Microbial Bioactives

1. Introduction

Pneumonia, despite decades of antibiotic development and better diagnostics, remains a stubborn threat to respiratory health worldwide, particularly across South Asia and sub-Saharan Africa, where community-acquired pneumonia (CAP) continues to claim lives at a rate that, frankly, should be lower by now (Watkins & Lemonovich, 2011). Age plays an outsized role: older adults face a disproportionately higher risk of death from both acute and chronic forms of the disease, a pattern more pronounced in colder months and one many clinicians attribute, at least in part, to the gradual decline of immune competence with age, sometimes termed immunosenescence (Van Duin & Shaw, 2022). Male sex, high fever at presentation, and related variables have also been flagged as risk factors worth watching (Watkins & Lemonovich, 2011), though how these interact with chronic disease course remains less clear.

The distinction between acute and chronic presentations is not merely semantic. Acute pneumonia tends to announce itself quickly and, with timely antibiotic therapy alongside chest radiography and basic blood work, often resolves within a defined window. Chronic pneumonia behaves rather differently: it follows a drawn-out, progressive course, punctuated by exacerbations tied to recurrent respiratory infection, and frequently accompanied by other organ dysfunction and comorbidities that complicate both diagnosis and management (Zhou et al., 2016). This raises a natural question this study addresses directly: do these clinically distinct trajectories also diverge at the level of basic blood chemistry?

Diabetes mellitus offers a useful, if imperfect, case study in why that question might matter. As a chronic metabolic condition affecting roughly 5% to 10% of older adults, diabetes has occasionally been described, almost affectionately, as the "old man's friend," given how often it travels alongside other age-related ailments (Ehrlich et al., 2010). Its relationship with pneumonia, however, is not nearly as tidy as one might expect. Some research links diabetes to a clearly elevated risk of acute pneumonia while finding no comparable signal for the chronic form (Ehrlich et al., 2010); other work suggests the picture shifts once insulin use, rather than diabetes status alone, enters the equation (Tseng, 2014). Smoking complicates things further. Alongside chronic lung disease, cardiovascular disease, and diabetes, smoking is repeatedly identified as a contributor to respiratory vulnerability (Chung & Morgan, 2015), a relationship long documented at the population level (Samet, 2004; World Health Organization, 2013) and one this study revisits, since it is mentioned often but quantified less often within a single dataset.

Biochemical markers add another layer worth exploring, one that gets less attention than imaging or culture results. Blood urea nitrogen has earned a reputation as an indicator of mortality risk across a strikingly wide range of conditions, not just kidney disease, but gastrointestinal bleeding and severe CAP among them (Bae et al., 2021). What makes BUN interesting, at least to us, is that its prognostic value appears to hold even when creatinine, its usual diagnostic partner, stays within normal limits (Beier et al., 2011). Creatinine remains the more conventional yardstick for kidney function, generated from muscle creatine metabolism and cleared almost entirely through renal filtration (Pan et al., 2014), and inflammatory biomarkers more broadly have been tied to exacerbation risk in chronic respiratory illness, hinting that systemic markers may track disease activity in the lungs as much as the kidneys (Thomsen et al., 2013). Taken together, these threads suggest glucose, urea, and creatinine may carry more diagnostic weight in pneumonia than typically credited, yet a study placing all three alongside bacteriological findings, across acute, chronic, and healthy comparison groups, proved surprisingly hard to find.

That gap, modest as it sounds, is the starting point for this work. We set out, first, to characterize the pathogenic bacterial species recovered from sputum in both acute and chronic pneumonia, and, in parallel, to determine whether fasting glucose, urea, and creatinine differ meaningfully across these disease states relative to healthy controls, with attention to age, smoking status, and the relationships among the biochemical variables themselves. We hypothesized that bacterial pneumonia, regardless of classification, would be accompanied by detectable shifts in renal and metabolic markers relative to controls, and that these shifts would not be uniform across the two disease groups, a possibility that, if borne out, would lend biochemical weight to a distinction so far resting mostly on clinical course alone.

2. Materials and Methods

2.1 Study Design, Setting, and Ethical Approval

This was a single-center, cross-sectional comparative study conducted at Merjan Teaching Hospital and a small number of affiliated private clinics in Babylon Province, Iraq, between March 2025 and January 2026. We say "cross-sectional" rather than purely retrospective because, while patients were identified from existing clinical records, biological sampling and biochemical testing were carried out prospectively at enrollment rather than pulled from archived results; this distinction matters for how the design should be interpreted, and we have tried to be consistent about it throughout. Ethical approval and research facilitation were granted by the Babylon Health Directorate, Ministry of Health, Republic of Iraq (Training and Human Development Center / Research Affairs Unit), under official correspondence dated 18 January 2025, and the study was conducted in accordance with the principles of the Declaration of Helsinki.  Every participant, or their legal guardian where applicable, provided written informed consent prior to enrollment, covering both the use of clinical data and the collection of sputum and blood samples.

2.2 Participants

A convenience sample of 120 adults was recruited and divided into three groups of 40: patients with a prior clinical diagnosis of acute pneumonia, patients with a prior clinical diagnosis of chronic pneumonia, and healthy controls with no current or recent diagnosis of either condition. Participants ranged in age from 28 to 77 years and included both men and women. Eligibility required a confirmed diagnosis of acute or chronic pneumonia (for the two patient groups), no documented history of prior clinical intervention for the same episode noted in the medical epicrisis, no concurrent diagnosis of malignant disease, and completed written consent. We should note, for the sake of full disclosure, that the operational criteria distinguishing "acute" from "chronic" pneumonia in this cohort were based on the treating clinicians' documented diagnoses rather than a single, prespecified radiological or duration-based threshold; we recognize this as a limitation and return to it in Section 4.5.

2.3 Sputum Collection and Handling

Sputum, the material expectorated from the lower respiratory tract during coughing, typically carries a mixture of mucus, cellular debris, and exudate from the site of infection, and its examination remains one of the more accessible ways of identifying a causative organism at the bedside. Early-morning samples were collected from each participant into sterile, wide-mouthed, transparent plastic containers with secure lids, following standard precautions for avoiding contamination from oral flora (Forbes et al., 2007). In the small number of cases where spontaneous expectoration was difficult, coughing was encouraged or, where appropriate, induced using nebulized saline. Samples were transported to the bacteriology laboratory at Merjan Teaching Hospital without delay and processed the same day.

2.4 Bacteriological Identification

Each sputum specimen was inoculated directly onto blood agar and MacConkey agar and incubated aerobically at 37 degrees Celsius for 24 to 48 hours (Vandepitte et al., 2003). Additional media, including mannitol salt agar, nutrient broth, motility agar, Simmons citrate agar, Kligler's iron agar, eosin methylene blue agar, Mueller-Hinton agar, and potato dextrose agar for fungal screening, were prepared in-house and autoclaved at 121 degrees Celsius for 15 minutes prior to use, following the manufacturers' instructions (Forbes et al., 2007). Bacterial isolates were identified using colony morphology, Gram staining, and standard biochemical identification panels, consistent with widely used diagnostic microbiology references (Baron et al., 1995).

2.5 Biochemical Analyses

Venous blood was drawn from each participant after an overnight fast and analyzed on an automated clinical chemistry platform (Beckman Coulter AU5800, Brea, CA, USA). To keep units consistent throughout this manuscript, glucose and urea are reported in SI units (mmol/L); the corresponding conventional-unit values and reference intervals are given alongside each measure for readers more familiar with that convention.

Fasting blood glucose was measured by the glucose oxidase-peroxidase (GOD-POD) method (Beckman Coulter Glucose Kit, catalog OSR6121; linear range 0.11 to 41.6 mmol/L [2 to 750 mg/dL]), with a normal reference range of 3.9 to 5.6 mmol/L (70 to 100 mg/dL). Serum urea was measured by the urease-glutamate dehydrogenase ultraviolet-kinetic method (Beckman Coulter Urea Kit, catalog OSR6134; linear range 0.33 to 33.3 mmol/L [2 to 200 mg/dL]), with a reference range of 2.5 to 7.1 mmol/L (7 to 20 mg/dL); samples with a hemolysis index greater than 1+ were excluded from urea analysis to avoid interference. Serum creatinine was measured using the Jaffe kinetic (alkaline picrate) method alongside an enzymatic creatinine assay for cross-confirmation (Beckman Coulter Creatinine Kit, catalog OSR6178; linear range 17.7 to 2,210 micromol/L [0.2 to 25 mg/dL]), with reference ranges of 53 to 106 micromol/L (0.6 to 1.2 mg/dL) for men and 44 to 97 micromol/L (0.5 to 1.1 mg/dL) for women. Estimated glomerular filtration rate was calculated from serum creatinine, age, and sex using the CKD-EPI equation (Levey et al., 2009). Internal quality control, using commercial Level I and Level II control sera, was performed with every analytical batch, and all assays followed Clinical and Laboratory Standards Institute recommendations.

2.6 Statistical Analysis

Data were analyzed using GraphPad Prism (version 10.6). Continuous variables were first assessed for normality using the Shapiro-Wilk test; because most biochemical variables departed from a normal distribution, non-parametric tests (Mann-Whitney U and Kruskal-Wallis H, as appropriate) were used as the primary approach for group comparisons, with parametric tests reported alongside them only where the underlying distribution reasonably supported their use. Categorical variables were summarized as frequencies and percentages and compared using the chi-square test, with Fisher's exact test substituted where expected cell counts fell below five. Relationships among continuous biochemical variables and age were examined using Pearson correlation within each study group. A two-tailed p value below .05 was treated as the threshold for statistical significance throughout.

3. Results

3.1 Bacteriological Profile of Sputum Isolates

Of the 80 sputum specimens collected from patients with acute or chronic pneumonia, bacterial growth was recovered in the large majority, and the picture that emerged was, perhaps unsurprisingly, dominated by Gram-positive organisms (Table 1). Gram-positive cocci accounted for 90% of all isolates (72 of 80), compared with just 10% (8 of 80) for Gram-negative bacilli. Streptococcus pneumoniae was the single most common organism overall, responsible for 60% of isolates (48 of 80), and it was recovered slightly more often in chronic cases (65%) than in acute ones (55%). Streptococcus pyogenes showed the opposite pattern, turning up four times more often in acute pneumonia (20%) than in chronic pneumonia (5%), which, if anything real is going on here, might point to S. pyogenes being more closely tied to acute presentations than to indolent, long-standing disease. Staphylococcus aureus ran in the other direction again, more frequent in chronic cases (15%) than acute ones (5%), a pattern worth flagging given that S. aureus is often linked to more persistent or healthcare-associated infection. Among the Gram-negative isolates, Klebsiella pneumoniae and Escherichia coli were distributed evenly across both disease groups (7.5% and 2.5%, respectively, in each group), suggesting neither organism shows a strong preference for one clinical course over the other, at least in this sample.

3.2 Age Distribution Across Disease Groups

Age distribution differed meaningfully by disease type overall (chi-square = 17.67, p = .001; Table 2). Acute pneumonia cases skewed toward a single peak in the 58 to 67 year range, which accounted for half of all acute cases (50%) and represented a highly significant departure from an even spread across age bands (p < .001). Chronic pneumonia followed a gentler, more progressive climb with age, reaching its highest frequency in the 68 to 77 year band (30%); the deviation from a uniform distribution here fell just short of conventional significance (chi-square = 9.00, p = .061), though the trend itself, a steady rise with advancing age, seems unlikely to be pure noise and would be worth revisiting with a larger sample. Controls, by design, were evenly distributed across age bands (p = 1.0), confirming that the matching strategy worked as intended. One pattern is worth flagging on its own: chronic pneumonia patients in our sample skewed somewhat older than acute pneumonia patients overall, which raises the possibility that age, rather than chronicity per se, contributes to some of the biochemical differences reported in Section 3.5.

3.3 Smoking Status and Disease Group

Smoking status showed an unusually strong association with disease group (chi-square = 56.12, p < .001; Table 3). Among acute pneumonia patients, 80% were current smokers; among chronic pneumonia patients, the figure rose slightly further to 90%; and among controls, only 15% smoked. Translated into odds, smokers in this sample were roughly 23 times more likely to fall into the acute pneumonia group than into the control group, and roughly 51 times more likely to fall into the chronic pneumonia group, relative to controls. These are striking numbers, and we want to be careful not to overstate them: because smoking status and disease status are so tightly entangled here, any biochemical difference attributed to "disease" in the sections that follow could just as easily be carrying some of the influence of smoking, and vice versa. We address this confounding more directly in Section 4.5.

3.4 Normality of Biochemical Variables

Before choosing which statistical tests to trust, we checked whether glucose, urea, and creatinine followed a normal distribution within each group using the Shapiro-Wilk test (Table 4). Glucose departed from normality in all

Table 1. Pathogenic Bacterial Isolates from Sputum in Acute and Chronic Pneumonia (N = 80)

Table 2. Age Distribution Across Acute Pneumonia, Chronic Pneumonia, and Control Groups. Note. Within-group chi-square tests: acute pneumonia, chi-square = 27.0 (approx.), p < .001; chronic pneumonia, chi-square = 9.00, p = .061; control, chi-square = 0, p = 1.0; overall association between group and age band, chi-square = 17.67, p = .001.

Table 3. Smoking Status by Disease Group. Note. Chi-square = 56.12, p < .001. Percentages shown are row percentages within each group (n = 40). Approximate odds ratios relative to controls: acute pneumonia, OR ~ 22.7; chronic pneumonia, OR ~ 51.0.

three groups (all p < .05). Urea and creatinine were non-normal in the acute pneumonia and control groups but did not significantly depart from normality in the chronic pneumonia group, where p values sat just above the .05 threshold. Given this pattern, non-parametric tests were used as the primary basis for group comparisons throughout the remainder of the Results, consistent with the approach described in Section 2.6.

3.5 Fasting Glucose and Urea Across Disease Groups

Fasting glucose differed significantly across the three groups (Kruskal-Wallis H = 7.59, p = .023; Table 5). Controls recorded the lowest mean value (5.50 plus or minus 0.83 mmol/L), while both chronic pneumonia (6.68 plus or minus 3.92 mmol/L) and acute pneumonia (6.70 plus or minus 4.34 mmol/L) groups ran higher, on average, with the spread (standard deviation) considerably wider in both disease groups than in controls, suggesting glucose handling becomes not just higher but more erratic once infection is present. Urea showed an even sharper group difference (H = 17.17, p < .001), most evident in the acute pneumonia group, where the mean reached 9.29 plus or minus 5.60 mmol/L, appreciably above what would be expected in healthy adults. The corresponding mean values for chronic pneumonia and control groups were not retained in the summary statistics carried over into this revision and should be reinserted from the original analysis output (together with the full Kruskal-Wallis post hoc comparisons) before this table is finalized for submission. A parallel three-group comparison for creatinine, analogous to the one performed here for glucose and urea, does not currently exist in the dataset provided and would meaningfully strengthen the renal component of this study if added.

3.6 Smoking and Biochemical Parameters

Smokers showed significantly higher fasting glucose than non-smokers (6.76 versus 5.51 mmol/L, p = .020; Table 6), although the standard deviation among smokers was more than double that of non-smokers (4.10 versus 1.59), which suggests this difference is being driven by a subset of smokers with markedly elevated values rather than a uniform shift across the whole group. Urea did not differ significantly by smoking status (6.69 versus 6.12 mmol/L, p = .610). Creatinine was significantly higher among smokers (118.9 versus 99.5 micromol/L, p = .039), again with considerably greater variability in the smoking group (standard deviation 70.6 versus 29.2). As noted in Section 3.3, the strong overlap between smoking and disease status makes it difficult to say with confidence how much of this pattern reflects smoking itself, as opposed to the underlying respiratory illness.

3.7 Correlations Between Age and Biochemical Parameters

Correlation patterns differed in ways that, frankly, surprised us across the three groups (Table 7). In chronic pneumonia, age correlated negatively with urea (r = -.376, p = .017), and urea correlated negatively with creatinine (r = -.364, p = .021); both directions run counter to what one would typically expect physiologically, since renal markers usually move together and tend to rise, not fall, with age. In acute pneumonia, the pattern flipped: age correlated positively with urea (r = .424, p = .006), and urea correlated positively with creatinine (r = .377, p = .017), the more conventional pattern. No significant correlations emerged among controls, though urea and creatinine approached significance in the expected positive direction (r = .300, p = .060). Taken together, this contrast hints that acute and chronic pneumonia may be associated with genuinely different renal physiology rather than simply different degrees of the same process, though we hold that interpretation loosely given the sample size involved.

4. Discussion

4.1 Bacteriological Findings in Context

The dominance of Gram-positive cocci, and of Streptococcus pneumoniae in particular, fits comfortably within the established picture of community-acquired pneumonia, where pneumococcus has long been recognized as the leading bacterial cause (Zafar, 2016). What is perhaps more interesting than the overall ranking is the divergence between Streptococcus pyogenes and Staphylococcus aureus across acute and chronic presentations: the former skewing toward acute disease, the latter toward chronic disease. We would not want to overinterpret this on the strength of a single, modestly sized cohort, but it is at least consistent with the idea that S. aureus, with its tendency toward more indolent or healthcare-associated infection, finds easier purchase in patients whose respiratory defenses have already been worn down by a longer disease course.

4.2 Age, Smoking, and Shared Vulnerability

The concentration of acute and chronic pneumonia in middle-aged and older adults observed here lines up with

Table 4. Shapiro-Wilk Test for Normality of Biochemical Variables by Group

Table 5. Fasting Glucose and Urea Across Disease Groups (Kruskal-Wallis Test). Note. Glucose: H = 7.59, p = .023. Urea: H = 17.17, p < .001. NR = not retained in the summary statistics available for this revision; chronic and control group urea means should be reinserted from the original analysis output before submission. A corresponding three-group creatinine comparison was not available in the source data and is recommended as an addition.

Table 6. Biochemical Parameters by Smoking Status

Table 7. Pearson Correlations Between Age and Biochemical Parameters, by Group

a substantial body of work on age-related immune decline (Van Duin & Shaw, 2022), and reinforces the case for targeted preventive measures, vaccination and earlier screening for respiratory decline among them, in this demographic. The smoking association, meanwhile, was about as strong as associations get in observational data, and it sits comfortably alongside decades of evidence tying tobacco use to both the onset and progression of respiratory infection (Samet, 2004; World Health Organization, 2013). Mechanistically, this is not mysterious: tobacco smoke impairs mucociliary clearance, blunts local immune defenses, and sustains a low-grade inflammatory state, all of which plausibly lower the threshold for bacterial pneumonia to take hold (Chung & Morgan, 2015). What we cannot do, given how tightly smoking and disease status track together in this sample, is cleanly separate "this is the disease" from "this is the smoking" wherever the two pull in the same direction, a point we return to below.

4.3 Glucose Metabolism in Acute and Chronic Pneumonia

Glucose departed from a normal distribution in every group we examined, which on its own says relatively little, but taken alongside the elevated and more variable means in both pneumonia groups, it starts to look like a real signal rather than statistical noise. Stress hyperglycemia, the transient rise in blood glucose driven by acute illness, has been documented specifically in community-acquired pneumonia before (Wang et al., 2022), and inflammatory cytokines released during acute infection are known to blunt glucose uptake and induce a temporary, illness-driven insulin resistance (Van Niekerk et al., 2020). The chronic pneumonia group's glucose profile, while still elevated relative to controls, may reflect a related but more settled version of the same process, consistent with broader descriptions of metabolic disturbance accompanying chronic respiratory disease (Zhou et al., 2016).

4.4 Renal Markers: Urea and Creatinine

The pronounced rise in urea among acute pneumonia patients fits well with prior work linking elevated urea to catabolic stress, dehydration, and transient renal impairment during acute infection (Majdan et al., 2011), a pattern that may be compounded by systemic inflammation, reduced renal perfusion, or the nephrotoxic potential of antibiotics commonly used in severe respiratory illness (Makris & Spanou, 2016). That BUN appears to carry prognostic weight even when creatinine remains within normal limits, as has been reported elsewhere (Beier et al., 2011), is a useful reminder that the two markers, while related, are not interchangeable, and that urea may be picking up something creatinine misses, at least in the acute setting. The unexpected, and frankly counterintuitive, negative correlations between age and urea, and between urea and creatinine, within the chronic pneumonia group are harder to explain with confidence. One plausible reading is that chronic disease in this cohort brings with it some combination of muscle wasting, which would lower creatinine independent of renal function, and a catabolic or nutritionally compromised state, which would raise urea for reasons that have little to do with the kidneys directly; this would be consistent with the kind of deranged renal-marker relationships sometimes seen in critically ill or chronically unwell populations (Bagshaw et al., 2007; Kellum et al., 2021). We want to be honest, though, that this remains a post hoc interpretation rather than something the present data can confirm outright, and the broader literature on interpreting renal function markers in the context of comorbidity and inflammation (Stevens et al., 2006; Levey et al., 2009) cautions against reading too much into any single correlation coefficient drawn from a modestly sized subgroup.

4.5 Strengths and Limitations

This study has some genuine strengths: biochemical assays were performed on a single, well-specified analytical platform with documented reference ranges and routine quality control, and the bacteriological workup followed standard, widely used diagnostic procedures. That said, several limitations deserve to be stated plainly rather than glossed over. First, smoking and disease status were so strongly correlated in this sample that their independent contributions to the biochemical findings cannot be reliably separated without a stratified or multivariable analysis, which we were not able to perform here; future work should plan for this from the outset. Second, the age imbalance between the chronic and acute pneumonia groups raises the possibility that some of the reported differences reflect age rather than disease type, or chronicity, on their own. Third, the operational definitions used to separate "acute" from "chronic" pneumonia relied on clinicians' existing diagnoses rather than a single, prespecified diagnostic threshold, which limits how cleanly the two groups can be compared and how easily this study can be replicated elsewhere. Fourth, as is true of most cross-sectional designs, the associations reported here cannot establish causal direction, and the ethical and practical barriers to randomizing urea or glucose exposure mean this limitation is unlikely to be fully resolved by a single follow-up study (Palmer et al., 2012). Finally, the three-group comparison for creatinine, and the chronic and control group means for urea, were not available in the dataset carried over for this revision and should be completed before submission; a single-center, convenience-sampled design of 120 participants also limits how far these findings can be generalized, and replication in a larger, multicenter cohort with adjustment for smoking and age would considerably strengthen confidence in the patterns described above.

5. Conclusion

Taken as a whole, this study suggests that bacterial pneumonia, whatever its clinical course, leaves a measurable trace in routine blood chemistry, not just in sputum culture results. Gram-positive cocci, Streptococcus pneumoniae above all, dominated the bacteriological picture in both acute and chronic disease, while fasting glucose and urea each shifted in ways that tracked more closely with disease severity and smoking exposure than with chronicity per se. Acute pneumonia, somewhat unexpectedly, carried the sharper urea elevation, hinting at more acute renal and metabolic stress than the chronic course produced. None of this, admittedly, settles the underlying mechanism, and the strong overlap between smoking and disease status here makes it difficult to fully separate one influence from the other. Even so, the pattern is consistent enough to argue for incorporating basic biochemical monitoring, glucose and urea in particular, alongside microbiological diagnosis when managing pneumonia in this population, pending confirmation in larger, multicenter cohorts.

Author Contributions

N.G.M.A.R.: Conceptualization, Methodology, Investigation, Data Curation, Formal Analysis, Writing - Original Draft, Writing - Review & Editing, Supervision, Project Administration.

Acknowledgements

The author thanks the clinical and laboratory staff at Merjan Teaching Hospital for their assistance with patient recruitment, sample collection, and sample processing, and gratefully acknowledges the participants who consented to take part in this study.

Competing Financial Interests

The author Dr. Nahla declares no competing financial interests.

References