{"id":60860,"date":"2024-09-30T11:20:44","date_gmt":"2024-09-30T11:20:44","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=60860"},"modified":"2024-10-09T18:09:51","modified_gmt":"2024-10-09T18:09:51","slug":"impact-of-sleep-quality-on-hemogram-derived-inflammatory-indices-in-medical-undergraduates-a-cross-sectional-study","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol17no3\/impact-of-sleep-quality-on-hemogram-derived-inflammatory-indices-in-medical-undergraduates-a-cross-sectional-study\/","title":{"rendered":"Impact of Sleep Quality on Hemogram-Derived Inflammatory Indices in Medical Undergraduates: A Cross-Sectional Study"},"content":{"rendered":"\n

Introduction <\/strong><\/p>\n\n\n\n

Sleep quality is essential for overall\nhealth. Research shows that a substantial portion of the general population,\nbetween 20% and 65%, experiences poor sleep quality, with roughly one-third of\nadults affected by various sleep disorders. (1,2)<\/sup> Among medical\ncollege students, 20-40% are reported to be sleep-deprived, highlighting the\nprevalence of this issue in academia. (3,4)<\/sup> Recent research\nindicates that inadequate sleep quality can negatively impact health through\nbiological pathways, including pro-inflammatory responses. (5)<\/sup> Higher\nlevels of systemic inflammation markers, including fibrinogen, Interleukin 6\n(IL-6), and C-reactive protein (CRP), have been associated with suboptimal\nsleep quality, where elevated levels correlating with worse sleep outcomes. (6)<\/sup> <\/p>\n\n\n\n

Recently, there has been an increasing trend in utilizing hemogram-based inflammatory indices to evaluate systemic inflammation. These indices, including the SII, PLR, and NLR, are derived from routine and cost-effective complete blood count tests. They are reported to offer greater specificity compared to traditional markers such as erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP). (7,8)<\/sup> Additionally, these indices have demonstrated potential in predicting the outcomes of various inflammation-related health conditions. (9)<\/sup> This study explores whether these parameters can provide insights into the relationship between prevalent sleep deprivation and underlying sub-chronic inflammation.<\/p>\n\n\n\n

Aim <\/strong><\/p>\n\n\n\n

This study aims to investigate the association between sleep inconsistency and systemic inflammation using Hemogram Based Inflammatory Indices <\/p>\n\n\n\n

Methodology <\/strong><\/p>\n\n\n\n

This institution-based cross-sectional study was\napproved by the ethics committee and involved 90 undergraduate medical students\naged 18 to 25 years, including 48 males and 42 females, who participated after\nproviding informed consent. Individuals using sedatives, narcotics, or any\ncentral nervous system suppressant for acute or chronic medical conditions were\nexcluded. The questionnaire was designed to screen for subjects who met the\nexclusion criteria. Demographic details (age, gender, weight, height, and\nmedical history) were collected, and Body Mass Index (BMI) and waist-to-hip\nratio (WHR), was calculated.  <\/p>\n\n\n\n

The study used the Pittsburgh Sleep Quality Index\n(PSQI), comprising of 19 items and 7 components to evaluate sleep quality. (10)<\/sup>\nAfter obtaining informed consent, the participants were instructed to track\ntheir sleep patterns and other specified parameters outlined in the\nquestionnaire during weekdays. The participants then completed a questionnaire\non weekends. <\/p>\n\n\n\n

Whole blood samples were collected from all the\nparticipants. Participants were divided into 2 categories based on their PSQI\nscores: Group 1 (PSQI \u2264 5, signifying\ngood sleep quality)\nand Group 2 (PSQI > 5, signifying poor sleep quality). <\/p>\n\n\n\n

The samples were processed in an automated cell counter to assess the complete blood count including the red blood cell count, hemoglobin content, and differential cell count. Further, the hemogram-based inflammatory indices had been derived accordingly. <\/p>\n\n\n\n

SII: calculated as peripheral platelet count \u00d7 neutrophil count\/lymphocyte count. SII = P * N\/L<\/p>\n\n\n\n

PLR: Ratio of peripheral platelet count to lymphocyte blood counts, PLR = P\/L<\/p>\n\n\n\n

NLR: Ratio of neutrophils to lymphocytes, NLR= N\/L <\/p>\n\n\n\n

Statistical Analysis<\/strong><\/p>\n\n\n\n

The data analysis was conducted utilizing SPSS Version 16.0 (Chicago, Illinois, USA), with descriptive findings presented in percentage format. Group comparisons were performed using an unpaired t-test, while correlations were evaluated through Pearson\u2019s correlation analysis. A P-value of less than 0.05 was considered as statistically significant.<\/p>\n\n\n\n

Results <\/strong><\/p>\n\n\n\n

The study included 90 participants, with a\ngender distribution of 53% male and 47% female. The average PSQI score among\nthe participants was 5.9\u00b12.9, and 53% of the participants had a PSQI score\ngreater than 5 (fig-1).\n<\/p>\n\n\n\n

The\nparticipants were categorized into two groups based on their PSQI scores: Group\n1 (PSQI \u2264 5) and Group 2 (PSQI > 5). Demographic characteristics, including\nmean age, BMI, WHR, Hb, and blood indices, were comparable\nbetween the two groups. (Table-1) The group with good sleep quality (PSQI score\n\u2264 5) had a higher proportion of males (57%). (Fig-2) <\/p>\n\n\n\n

Analysis of inflammatory markers revealed\nthat subjects with poor sleep quality (Table-2) had elevated levels of PLR\n(84\u00b123 vs. 100\u00b137; P<0.090), NLR (1.36 \u00b1 0.46 vs. 1.57 \u00b1 0.54: P<0.166),\nand SII (344\u00b1126 vs. 439\u00b1193; P<0.059). <\/p>\n\n\n\n

The PSQI score was comparable between male and female participants (Table-3), but an elevated NLR (1.38 \u00b1 0.5 v\/s. 1.62 \u00b1 0.5: P < 0.120), PLR (89.4 \u00b1 33 v\/s 98.3 \u00b128; P < 0.352), and SII (338 \u00b1 136 v\/s 471\u00b1186; P<0.014) was observed in female subjects (Table-4), and the increase in SII was statistically significant. Additionally, correlation analysis revealed that the PSQI score had a significant positive correlation with NLR, PLR, and SII, particularly among female subjects (r=0.322; p<\/em><0.049) (Table 5).<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure <\/strong>1<\/strong>: Subjects categorised according to PSQI score<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
\"\"<\/td>\n\n

Figure 2: comparison of sleep deprivation among males and females<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 1: Comparison of study parameters among subjects in group-1 and 2 <\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 <\/p>\n<\/td>\n

\n

Group 1 PSQI \u2264 5<\/strong><\/p>\n<\/td>\n

\n

Group 2 PSQI > 5<\/strong><\/p>\n<\/td>\n

\n

P<\/em> value<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

PSQI<\/p>\n<\/td>\n

\n

3.4 \u00b1 1.23<\/p>\n<\/td>\n

\n

8.12 \u00b1 2.04<\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n<\/tr>\n

\n

Gender (M\/F)<\/p>\n<\/td>\n

\n

24\/18<\/p>\n<\/td>\n

\n

24\/24<\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n<\/tr>\n

\n

Age<\/p>\n<\/td>\n

\n

21 \u00b1 1<\/p>\n<\/td>\n

\n

21 \u00b1 1<\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n<\/tr>\n

\n

BMI<\/p>\n<\/td>\n

\n

24 \u00b1 3<\/p>\n<\/td>\n

\n

24 \u00b1 4<\/p>\n<\/td>\n

\n

0.437<\/p>\n<\/td>\n<\/tr>\n

\n

WHR<\/p>\n<\/td>\n

\n

0.83 \u00b1 0.05<\/p>\n<\/td>\n

\n

0.85 \u00b1 0.07<\/p>\n<\/td>\n

\n

0.448<\/p>\n<\/td>\n<\/tr>\n

\n

HB (g\/dL)<\/p>\n<\/td>\n

\n

14.75 \u00b1 1.77<\/p>\n<\/td>\n

\n

14.01 \u00b1 1.69<\/p>\n<\/td>\n

\n

0.171<\/p>\n<\/td>\n<\/tr>\n

\n

TLC (\/\u00b5L)<\/p>\n<\/td>\n

\n

7.93 \u00b1 1.96 X10\u00b3<\/p>\n<\/td>\n

\n

8.09 \u00b1 1.98 X10\u00b3<\/p>\n<\/td>\n

\n

0.795<\/p>\n<\/td>\n<\/tr>\n

\n

MCV (fL)<\/p>\n<\/td>\n

\n

92.85 \u00b1 4.1<\/p>\n<\/td>\n

\n

90.23 \u00b1 6.78<\/p>\n<\/td>\n

\n

0.823<\/p>\n<\/td>\n<\/tr>\n

\n

MCH (pg)<\/p>\n<\/td>\n

\n

30.66 \u00b1 2.35<\/p>\n<\/td>\n

\n

29.39 \u00b1 2.60<\/p>\n<\/td>\n

\n

0.097<\/p>\n<\/td>\n<\/tr>\n

\n

MCHC (g\/dL)<\/p>\n<\/td>\n

\n

32.97 \u00b1 1.24<\/p>\n<\/td>\n

\n

32.53 \u00b1 0.88<\/p>\n<\/td>\n

\n

0.201<\/p>\n<\/td>\n<\/tr>\n

\n

RDW (%)<\/p>\n<\/td>\n

\n

13.78 \u00b1 1.48<\/p>\n<\/td>\n

\n

14.1 \u00b1 1.13<\/p>\n<\/td>\n

\n

0.440<\/p>\n<\/td>\n<\/tr>\n

\n

PLT (\/\u00b5L)<\/p>\n<\/td>\n

\n

273 \u00b1 74 X10\u00b3<\/p>\n<\/td>\n

\n

267 \u00b1 65 X10\u00b3<\/p>\n<\/td>\n

\n

0.794<\/p>\n<\/td>\n<\/tr>\n

\n

PDW (fL)<\/p>\n<\/td>\n

\n

18.62 \u00b1 23.34<\/p>\n<\/td>\n

\n

16.90 \u00b1 21.37<\/p>\n<\/td>\n

\n

0.800<\/p>\n<\/td>\n<\/tr>\n

\n

MPV (fL)<\/p>\n<\/td>\n

\n

11.04 \u00b1 1.15<\/p>\n<\/td>\n

\n

10.76 \u00b1 1.14<\/p>\n<\/td>\n

\n

0.430<\/p>\n<\/td>\n<\/tr>\n

\n

NEU (\/\u00b5L)<\/p>\n<\/td>\n

\n

4.26 \u00b1 1.51 X10\u00b3<\/p>\n<\/td>\n

\n

4.65 \u00b1 1.83 X10\u00b3<\/p>\n<\/td>\n

\n

0.447<\/p>\n<\/td>\n<\/tr>\n

\n

LYM (\/\u00b5L)<\/p>\n<\/td>\n

\n

2.86 \u00b1 0.81 X10\u00b3<\/p>\n<\/td>\n

\n

3.06 \u00b1 0.71 X10\u00b3<\/p>\n<\/td>\n

\n

0.394<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 2: Comparison of inflammatory indices (NLR, PLR, SII) among subjects in group-1 and 2 <\/strong><\/p>\n\n\n\n\n\n\n\n\n\n
\n

 <\/p>\n<\/td>\n

\n

Group 1 PSQI \u2264 5<\/strong><\/p>\n<\/td>\n

\n

Group 2 PSQI > 5<\/strong><\/p>\n<\/td>\n

\n

P<\/em> value<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

PSQI<\/p>\n<\/td>\n

\n

3.4 \u00b1 1.23<\/p>\n<\/td>\n

\n

8.12 \u00b1 2.04<\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n<\/tr>\n

\n

NLR<\/p>\n<\/td>\n

\n

1.36 \u00b1 0.46<\/p>\n<\/td>\n

\n

1.57 \u00b1 0.54<\/p>\n<\/td>\n

\n

P<\/em> < 0.166<\/p>\n<\/td>\n<\/tr>\n

\n

PLR<\/p>\n<\/td>\n

\n

84 \u00b1 24<\/p>\n<\/td>\n

\n

100 \u00b1 37<\/p>\n<\/td>\n

\n

P<\/em> < 0.090<\/p>\n<\/td>\n<\/tr>\n

\n

SII<\/p>\n<\/td>\n

\n

344 \u00b1 126<\/p>\n<\/td>\n

\n

439 \u00b1193<\/p>\n<\/td>\n

\n

P<\/em> < 0.059<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 3: Comparison of study parameters between male and female subjects <\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 <\/p>\n<\/td>\n

\n

MALE<\/strong><\/p>\n<\/td>\n

\n

FEMALE<\/strong><\/p>\n<\/td>\n

\n

P<\/em> value<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Number<\/p>\n<\/td>\n

\n

48<\/p>\n<\/td>\n

\n

42<\/p>\n<\/td>\n

\n

 <\/p>\n<\/td>\n<\/tr>\n

\n

Age<\/p>\n<\/td>\n

\n

21 \u00b1 1<\/p>\n<\/td>\n

\n

21 \u00b1 0.7<\/p>\n<\/td>\n

\n

0.560<\/p>\n<\/td>\n<\/tr>\n

\n

PSQI<\/p>\n<\/td>\n

\n

5.96 \u00b1 3.25<\/p>\n<\/td>\n

\n

6.00 \u00b1 2.51<\/p>\n<\/td>\n

\n

0.964<\/p>\n<\/td>\n<\/tr>\n

\n

BMI<\/p>\n<\/td>\n

\n

23 \u00b14<\/p>\n<\/td>\n

\n

22 \u00b1 4<\/p>\n<\/td>\n

\n

0.607<\/p>\n<\/td>\n<\/tr>\n

\n

WHR<\/p>\n<\/td>\n

\n

0.87 \u00b10.04<\/p>\n<\/td>\n

\n

0.82 \u00b10.07<\/p>\n<\/td>\n

\n

0.041<\/p>\n<\/td>\n<\/tr>\n

\n

HB (gm\/dl)<\/p>\n<\/td>\n

\n

15.59 \u00b1 0.99<\/p>\n<\/td>\n

\n

12.72 \u00b11.03<\/p>\n<\/td>\n

\n

0.000<\/p>\n<\/td>\n<\/tr>\n

\n

TLC (\/\u00b5L)<\/p>\n<\/td>\n

\n

7.64 \u00b1 2.03 X10\u00b3<\/p>\n<\/td>\n

\n

8.52 \u00b1 1.76 X10\u00b3<\/p>\n<\/td>\n

\n

0.130<\/p>\n<\/td>\n<\/tr>\n

\n

MCV (fL)<\/p>\n<\/td>\n

\n

92.55 \u00b14.22<\/p>\n<\/td>\n

\n

89.94 \u00b17.26<\/p>\n<\/td>\n

\n

0.173<\/p>\n<\/td>\n<\/tr>\n

\n

MCH (pg)<\/p>\n<\/td>\n

\n

30.61 \u00b12.02<\/p>\n<\/td>\n

\n

29.12 \u00b12.94<\/p>\n<\/td>\n

\n

0.068<\/p>\n<\/td>\n<\/tr>\n

\n

MCHC (g\/dL)<\/p>\n<\/td>\n

\n

33.04 \u00b10.96<\/p>\n<\/td>\n

\n

32.34 \u00b11.09<\/p>\n<\/td>\n

\n

0.034<\/p>\n<\/td>\n<\/tr>\n

\n

RDW (%)<\/p>\n<\/td>\n

\n

13.56 \u00b11.06<\/p>\n<\/td>\n

\n

14.47 \u00b11.42<\/p>\n<\/td>\n

\n

0.026<\/p>\n<\/td>\n<\/tr>\n

\n

PLT (\/\u00b5L)<\/p>\n<\/td>\n

\n

249.64 \u00b159.38 X10\u00b3<\/p>\n<\/td>\n

\n

297.58 \u00b172.36 X10\u00b3<\/p>\n<\/td>\n

\n

0.025<\/p>\n<\/td>\n<\/tr>\n

\n

PDW (fL)<\/p>\n<\/td>\n

\n

17.07 \u00b120.97<\/p>\n<\/td>\n

\n

18.50 \u00b123.93<\/p>\n<\/td>\n

\n

0.837<\/p>\n<\/td>\n<\/tr>\n

\n

MPV (fL)<\/p>\n<\/td>\n

\n

10.76 \u00b11.06<\/p>\n<\/td>\n

\n

11.06 \u00b11.24<\/p>\n<\/td>\n

\n

0.395<\/p>\n<\/td>\n<\/tr>\n

\n

NEU (\/\u00b5L)<\/p>\n<\/td>\n

\n

4.14 \u00b11.78 X10\u00b3<\/p>\n<\/td>\n

\n

4.92 \u00b11.48 X10\u00b3<\/p>\n<\/td>\n

\n

0.121<\/p>\n<\/td>\n<\/tr>\n

\n

LYM (\/\u00b5L)<\/p>\n<\/td>\n

\n

2.92 \u00b10.92 X10\u00b3<\/p>\n<\/td>\n

\n

3.01 \u00b10.50 X10\u00b3<\/p>\n<\/td>\n

\n

0.697<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 4: Comparison of inflammatory indices (NLR, PLR, SII) among male and female subjects <\/strong><\/p>\n\n\n\n\n\n\n\n\n
\n

 <\/p>\n<\/td>\n

\n

MALE <\/strong><\/p>\n<\/td>\n

\n

FEMALE <\/strong><\/p>\n<\/td>\n

\n

P<\/em> value <\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

NLR<\/p>\n<\/td>\n

\n

1.38 \u00b1 0.50<\/p>\n<\/td>\n

\n

1.62 \u00b1 0.50<\/p>\n<\/td>\n

\n

P<\/em> < 0.120<\/p>\n<\/td>\n<\/tr>\n

\n

PLR<\/p>\n<\/td>\n

\n

89.44 \u00b1 33.81<\/p>\n<\/td>\n

\n

98.32 \u00b128.65<\/p>\n<\/td>\n

\n

P<\/em> < 0.352<\/p>\n<\/td>\n<\/tr>\n

\n

SII<\/p>\n<\/td>\n

\n

338.84 \u00b1 136.22<\/p>\n<\/td>\n

\n

471.74 \u00b1 186.90<\/p>\n<\/td>\n

\n

P<\/em> < 0.014<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 5: Correlation of inflammatory indices (NLR, PLR, SII) with PSQI in female subjects <\/strong><\/p>\n\n\n\n\n\n\n\n\n
\n

 <\/p>\n<\/td>\n

\n

r value<\/strong><\/p>\n<\/td>\n

\n

P <\/em>value<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

NLR<\/p>\n<\/td>\n

\n

0.283<\/p>\n<\/td>\n

\n

0.085<\/p>\n<\/td>\n<\/tr>\n

\n

PLR<\/p>\n<\/td>\n

\n

0.192<\/p>\n<\/td>\n

\n

0.249<\/p>\n<\/td>\n<\/tr>\n

\n

SII<\/p>\n<\/td>\n

\n

0.322<\/p>\n<\/td>\n

\n

0.049<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Discussion <\/strong><\/p>\n\n\n\n

Sleep quality among medical students has been\nstudied worldwide because of its negative health outcomes and consequences on\ntheir academic routines and personal lives. A metanalysis of 57 studies\ninvolving 25,735 medical students worldwide revealed a high prevalence of poor\nsleep quality, with 52.7% of participants experiencing sleep disturbance with a\nmean PSQI score of 6.1. (11) <\/sup>Our findings align with these studies,\nshowing a high rate of sleep inconsistency among our participants, with a mean\nPSQI score of 5.9 \u00b1 2.9 and 53% categorized as poor sleepers. <\/p>\n\n\n\n

Previous studies have explored the\nassociation between sleep quality, and body mass index have reported an\nassociation between short sleep duration and obesity. (12,13)<\/sup>\nContrary to these findings, our analysis did not reveal a significant\nassociation between BMI and sleep quality. This outcome could largely be\nattributed to the fact that the mean BMI of our study population was 24 which falls within the normal weight range according to the World\nHealth Organization’s BMI classifications. <\/p>\n\n\n\n

Gender differences in sleep quality and\npatterns, as well as sleep disorders, are well-documented. Studies have\nconsistently revealed that women experience a higher prevalence of various\nsleep disturbances compared to men. (14) <\/sup>Conversely, men tend to be\nbetter sleepers, exhibiting superior sleep quality, longer sleep duration, and\nhigher sleep efficiency. Our findings support this, as we observed that the\ngroup with good sleep quality (PSQI score \u2264 5) predominantly consisted of\nmales. This is consistent with previous research showing that female college\nstudents tend to have poorer sleep quality, more awakenings, and longer sleep\nlatency. (15,16)<\/sup><\/p>\n\n\n\n

Recent studies have explored the relationship\nbetween sleep quality and the immune system. Some studies have reported that\nsleep disturbances can affect the immune system by weakening its defenses,\nrendering the body more susceptible to various disorders. (17) <\/sup>Conversely,\nother studies have observed that sleep patterns can affect the functionality of\nthe immune system, highlighting a reciprocal and intricate interaction between\nthese two critical aspects of health (18,19).<\/sup> <\/p>\n\n\n\n

A meta-analysis investigating the\nrelationship between NLR and obstructive sleep apnea (OSA) found that NLR\nlevels were significantly higher in OSA patients compared to controls. This\nindicates that NLR may serve as a reliable marker for systemic inflammation and\na predictor of disease severity in OSA patients. (20)<\/sup> In addition,\nthe role of the synaptic adhesion molecule Neuroligin-1 (NLG1) in sleep-wake\nregulation has been demonstrated, suggesting a potential mechanism through\nwhich sleep can influence the NLR. (21) <\/sup>Even though we observed an\nelevation in NLR among students with poor sleep quality (PSQI score >5),\nthese results were not statistically significant and when compared based on gender, it was higher in females. <\/p>\n\n\n\n

The PLR is associated with sleep quality and\nis significantly elevated in patients with OSA, these increases correlate with\nOSA severity. (22) <\/sup>Additionally, PLR serves as an independent marker\nof cardiovascular disease in individuals with sleep apnea. (23) <\/sup>Factors\nsuch as poor sleep quality, fatigue, and vital exhaustion contribute to higher\nplatelet counts, potentially leading to increased PLR. (24) <\/sup>In our\nstudy, we also observed an increase in PLR among students with a score above 5,\nand when compared based on gender, it was higher in females. <\/p>\n\n\n\n

As suggested by previous research, the SII\ncombines neutrophils, platelets, and lymphocytes to represent the systemic\nimmune response and inflammation within the body. (25)<\/sup> In a recent\ncohort study, it was shown that the SII strongly correlates with the severity\nof OSA and exhibits superior performance compared to both NLR and PLR. (26)\n<\/sup>Despite these findings, research on the connection between sleep quality,\nsleep duration, and SII is still relatively unexplored and complex. (27)<\/sup>\nWe noted a statistically significant increase in SII in female participants\nwith sleep disturbance. In addition, a significant positive correlation was\nidentified between the SII and PSQI scores in female subjects compared to NLR\nand PLR. These findings are in line with earlier studies that reported women\nare more susceptible to the effects of poor sleep on systemic inflammation than\nmen, and sleep related disorders had a strong correlation with SII than PLR and\nNLR. (5)<\/sup> <\/p>\n\n\n\n

However, this study’s limitations include a\npotentially limited sample size and restricted generalizability to larger\npopulations, as it exclusively involved medical college students, a single\nethnic group. Potential confounding variables were not controlled including\nsocioeconomic status, psychiatric history, physical activity levels, diet,\nmedication use, or other lifestyle factors that can influence both sleep\nquality and inflammation. Furthermore, the adoption of a cross-sectional study\ndesign limits the inference of causal relationships between sleep quality,\ninflammatory markers, and health outcomes. <\/p>\n\n\n\n

Future research should explore advanced technologies such as wearable devices and AI-based tools for more accurate and real-time monitoring of sleep patterns and inflammatory markers to better understand the molecular mechanism involved. The gender-specific variation in inflammation underscores the importance of considering hormonal, lifestyle, and genetic factors in future research. <\/p>\n\n\n\n

These findings support the development of targeted educational programs on the importance of sleep, especially in environments like medical schools, where students face high stress and irregular schedules. The simplicity of calculating SII, PLR, and NLR from routine blood counts makes these indices valuable for use in clinical practice.<\/p>\n\n\n\n

Conclusion <\/strong><\/p>\n\n\n\n

Our findings emphasize the link between irregular sleep\npatterns and increased systemic inflammation. This highlights the need to\nidentify and address sleep disturbances among medical students, ultimately\nenhancing the effectiveness of medical education programs. <\/p>\n\n\n\n

Acknowledgment<\/strong><\/p>\n\n\n\n

I would like to sincerely thank everyone who supported me in my studies: my guide, my co-authors, and all the participants<\/p>\n\n\n\n

Conflict of Interest<\/strong><\/p>\n\n\n\n

There is no conflict of interest to be declared.<\/p>\n\n\n\n

Funding Sources<\/strong><\/p>\n\n\n\n

Our study is funded by Manipal Academy of Higher Education Seed Grand 2023. The grant number is 205501002\/315\/2022<\/p>\n\n\n\n

References <\/strong><\/p>\n\n\n\n

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