscle quantity or quality, and further supported by poor physical performance to determine severity [2, 3].

Recognizing sarcopenia is important due to its implications for functional decline, increased risk of adverse health outcomes, and elevated healthcare utilization among aging populations [2, 3]. With the increasing global aging demographic, the prevalence of sarcopenia is estimated to range from 10% to 27% in individuals over 60 years, varying by region and diagnostic criteria [4]. Sarcopenia is frequently associated with multiple chronic conditions, including type 2 diabetes mellitus (T2DM), cardiovascular disease, chronic kidney disease, chronic obstructive pulmonary disease, and cancer, all of which may exacerbate muscle catabolism and functional impairment [5]. Moreover, sarcopenia is an independent predictor of frailty, disability, hospital readmission, and all-cause mortality [5, 6].

Prediabetes is a metabolic condition characterized by blood glucose levels that are above the normal range but not yet meeting the diagnostic threshold for T2DM. It encompasses two main abnormalities: impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), both reflecting disruptions in glucose homeostasis [7]. The pathophysiology of prediabetes involves a combination of insulin resistance, particularly in skeletal muscle and adipose tissue, and an inadequate compensatory insulin secretory response by pancreatic β-cells. In IFG, hepatic insulin resistance predominates, leading to elevated fasting plasma glucose levels, whereas IGT is primarily due to peripheral insulin resistance and delayed postprandial insulin secretion. These abnormalities are often accompanied by chronic low-grade inflammation, oxidative stress, and ectopic fat accumulation, which further impair insulin signaling pathways [8]. Prediabetes has been linked to a reduction in appendicular skeletal muscle mass (ASM) and an elevated risk of developing sarcopenia [9]. Effective management and attenuation of hyperglycemia progression may substantially mitigate the risk and prevalence of sarcopenia [10].

A significant two-way longitudinal relationship between diabetes and sarcopenia has been observed [11]. Nevertheless, it is also hypothesized that prediabetes and sarcopenia may interact through shared and interconnected pathophysiological mechanisms, with each condition potentially contributing to the progression of the other [9, 12]. Skeletal muscle serves as the primary site for insulin-mediated glucose uptake, accounting for approximately 80% of postprandial glucose disposal [13]. Consequently, the loss of muscle mass and function characteristic of sarcopenia can impair glucose utilization, contributing to insulin resistance [8]. Conversely, insulin resistance and hyperglycemia associated with prediabetes can lead to muscle protein degradation, mitochondrial dysfunction, and chronic low-grade inflammation, all of which accelerate muscle atrophy. Recent studies have demonstrated a significant association between prediabetes and sarcopenia [9, 10]. For instance, a cross-sectional analysis of U.S. adults revealed that individuals with sarcopenia had a higher prevalence of prediabetes compared to those without sarcopenia, with sarcopenia being independently associated with an increased risk of prediabetes [14].

Previous studies have largely concentrated on the association between T2DM and sarcopenia risk. A recent meta-analysis by Yogesh et al. revealed that among Asian patients with T2DM, 61% exhibited signs of possible sarcopenia, while 12.1% were affected by severe sarcopenia [15]. Nevertheless, the existing literature on the association between prediabetes and sarcopenia risk remains fragmented, and no quantitative synthesis has yet established whether prediabetes independently increases the risk of sarcopenia or the magnitude of this effect. To address this knowledge gap, we conducted this systematic review and meta-analysis to investigate whether individuals with prediabetes have a higher risk of sarcopenia compared to those with normal glucose regulation (NGR).

METHODS

This systematic review was designed and conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 statement guideline [16]. The protocol was registered at the International Prospective Register of Systematic Reviews (PROSPERO) on May 6th, 2025 with the following registration number: CRD420251038042.

Eligibility criteria

Type of studies

This systematic review included all published observational studies (cross-sectional, cohort studies, and case-control studies) that explored the association between prediabetes and sarcopenia risk. Nevertheless, no unpublished studies that met the eligibility criteria were identified and therefore none were included in the analysis. Conversely, reviews, randomized controlled trial studies, case reports, case series, conference abstracts, book sections, commentaries/editorials, and non-human studies were excluded. Articles without available full- text manuscripts were also excluded.

Participants

Individuals diagnosed with prediabetes, defined by established criteria (e.g., American Diabetes Association [ADA], World Health Organization [WHO], or International Diabetes Federation [IDF), or International Expert Committee [IEC]), such as impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and elevated HbA1c levels within the prediabetes range [7, 17–19]. Patients who are diagnosed with T2DM or type 1 diabetes mellitus (T1DM), children or adolescents (<18 years) were excluded.

Variable and outcome of interest

Our study aimed to evaluate whether individuals with prediabetes have a higher risk of sarcopenia compared to those with NGR. Sarcopenia was defined according to all reported reference criteria, including AWGS, EWGSOP2, Foundation for the National Institutes of Health (FNIH), or other published definitions [2, 3, 20]. The primary outcome of interest was prevalence of sarcopenia. The secondary outcomes were subgroup analyses sex, age, and sarcopenia reference criteria.

Search strategy and study selection

We systematically searched multiple electronic databases including PubMed, ProQuest, ScienceDirect, Google Scholar, and the Cochrane Library for studies published up to May 2025. We utilized both medical subject headings (MeSH) and free-text variation of the keywords “Prediabetes” OR “Sarcopenia”. Two independent reviewers conducted the search using predefined PECOTS-SD criteria, including observational studies of adults aged ≥18 years comparing individuals with prediabetes to those with NGR, assessing the risk of sarcopenia. Further detailed PECOTS-SD criteria and search strategy are provided in Supplementary Files S1 and S2. All identified records were imported into Zotero for reference management and de-duplication. Titles and abstracts were then screened independently, and studies that clearly did not match the review scope were excluded. The remaining articles underwent full-text assessment based on the eligibility criteria. Any disagreements at any stage of study selection or data extraction were resolved by discussion and, when necessary, adjudication by a third reviewer.

Data collection process

The included studies were analyzed and the following data were extracted: first author, publication year, country of origin, study design, sample sizes, age, ethnicity, sex (male), population, prediabetes criteria, HbA1c, sarcopenia prevalence, sarcopenia definition, diagnostic cut-off values (e.g., threshold for handgrip strength, ASM, and other parameters), methods of measurement, and reference criteria used to define sarcopenia (e.g., AWGS, EWGSOP, FNIH, or other published criteria). For mean and standard deviation data reported separately, such as age stratified by sex, we recalculated the values for a combined group using established formulas for pooling data across subgroups [21].

Assessment of risk of bias/ quality assessment

The quality of each study was assessed using the Newcastle-Ottawa Scale (NOS) prior to inclusion in the meta-analysis [22]. Studies were categorized as low quality (scores ≤4), moderate quality (scores 5–6), or high quality (scores ≥7). Those rated as low quality were excluded from the review. Two investigators independently evaluated each study using the NOS. The same consensus process described above was used to resolve any disagreements.

Synthesis of results and statistical analysis

The results from the included studies were synthesized quantitatively using a meta-analysis. Odds ratios (ORs) and their 95% confidence intervals (CIs) were pooled using the generic inverse variance method. A random-effects model was applied to account for potential heterogeneity across studies. Statistical heterogeneity was assessed using the I² statistic, with values of 25%, 50%, and 75% representing low, moderate, and high heterogeneity, respectively [23]. If notable variation was found, we performed sensitivity analyses and subgroup analyses. A p-value less than 0.05 was considered statistically significant. All analyses were carried out using Review Manager version 5.4.

Confidence in cumulative evidence

We evaluated the overall quality of evidence using the GRADE approach [24]. This method looks at factors like study quality, how directly the evidence answers the research question, consistency across studies, precision of the findings, and possible publication bias. Based on these, the certainty of evidence was graded as high, moderate, low, or very low.

RESULTS

Search strategy

Figure 1 shows the flowchart of the study selection process. The initial search identified 252 potentially relevant articles. After screening based on predefined criteria, eleven studies were selected for full-text review. Among these, one was excluded for involving the wrong population, four for reporting an irrelevant outcome, and one for using an unsuitable study design. Finally, five studies met the inclusion criteria for the systematic review. Of these, four were included in the meta-analysis, while one study by Sambashivaiah et al. was excluded due to the absence of extractable quantitative data relevant to the primary objective of this study [25].

 

Fig. 1. Flow Diagram PRISMA 2020.

 

Characteristics of the Included Studies

This review included five observational studies that met the inclusion criteria. All studies were conducted in Asia, with three taking place in China, one in Japan, and one in India. The ADA criteria were the most commonly used for diagnosing prediabetes, while one study applied the Japan Diabetes Society criteria.

In total, 30,511 individuals with NGR and 13,861 with prediabetes were analyzed. The prediabetes participants’ ages ranged from 37.6to 73.78 years, and the proportion of men in the prediabetes category varied between 40.3% and 53.31%. The definition of sarcopenia differed across studies, utilizing reference criteria from the AWGS, EWGSOP2, and the FNIH. Consequently, sarcopenia prevalence in the prediabetes group ranged from 2.4% to 12.14%. Further details can be found in Tables 1 and 2.

 

Table 1

Characteristics of Glucose Status of the Included Studies

No

Author, Year, Country, Study design

Participants (N)

Ethnicity

Age

Sex (Male)

HbA1c

Glucose Status Criteria

NOS

PD

NGR

PD

NGR

PD

NGR

PD

NGR

1

Sambashivaiah, 2019, India, cross-sectional [25]

125

44

Asian Indians

37.6±9.3

32.6±11.4

100%

100%

5.7±0.4

5.3±0.2

American Diabetes Association Prediabetes: FPG 101–125 mg/dL, postprandial plasma glucose 141–199 mg/dL, or HbA1c 5.7–6.4%. NGR: FBG ≤100 mg/dL, postprandial plasma glucose ≤140 mg/dL, or HbA1c ≤5.6%

7

2

Kaga, 2022, Japan, prospective cohort [12]

454

832

Not explicitly specified

Men 73.6±5.0 Women 73.9±5.1 Total: 73.78±5.06

Men 72.1±5.4 Women 72.4±5.5 Total: 72.2±5.47

40.3%

36.53%

Н/Д

Н/Д

Japan Diabetes Society Prediabetes were defined from the remaining participants, except T2DM. NGR: fasting plasma glucose <110 mg/dL, 2-h glucose level after the 75-g OGTT<140 mg/dL, HbA1c <6.5%

7

3

Liu, 2023, China, cross sectional [41]

7427

11 896

White, black, Hispanic, Asian, other

53±17

41±18

49.2%

47.1

5.7±0.3

5.2±0.3

American Diabetes Association NGR: a denied history of diabetes or prediabetes in the questionnaire; HbA1c level <5.7% (6); and fasting glucose level <5.6 mmol/L Prediabetes: the remaining participants, except T2DM

9

4

Li, 2023, China, cross sectional [9]

4808

15 844

Mexican American, other Hispanic, Non-Hispanic White, Non-Hispanic Black, multi-racial

42.92 (42.54; 43.30)

53.31%

50.15%

5.45 (5.43; 5.47)

American Diabetes Association Prediabetes: HbA1c 5.7-6.5% or serum glucose 5.6–7.0 mmol/L in the non-DM population. NGR: others

9

5

Yuan, 2024, China, cohort [10]

1172

1939

Non-Hispanic White, Non-Hispanic Black, Mexican American, other races

64.43±10.18

61.81±9.71

50.5%

44.3%

5.70±0.32

5.27±0.24

American Diabetes Association Prediabetes: a positive answer to the question “Have you ever been told by a doctor that you have prediabetes?” or had FPG ≥6.1, <7.0 mmol/L, or HbA1c ≥5.7%, <6.5%

9

Note. PD, prediabetes; NGR, normal glucose regulation; OGTT, oral glucose tolerance test.

 

Table 2

Characteristics of Sarcopenia of the Included Studies

No	Author, Year, Country, Study design	Sarcopenia Prevalence		Sarcopenia Definition	Methods of measurement	Diagnostic Cut-off values	Reference Criteria	Adjusted Variables	Effect size measures
		PD	NGR
1	Sambashivaiah, 2019, India, cross-sectional [25]	NR	NR	AMI (AMI = AMM/height2) which is equivalent to the sum of lean soft tissue in both the right and left arms and legs	DXA, KinCom dynamometer	NR	Kwon et al., 2015 [42]	NR	NR
2	Kaga, 2022, Japan, prospective cohort [12]	12.14%	9.86%	Low handgrip strength + low appendicular skeletal muscle mass (ASM/height2)	Handgrip dynamometer, BIA (InBody770)	Handgrip strength <28 kg (men), <18 kg (women); ASM <7.0 kg/m2 (men), <5.7 kg/m2 (women)	AWGS 2019	Age, body mass index, % body fat, physical activity and energy intake, and CVD	OR from multivariate logistic regression
3	Liu, 2023, China, cross sectional [41]	3.3%	6.9%	Skeletal Muscle Index (SMI = ASM/height2); Low muscle mass diagnosis	DXA	SMI <7.0 kg/m2 (men), <5.5 kg/m2 (women)	EWGSOP2	Age, sex, race, BMI, current smoking status, educational level and physical activity (MET score)	OR from multivariate logistic regression
4	Li, 2023, China, cross sectional [9]	11.8%	5.41%	Low appendicular skeletal muscle mass relative to BMI (ASMBMI)	DXA	ASMBMI <0.789 (men), <0.512 (women)	FNIH	Age, race, sex, education level, BMI, HTN, anti-glycemic medicine and cholesterol, TG, TC, ALT, SUA, alcohol and cigarette use, TFP, and energy intake	OR from multivariate logistic regression
5	Yuan, 2024, China, cohort [10]	6.6% Severe sarcopenia 2.4%	3.8% Severe sarcopenia 1.6%	Sarcopenia: Low muscle mass (ALM/BMI) and low muscle strength (knee extensor strength) Severe sarcopenia: Low gait speed (LGS)	DXA, KinCom dynamometer, 6-m gait test	ALM/BMI <0.789 (men), <0.512 (women); LMS: <262.25 N (men), <215.10 N (women); Gait speed <0.8 m/s	FNIH	Age, sex, race, BMI, waist circumference, percent of total fat, education level, marital status, PIR, smoked at least 100 cigarettes in life, vigorous activities, sedentary activities, serum 25(OH) D, total bilirubin, uric acid, creatinine, cholesterol, history of cancer and history of osteoporosis or brittle bones	OR from multivariate logistic regression

Note. AMI, appendicular muscle index; AMM, appendicular muscle mass; ALM, appendicular lean mass; ASM, appendicular skeletal muscle mass; ASMBMI, appendicular skeletal muscle mass index adjusted for BMI; BIA, bioelectrical impedance analysis; BMI, body mass index; AWGS, Asian Working Group for Sarcopenia; DXA, dual-energy x-ray absorptiometry EWGSOP2, European Working Group on Sarcopenia in Older People; FNIH, Foundation for the National Institutes of Health; NR, not reported; OR, odds ratio.

 

Meta Analysis Results

The quantitative synthesis of prediabetes and its correlation with the risk of sarcopenia is shown in Figure 2. The pooled OR of 1.29 (95% CI: 1.13–1.47) shows a substantial increase in sarcopenia risk among people with prediabetes (p <0.0001). The pooled OR for the association between prediabetes and sarcopenia risk remained statistically significant across all iterations of the leave-one-out sensitivity analysis, underscoring the robustness of the observed relationship (Supplementary Files S5).

 

Fig. 2. Forest plot of pooled analysis between sarcopenia and odds of sarcopenia.

 

Further subgroup analyses were conducted. A sex-specific analysis revealed a significant correlation between prediabetes and women’s sarcopenia (OR 1.23, 95% CI 1.01–1.49; I²=0%), whereas the association in men was insignificant (OR 1.56, 95% CI 0.98–2.49; I²=47%). However, no subgroup difference according to sex was found (p=0.35). Subgroup analyses according to age and sarcopenia reference criteria also indicated no significant differences (p=0.45 and p=0.98, respectively). Further details are presented in Supplementary Files S6-9.

Quality assessment and Confidence in Cumulative Evidence

The NOS for cross-sectional and cohort studies was used to evaluate the quality of all included research. Table 1 displays the findings of the quality evaluation for each of the included studies. Further detail regarding risk of bias were shown in Supplementary Files S3. When comparing the sarcopenia risk in prediabetes to the NGR group, there was low heterogeneity (I²=7%), suggesting minimal inconsistency. Furthermore, there may have been significant imprecision in the effect estimates because they were derived from a small number of research. Nevertheless, no significant problems with indirectness or publication bias were found that could have affected the final findings. Publication bias was evaluated qualitatively due to the small number of included research, and the literature search did not turn up any unpublished studies, reducing worries about it. As a result, Supplementary Files S4 displays the moderate rating for the quality of the evidence.

DISCUSSION

Our meta-analysis shows a clear association between prediabetes and a higher risk of sarcopenia, suggesting that even early disturbances in blood sugar regulation can negatively affect muscle health. While prior meta-analysis by Qiao et al. [25, 26] had already pointed out that diabetes and its complications are associated with sarcopenia, their findings on prediabetes were based on just one study by Sambashivaiah et al [25, 26]. That study found that people with prediabetes had similar lower muscle mass, strength, and contractility compared to non-diabetic individuals, indicating that muscle loss may begin well before full-blown diabetes sets in [25].

The processes by which prediabetes affects muscles may be similar to those of T2DM, including insulin resistance, an increase in inflammatory cytokines, oxidative stress and an accumulation of AGEs. Insulin plays an anabolic role in skeletal muscle, helping to build and maintain muscle tissue. According to meta-analysis by Abdulla et al, insulin seems to support muscle protein synthesis (MPS) when amino acid levels are high, and it clearly helps reduce muscle protein breakdown (MPB) regardless of amino acid availability [27]. When insulin signaling is impaired, it can disrupt protein synthesis, potentially leading to a loss of muscle mass and strength [28]. The buildup of advanced glycosylation end products (AGEs) in skeletal muscle is encouraged by chronic hyperglycemia and is linked to lower muscle strength, mass, and function [29, 30]. Chronic low-grade inflammation, marked by elevated levels of pro-inflammatory factors like interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP), has been shown to negatively impact muscle mass and function [31]. Hyperglycemia could also increase oxidative stress and decrease antioxidant effects [32]. Patients with T2DM or prediabetes may also develop diabetic neuropathy, which is related to decreased muscular strength. These factors may increase the incidence of sarcopenia in prediabetes [12].

There may also be a bidirectional causal relationship [12]. Since skeletal muscle is one of the main organs of insulin-stimulated glucose absorption during the postprandial state, sarcopenia may result in prediabetes. Sarcopenia could induce insulin resistance via three important mechanisms, which are increased levels of amino acid catabolites in branched-chain skeletal muscle, a reduced proportion of type 1 skeletal muscle fibers, and aberrant lipid infiltration accompanied by lipotoxicity in skeletal muscle. Kalyani et al. 2011, showed a link between reduced muscle mass and elevated glucose levels during OGTT and fasting [33]. Buscemi et al. [34] also indicated that older women with low muscle mass were far more likely to have impaired fasting glucose (IFG), provides evidence for this association. Indeed, IFG was found in 33% of persons with sarcopenia and just 7% of participants without sarcopenia. These results imply that early metabolic dysfunction and muscle loss frequently coexist and may even feed off one another in a vicious cycle.

The finding in this study contradicts previous meta-analyses in diabetic populations, which reported a higher prevalence of sarcopenia among males [35]. However, the chi-square results indicated that no significant subgroup differences were found. This pattern may reflect biological factor. The sex distribution of sarcopenia susceptibility may be explained by changes in estrogen, testosterone, and insulin-like growth factor-1 levels, as well as faster muscle degradation in males and a progressive increase in body fat in women as they age [36].

Subgroup analyses by age showed variable associations between prediabetes and sarcopenia. Although no subgroup differences were found, the link was strongest and significant in younger adults. This contrasts with the theoretical expectation that aging is the primary driver of sarcopenia, given its progressive course and close link to age-related muscle loss largely attributed to motoneuron degeneration [37, 38]. It’s also worth noting that sarcopenia does not affect only the elderly. Recent research has shown that even younger adults with IGT can be affected, and that when sarcopenia is combined with obesity, the risk of developing prediabetes increases substantially [14]. This points to a synergistic effect, where both fat and muscle abnormalities contribute to metabolic problems.

No subgroup differences by sarcopenia criteria were found. The only study using EWGSOP2 showed a significant association, but since it did not assess heterogeneity, that result probably reflect limited evidence rather than true consistency. Overall, the findings support an association between prediabetes and sarcopenia risk but highlight the need for cautious interpretation of age- and sarcopenia reference criteria-specific estimates.

Despite growing evidence, there’s still no universal standard for measuring sarcopenia. In people who are overweight or have metabolic issues, adjusting for body size using ALM/BMI (appendicular lean mass divided by BMI) may offer a more accurate picture of true muscle status [39]. Lastly, we should recognize that sarcopenia is closely tied to other metabolic conditions, such as metabolic syndrome, as noted by Park et al., [40]. However, inconsistent definitions and diagnostic criteria across studies make it difficult to draw firm conclusions or compare findings directly.

Heterogeneity Analysis

Despite some variability in study populations and methodologies, this meta-analysis demonstrated low statistical heterogeneity (I²=7%), suggesting a consistent association between prediabetes and sarcopenia across included studies. However, differences in diagnostic criteria with participant characteristics such as age and sex were present but did not substantially affect the overall findings. These clinical variations may influence generalizability and should be considered in future research.

Publication bias analysis

Given that our meta-analysis had fewer than ten trials, applying a funnel plot assessment could lead to misunderstandings and results that are not trustworthy. As a result, as shown in, we used the GRADE assessment to perform a qualitative assessment. Publication bias frequently arises from authors’ or publishers’ predilection for research with noteworthy results. There was no significant risk of publication bias in any of the studies included in this review, and the overall quality of the evidence for sarcopenia outcomes is moderate.

Strengths and limitations

This is the first thorough systematic review and meta-analysis assessing the risk of sarcopenia in patients with prediabetes. However, there were some limitations on our study. First, the number of eligible studies was relatively small, and there was variation in the reference criteria used to define sarcopenia, with most studies being conducted in Asian populations. However, some studies did include participants from multiple ethnic backgrounds. Finally, as part of the subgroup analysis was based on only one eligible study, the corresponding conclusions should be interpreted with caution.

Future direction

Future research should aim to use consistent definitions and measurement methods to better understand the connection between prediabetes and sarcopenia. Standardized diagnostic criteria would help make results across studies more comparable and improve the accuracy of pooled analyses. As this study did not assess the bidirectional relationship directly, it is still unclear whether prediabetes leads to sarcopenia, whether muscle loss contributes to prediabetes, or if both conditions arise from shared factors like insulin resistance and chronic inflammation. Therefore, studies assessing sarcopenia and prediabetes risk could be explored more. Well-designed longitudinal studies are needed to untangle these relationships and clarify the direction of causality.

CONCLUSION

Our findings highlight that prediabetes is more than just a warning sign for diabetes, it may also contribute to the development of sarcopenia. This underscores the importance of monitoring muscle health early, especially in those with metabolic risk factors. These findings highlight the necessity of detecting and treating prediabetes early in order to lower the odds of sarcopenia and its associated complications. Further studies were needed to validate these findings.

Additional information

Supplement 1. Additional files to the article.

doi: 10.17816/clinpract696288-4418230

Author contribution: H. Nugroho, S. Alvianto, K.G. Pratama, setting the concept and design of the study, data acquisition, drafting and critical revision of the manuscript, approval of the final version of the manuscript; H. Nugroho, S. Alvianto, data analysis and/or interpretation. Thereby, all authors provided approval of the version to be published and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Funding source: This study did not receive any specific grant nor funding from any agencies or sponsors.

Disclosure of interests: The authors declare no conflict of interests.

Statement of originality: The authors confirm that this work is original and has not been published elsewhere. The study is based on a systematic review and meta-analysis of previously published data, all of which have been properly acknowledged.

Data availability statement: The editorial policy regarding data sharing does not apply to this work, data can be published as open access.

Generative AI: Artificial intelligence tools were employed solely to enhance the readability and linguistic quality of this manuscript. All AI-assisted refinements were undertaken under strict human supervision. Following these interventions, the authors conducted a comprehensive review and revision of the text to ensure its accuracy, clarity, and scientific integrity. Recognizing that AI-generated content may occasionally contain errors, omissions, or unintended biases, every component of the manuscript was critically appraised and finalized through human judgment.

About the authors

H. Nugroho

Diponegoro University

Author for correspondence.
Email: khris_heri@yahoo.com
ORCID iD: 0000-0001-7875-6973

MD; Division of Diabetes, Endocrine, and Metabolism, Department of Internal Medicine, Faculty of Medicine

Indonesia, Semarang

S. Alvianto

Atma Jaya Catholic University of Indonesia

Email: alvianto_steven@yahoo.com
ORCID iD: 0000-0003-1783-8531

MD; School of Medicine and Health Sciences

Indonesia, Jakarta

K. Pratama

Diponegoro University

Email: kevingracia14@gmail.com
ORCID iD: 0000-0003-4616-5671

MD; Department of Internal Medicine, Faculty of Medicine

Indonesia, Semarang

References

Supplementary files