Abstract
Introduction
Diabetes, obesity, and CKD collectively impact musculoskeletal health and increase the risk of severe coronavirus disease 2019 (COVID‐19) outcomes.
Methods
This cross‐sectional study included 32 dialysis patients, categorized based on their COVID‐19 status. Laboratory assessments included inflammatory markers (IL‐1β, IL‐6, IL‐8, and TNF‐α). Sarcopenia risk was evaluated using the strength, assistance with walking, rising from a chair, climbing stairs, and falls (SARC‐F) questionnaire, bioimpedance analysis, and static muscle strength testing.
Results
No significant differences were observed between groups in laboratory values, sarcopenia risk, or inflammatory markers. Body composition, SARC‐F scores, and static muscle strength were comparable across both groups, except for elevated parathyroid hormone (PTH) levels in Group A (p = 0.008).
Conclusion
The lack of association between the inflammatory response and sarcopenia risk may be attributed to the existing inflammatory status of this population, given the coexistence of diabetes, CKD, and obesity. Notably, all studied laboratory variables showed no significant differences, except for the higher PTH levels.
Keywords: chronic kidney disease, COVID‐19, diabetes mellitus, hemodialysis, inflammation, obesity
1. INTRODUCTION
The coronavirus disease 2019 (COVID‐19) has profoundly affected the delivery of medical services. 1 Hypertension, DM, cardiovascular diseases, and chronic obstructive pulmonary disease have been identified as comorbidities associated with disease progression and poor outcomes in COVID‐19. 2 Patients with chronic inflammatory conditions are particularly prone to progressing to the hyperinflammatory phase of COVID‐19. 3
Diabetic nephropathy is the leading cause of end‐stage kidney disease worldwide. 4 Diabetes is linked to a chronic inflammatory state, metabolic syndrome, and obesity, which, when combined with COVID‐19, results in the activation of innate and adaptive immunity in adipose tissue, leading to an increased release of inflammatory cytokines both locally and systemically. 5
An analysis of patients admitted to hospitals in China showed that obese individuals have a three‐fold higher risk of severe COVID‐19 outcomes. 6 The relationship between obesity and CKD may be explained by shared pathophysiological mechanisms, risk factors, and associated conditions.
The term “sarcopenic obesity” describes a condition where both sarcopenia and excess adipose tissue coexist, which is associated with higher mortality rates and increased risk for metabolic disorders and cardiovascular diseases. 7
Diabetes, obesity, and CKD are each inflammatory conditions in isolation, but their combined effect has a synergistically detrimental impact, directly compromising muscle mass, strength, and quality of life. Consequently, diabetic patients with concurrent obesity and CKD who contract COVID‐19 may experience serious long‐term sequelae, underscoring the need for further research on this population, which may require extended treatment and follow‐up.
1.1. Objective
To investigate the inflammatory response and predisposition to sarcopenia following COVID‐19 infection among diabetic patients with CKD and overweight/obesity who are currently undergoing HD.
2. MATERIALS AND METHODS
2.1. Study design
This cross‐sectional study included participants over 18 years of age with overweight/obesity, DM, and CKD undergoing HD. The research was conducted across four in‐hospital dialysis centers in Maceió, Alagoas, Brazil, with recruitment and data collection taking place between January and May 2021. All centers are within tertiary hospitals in Alagoas's capital city; two of the hospitals serve the population through the Unified Health System (SUS), while one hospital exclusively treats patients with private health insurance plans.
The inclusion criteria required participants to be over 18 years of age, have a BMI greater than 25 kg/m2, a confirmed diagnosis of DM, and have been undergoing HD for more than 3 months at the study sites. All participants provided signed informed consent. Exclusion criteria included individuals under 18, those with hepatitis B/C, HIV‐positive individuals, those with autoimmune disorders or malignant neoplasms, and pregnant women.
Participants were divided into two groups:
Group A: Overweight/obese (BMI >25 kg/m2) diabetics on HD with a history of SARS‐CoV‐2) (Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection.
Group B: Overweight/obese (BMI >25 kg/m2) diabetics on HD without a history of SARS‐CoV‐2 infection.
Participants with documented COVID‐19 in their medical records were classified as having a prior SARS‐CoV‐2 infection. Confirmation included reverse transcription‐polymerase chain reaction, IgM or IgG serology, or clinical and epidemiological criteria, along with compatible findings on chest computed tomography. The final sample included 32 participants: 12 in Group A and 20 in Group B (see Figure 1).
FIGURE 1.
Flow diagram of participants selection. BMI (kg/m2), COVID‐19+ = Participants with confirmed COVID‐19 Infection, COVID‐19− = Participants with no confirmed COVID‐19 Infection. BMI, body mass index; COVID‐19, coronavirus disease 2019; HD, haemodialysis.
Exposures of interest included prior COVID‐19 infection, dialysis‐dependent CKD, diabetes, inflammatory profile, and sarcopenia risk. Potential confounders were age, sex, duration of HD, additional comorbidities, and time since COVID‐19 infection.
The study was conducted in two stages. The first stage involved collecting data from medical records. The second stage comprised analyzing plasma inflammatory markers, conducting static muscle strength testing, and using bioimpedance to determine lean tissue index (LTI) and fat tissue index (FTI).
Demographic and clinical data were collected from patient records, including recent serum biochemical measurements such as blood count, urea, creatinine, sodium, potassium, calcium, phosphorus, uric acid, albumin, ferritin, and parathyroid hormone (PTH). Albumin, ferritin, and PTH values were obtained from the most recent routine measurements before participants' enrollment in the study. These tests were performed by laboratories affiliated with each dialysis unit and analyzed using chemiluminescence.
2.2. Inflammation marker analysis
Concentrations of inflammatory markers, including IL‐1β, IL‐6, IL‐8, and TNF‐α, were determined using a Sigma‐Aldrich® ELISA commercial kit. Measurements were read at 450 nm, with scans up to 620 nm, and results expressed in picograms per milliliter (pg/mL).
2.3. Muscle strength
Static muscle strength was evaluated with a handgrip test (dynamometry) using a SAEHAN hydraulic dynamometer® (model SH5001). Measurements were taken on both the dominant and non‐dominant arms, noting the arm with the arteriovenous fistula (AVF) used for dialysis. Each arm was tested three times with a 30‐second rest between repetitions, with the highest value recorded. Strength values were later categorized according to the 2019 European Consensus on Sarcopenia, which defines cut‐off points of 27 kg for men and 16 kg for women for identifying sarcopenia risk.
2.4. Body composition
Weight and height measurements were taken immediately following the dialysis session, after obtaining informed consent. A digital scale (maximum capacity 150 kg) and a portable stadiometer were used in accordance with Brazilian Ministry of Health guidelines. For participants unable to stand, knee height was measured using an anthropometric caliper (sensitivity 1 mm, maximum capacity 90 cm), with the Chumlea predictive equation applied. BMI was calculated (kg/m2), with overweight defined as BMI >25 kg/m2 and obesity as BMI >30 kg/m2, following World Health Organization recommendations. 8
The SARC‐F questionnaire was used to assess sarcopenia risk across five functional domains strength (S), assistance with walking (A), rising from a chair (R), climbing stairs (C), and falls (F). A total score above 4 on the SARC‐F indicates an increased sarcopenia risk. Each item is scored from 0 (no impairment) to 3 (unable to perform), with total scores ranging from 0 to 10; a score of 4 or more suggest potential sarcopenia. When calf circumference (CC) is included, it becomes the SARC‐F + CC score, with an additional 10 points if CC is <34 cm in men or <33 cm in women, yielding a total score range of 0–20 points. 9 , 10
CC was measured at the largest point between the ankle and knee using a flexible, non‐stretchable measuring tape, positioned perpendicular to the calf without compression.
Body composition was assessed with a tetrapolar portable bioimpedance device (Fresenius Medical Care® Body Composition Monitor, version 3.3). LTI and FTI values were analyzed. Assessments were performed after dialysis, at the patient's dry weight (postdialysis weight that allows for symptom‐free well‐being). LTI and FTI are calculated by dividing lean or fat mass by height, 2 excluding extracellular fluid. LTI values below the 10th percentile are considered low per the manufacturer's guidelines.
Potential bias factors included inflammatory markers (PTH, IL‐1β, IL‐6, IL‐8, and TNF‐α), age, and survival bias; however, no significant differences were noted between the groups.
2.5. Clinical condition of COVID‐19 patients
Among the 12 COVID‐19‐positive patients in the study, clinical outcomes varied. Six required hospitalization, including three who were admitted to the intensive care unit. Of these, two patients required invasive mechanical ventilation due to severe respiratory failure. This group demonstrated a range of COVID‐19 outcomes, from moderate to severe.
2.6. Statistical analysis
Data were tabulated in Microsoft Excel and analyzed using JASP software (JASP Team, 2022) with a 95% confidence level (p < 0.05). Variable distribution was assessed using Shapiro–Wilk tests and Q‐Q plots, with variance homogeneity verified by Levene's test. For normally distributed variables, Student's t‐test was applied, while the Mann–Whitney U test was used for non‐normally distributed variables. Categorical variables were assessed using Pearson's Chi‐Square test and Fisher's exact test. Continuous variables are presented as mean ± SD or median with interquartile range, while categorical variables are presented in absolute values. Statistical significance was set at p < 0.05 with a 95% confidence interval.
3. RESULTS
The study included 32 participants (Figure 1), comprising 10.3% of patients in the public health system and 12.3% in the private health system among those treated at the dialysis centers. Table 1 describes the demographic and laboratory characteristics of all participants, with an average age of 63.6 ± 8.9 years. Gender distribution was relatively balanced, with 47% female and 53% male participants. The average BMI was 31.0 ± 2.8 kg/m2, reflecting a predominance of obesity over overweight status among the participants. Laboratory values aligned with expectations for a population with CKD undergoing dialysis. All data were complete, with no missing values for the variables of interest in this study.
TABLE 1.
Profile of overweight/obese, diabetic, and HD CKD patients.
| Variable | n (32) | 100% |
|---|---|---|
| Male | 17 | 53.13 |
| Female | 15 | 46.88 |
| Age (years) | 63.59 ± 8.89 | |
| BMI (kg/m2) | 31.04 ± 2.78 | |
| Time in HD (months) | 34.67 ± 24.51 | |
| Urea (mg/dL) | 142.34 ± 39.54 | |
| Creatinine (mg/dL) | 7.93 ± 2.74 | |
| Calcium (mg/dL) | 8.47 ± 0.81 | |
| Phosphorus (mL/dL) | 5.43 ± 1.64 | |
| CaxP product | 45.78 ± 14.79 | |
| Leukocytes (/mm3) | 7488 ± 2516 | |
| Platelets (/mm3) | 258 718 ± 82 543 | |
| Albumin (mg/dL) | 3.76 ± 0.55 | |
| Globulins (mg/dL) | 3.32 ± 1.14 | |
| Ferritin (mg/dL) | 244 ± 187.06 | |
| Iron (mg/dL) | 59.92 ± 25.08 | |
| Alkaline phosphatase (mg/dL) | 126.39 ± 65.69 | |
| PTH intact (pg/mL) | 298.19 ± 311.92 | |
| LTI (kg/m2) | 11.56 ± 2.93 | |
| FTI (kg/m2) | 20.01 ± 4.58 | |
| SARC‐F | 4.11 ± 3.05 | |
| SARC‐F + CC | 9.11 ± 7.15 | |
| CC (cm) | 34.76 ± 2.72 | |
| IL‐6 | 23.49 ± 16.99 | |
| TNF‐α | 42.99 ± 7.75 | |
| IL‐β | 78.74 ± 8.36 | |
| IL‐8 | 112.99 ± 11.87 |
Note: Data presented in numbers, percentages, and mean ± SD.
Abbreviations: CC, calf circumference; FTI, fat tissue index; LTI, lean tissue index; PTH, parathyroid hormone.
Table 2 presents a comparison between Group A (COVID‐19 positive) and Group B (COVID‐19 negative), detailing their demographic and laboratory characteristics. Both groups were comparable in terms of age, gender distribution, and duration of HD treatment. The average time from COVID‐19 diagnosis to study inclusion was 8.4 ± 5.0 months. No statistically significant differences were observed in laboratory variables, including inflammatory markers such as IL‐6 (p = 0.654), TNF‐α (p = 0.374), IL‐1β (p = 0.910), and IL‐8 (p = 0.926), except a higher PTH level in Group A (p = 0.008).
TABLE 2.
Clinical and biochemical data among patients with COVID‐19 (Group A) and without COVID‐19 (Group B).
| COVID‐19+ | COVID‐19 − | p | |
|---|---|---|---|
| n (%) | 12 (37.5%) | 20 (62.5%) | |
| Age (years) | 60.33 ± 8.03 | 65.55 ± 9.00 | 0.109 |
| Time in HD (months) | 28.86 ± 22.98 | 38.15 ± 25.36 | 0.308 |
| Time since COVID‐19 (months) | 8.4 ± 5.0 | ||
| Urea (mg/dL) | 166.59 ± 31.46 | 146.27 ± 42.35 | 0.161 |
| Creatinine (mg/dL) | 7.00 (4.28) | 7.37 (2.01) | 0.716 |
| Calcium (m/dL) | 8.35 ± 0.49 | 8.54 ± 0.96 | 0.530 |
| Phosphorus (mg/dL) | 6.16 (1.92) | 4.72 (2.20) | 0.192 |
| Calcium × phosphorus | 49.32 (11.86) | 43.65 (16.21) | 0.301 |
| Leucocytes (/mm3) | 7006.67 ± 1868.36 | 7777.00 ± 2841.93 | 0.411 |
| Platelets (/mm3) | 299 500 (58000) | 240 000 (92500) | 0.119 |
| Albumin (mg/dL) | 3.73 ± 0.51 | 3.77 ± 0.59 | 0.349 |
| Globulins (mg/dL) | 2.96 ± 0.52 | 3.55 ± 1.37 | 0.739 |
| Ferritin (mg/dL) | 229.07 ± 185.43 | 253.06 ± 192.24 | 0.732 |
| Iron (mg/dL) | 58.50 (39.72) | 54.30 (16.50) | 0.892 |
| Alkaline Phosphatase (mg/dL) | 121.66 ± 46.09 | 129.24 ± 76.09 | 0.758 |
| PTH (pg/mL) | 376.00 (541.00) | 140.00 (196.65) | 0.008 |
| IL6 (pg/mL) | 14.93 (10.00) | 16.25 (12.46) | 0.654 |
| TNF α (pg/mL) | 41.39 ± 7.99 | 43.95 ± 7.65 | 0.374 |
| IL‐β (pg/mL) | 78.97 ± 8.16 | 78.61 ± 8.69 | 0.910 |
| IL‐8 (pg/mL) | 113.24 ± 11.60 | 112.83 ± 12.33 | 0.926 |
Note: Data presented in absolute numbers and percentage, in addition to average ± SD and median (IQR).
Abbreviations: COVID‐19, coronavirus disease 2019; PTH, parathyroid hormone; IQR, interquartile range.
Table 3 provides a comparison of body composition, anthropometry, and muscle strength between the groups. Consistent homogeneity was noted, with no significant differences in variables such as BMI (p = 0.821), LTI (p = 0.280), SARC‐F (p = 0.267), and SARC‐F + CC (p = 0.130). Both groups predominantly consisted of individuals with a LTI below the 10th percentile (55% in Group A and 66% in Group B), alongside a high FTI, indicative of elevated BMI on average. In terms of muscle strength, diminished strength in the dominant arm was observed in both groups, even when considering the arm with an AVF (41.5% in Group A and 65% in Group B).
TABLE 3.
Data on body composition, anthropometry and sarcopenia among patients with COVID‐19 (Group A) and without COVID‐19 (Group B).
| COVID‐19 + | COVID‐19 − | p | |
|---|---|---|---|
| n (%) | 12 (37.5%) | 20 (62.5%) | |
| Weight (kg) | 85.71 ± 12.07 | 82.97 ± 9.30 | 0.477 |
| Height (m) | 1.65 ± 0.01 | 1.64 ± 0.08 | 0.609 |
| BMI (kg/m2) | 31.19 ± 1.89 | 30.95 ± 3.24 | 0.821 |
| LTI (kg/m2) | 10.35 (5.32) | 10.65 (7.05) | 0.280 |
| % Below 10th percentile | 55% | 66% | |
| FTI (kg/m2) | 20.21 ± 6.58 | 16.8 ± 6.99 | 0.218 |
| SARC‐F | 3.00 (2.50) | 4.50 (7.25) | 0.267 |
| SARC‐F + CC | 4.50 ± 7.25 | 12 ± 14.5 | 0.130 |
| CC (cm) | 35.55 ± 2.68 | 34.15 ± 2.69 | 0.230 |
| Dynamometry (kg) | 22.13 ± 8.47 | 18.45 ± 8.19 | 0.224 |
| % Below the cut‐off point | 41.46% | 65% |
Note: Data presented in absolute numbers and percentage, in addition to average ± SD and median (IQR).
Abbreviations: COVID19, coronavirus disease 2019; LTI, lean tissue index; FTI, fat tissue index, IQR, interquartile range; CC, calf circumference.
4. DISCUSSION
In our study, no significant differences were found in laboratory variables between the groups, except for a higher PTH level in the COVID‐19 group. Hematimetric indices, leukocyte and platelet counts, and biochemical markers were similar across groups.
Inflammatory status has been established as an important factor in various pathologies. In CKD, it is associated with higher cardiovascular mortality, malnutrition, and bone mineral disorders. Similarly, in DM and obesity, increased inflammation correlates with worse outcomes. In COVID‐19, inflammation plays a substantial role, particularly in severe cases.
It remains uncertain whether dialysis therapy worsens prognosis in patients with SARS‐CoV‐2 infection, although infections in general can destabilize underlying CKD. Diabetes, CKD, and COVID‐19 are closely linked to inflammatory responses. Both diabetes and CKD promote chronic inflammatory activity, which increases the risk of cardiovascular disease, bone mineral disorders, and premature aging in affected individuals. 11 , 12 In COVID‐19, the acute phase often involves hyperinflammatory responses, which can have catastrophic and even fatal outcomes, with DM being a known risk factor for poor prognosis.
In this study, where CKD patients exhibited a heightened inflammatory status, the study population was primarily overweight or obese. Obesity is known to be associated with increased inflammatory activation and is a major risk factor for poor outcomes in COVID‐19. 13 Obesity significantly impacts health outcomes through mechanisms such as ventilatory difficulties, insulin resistance, hypercoagulable and pro‐inflammatory states, nutritional deficiencies, immune dysregulation, comorbidities, oxidative stress, and lipotoxicity. 14
This study found no differences in laboratory variables, including hematimetric indices (hematocrit and hemoglobin), leukocyte and platelet counts, and biochemical markers such as calcium, phosphorus, uric acid, albumin, and ferritin, when compared between groups. The only variable that differed was PTH, which was elevated in the COVID‐19 group.
Despite the extended time since COVID‐19 diagnosis (mean 8.4 ± 5.0 months) before study inclusion, no long‐term COVID‐19 effects, which are a focus of current literature, were observed. Long‐term COVID‐19 15 may have various effects, including neurological, cardiac, and respiratory symptoms, 16 as well as anxiety, depression, 17 chronic fatigue, headaches, weight loss, and red eyes. 18 While this study did not reveal these changes, it is plausible that differences related to sarcopenia risk and inflammation could exist between groups. However, this observation was not confirmed.
In patients undergoing chronic HD, increased inflammation is associated with a range of adverse outcomes, including higher cardiovascular morbidity and mortality and malnutrition, known collectively as Malnutrition, Inflammation, and Atherosclerosis Syndrome. 19 Additionally, the study population displayed varying clinical manifestations, from mild symptoms to cases requiring intensive care and mechanical ventilation, which may warrant further analysis of the most severe cases.
The observed elevated PTH in the COVID‐19 group, though the study was not designed to examine this specifically, may be relevant. PTH's association with inflammation is well‐documented, not only in CKD but also in primary hyperparathyroidism 20 and obesity‐related primary hyperparathyroidism. 21 Among CKD patients on HD, elevated PTH has been linked to cardiovascular disease, insulin resistance, erythropoietin resistance (contributing to anemia), malnutrition, and cognitive impairments. 22 These associations may relate to PTH's link to pro‐inflammatory factors like FGF‐23, TNF‐α, IL‐1β, TGF‐β, and FGF. 23 Vitamin D levels could have provided further insight due to their direct role in bone metabolism, but these measurements were not routinely available for all patients, precluding analysis.
As no significant differences were observed between the groups, we cannot conclude a change in inflammatory status or sarcopenia risk among participants. However, the lack of an observed association may reflect the inherent inflammation in this population due to DM, CKD, and obesity. Furthermore, this population likely has a high baseline sarcopenia rate, as both groups exhibited reduced muscle strength and low muscle mass, though the full protocol per European Working Group on Sarcopenia in Older People 2 24 criteria was not implemented.
The time elapsed since COVID‐19 diagnosis—more than 3 months for most individuals—may have allowed clinical recovery to pre‐COVID‐19 states, explaining the lack of correlation. Conversely, it is possible that the preexisting inflammatory state masked any potential COVID‐19‐related changes. In a multicenter cohort study by Pilgram et al., 25 chronic dialysis dependency was not found to independently predict disease severity or mortality; instead, age, comorbidities, and other conditions were critical modifiers in these cases.
A potential limitation of this study relates to the inflammatory markers (IL‐1β, IL‐6, IL‐8, and TNF‐α), which exhibit significant variability and can be difficult to interpret. While these markers are validated in the literature as indicators of inflammation, there is no consensus on cutoff points, with no established reference values. Comparisons are thus made between disease populations or different disease stages rather than against absolute norms.
This study may also be subject to survival bias. As described in the literature, 26 the patient profile analyzed is associated with a high probability of mortality if infected. Age‐related survival bias could have influenced the results, although no significant age differences were found between groups.
Finally, the study's sample size was limited to 32 individuals due to participant selection constraints. Although statistical analyses appropriate for the sample size were conducted, larger studies could yield more representative population data.
5. CONCLUSIONS
In this study, individuals with CKD, overweight or obesity, and prior confirmed SARS‐CoV‐2 infection undergoing HD did not show an increased risk of sarcopenia or significant elevations in the inflammatory markers analyzed. Although post‐COVID inflammation was not notably elevated, the higher serum PTH levels in this group suggest that individuals with diabetes, obesity, and CKD on HD may be more vulnerable to symptomatic COVID‐19, particularly with respect to bone mineral density impacted by elevated PTH levels. However, it is important to note that this study was not specifically designed to address this issue, and further research with a larger sample size is warranted.
FUNDING INFORMATION
The authors declare that financial support for this research endeavor was provided by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and the Fundação de Amparo à Pesquisa do Estado de Alagoas (FAPEAL).
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest associated with this study.
ETHICS STATEMENT
The Ethics Approval Statement for this study is as follows: CAAE: 39943920.4.0000.5013, with approval number 4.498.512.
PATIENT CONSENT STATEMENT
All participants included in this study have agreed to and signed the Free and Informed Consent Form (TCLE), as recommended by bioethical protocols.
DATA AVAILABILITY STATEMENT
The authors of the article are willing to furnish the data pertaining to this study.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The authors of the article are willing to furnish the data pertaining to this study.