Assessing the risk of peripheral neuropathy among male tobacco smokers with type 2 diabetes: a matched case-control study in an Arabic-speaking country

doi:10.21203/rs.3.rs-5237206/v1

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Assessing the risk of peripheral neuropathy among male tobacco smokers with type 2 diabetes: a matched case-control study in an Arabic-speaking country

https://doi.org/10.21203/rs.3.rs-5237206/v1

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The increasing prevalence of diabetic complications and tobacco consumption are alarming worldwide. This study aimed to assess the association between tobacco smoking and diabetic peripheral neuropathy (DPN) among male smokers with type 2 diabetes in the United Arab Emirates (UAE).

In a matched case-control study, participants were recruited from two major hospitals in Sharjah. Male patients aged ≥18 years and diagnosed with DPN who had type 2 diabetes mellitus for at least one year were recruited. Age-(± 3 years)-matched controls were recruited for each case. Sociodemographic characteristics and self-reported data on physical activity, tobacco smoking were collected. Smoking status was assessed as current, past, or never. Medical and clinical data were collected from hospital records.

An equal 140 of each cases and controls were included in the analysis, with a mean age of 63.75 ± 10.61 and 63.69 ± 10.50, respectively. The case group was more likely to smoke and had a longer smoking duration than the control group. After adjusting for all plausible confounders, the association between tobacco smoking and DPN was significant (past smokers [OR, 4.12; 95% CI 1.84–9.21], and current smokers [OR, 2.63; 95% CI 1.09–6.35]).

This study found a significant association between tobacco consumption and DPN among males with diabetes in the UAE. Targeted smoking cessation programs are urgently needed.

Health sciences/Diseases

Health sciences/Endocrinology

Health sciences/Health care

Health sciences/Medical research

Health sciences/Neurology

Health sciences/Risk factors

Diabetes mellitus

Peripheral neuropathy

tobacco smoking

Diabetes mellitus is a global public health concern that places a great burden on individuals’ health and the healthcare system alike [1]. The prevalence of diabetes complications is also alarmingly high, which can be microvascular (e.g., neuropathy, retinopathy, and nephropathy) and macrovascular (e.g., coronary artery disease, peripheral artery disease, and cerebrovascular disease) [2]. In 2021, around 12.2% of the global mortality from all causes was diabetes-related [3]. The increasing burden of diabetes mellitus and its complications has led to a drastic 316% increase in global health spending on diabetes over the past fifteen years [3].

Diabetic peripheral neuropathy (DPN) is the most common among all diabetes complications [2], affecting nearly 50% of individuals with diabetes at some point in their lifetime [4]. DPN accounts for 75–90% of all neuropathy cases in individuals with diabetes [5] and leads to the highest hospitalization rate among all diabetes complications combined [6–7]. DPN worsens the burden of diabetes substantially, contributing to reduced quality of life [8] and increasing the risk of foot ulcers, infections, amputation, and disability [2, 9]. Individuals with painful DPN are known to be at higher risk of developing mental disorders, including depression, anxiety, and sleep disturbance [10]. The nerve damage induced by DPN is usually irreversible, highlighting the necessity of risk factor prevention and early detection to mitigate its devastating consequences [11].

Non-modifiable risk factors for DPN have been proposed to include age at diagnosis, duration of diabetes, microalbuminuria, and macroalbuminuria [12–13] and modifiable risk factors include tobacco smoking, high body mass index (BMI), and elevated glycosylated haemoglobin (HbA1C) levels [12–13]. Evidence from various populations over the past two decades has reported a link between tobacco smoking and DPN [14], this is of great magnitude given the substantial prevalence of diabetes [3] and widespread use of tobacco globally [16 − 15].

In the Middle East, there were 73 million cases of diabetes in 2021, projected to reach 136 million by 2045, and indicating an 87% increase in diabetes prevalence [3]. The prevalence of DPN is also notably high in the Middle East [17–21], with a varying prevalence between 9% and 61% [22], and nearly 5.3% of all persons with diabetes found to have neuropathy in UAE [3].

According to the World Health Organization (WHO) 2020 report, the prevalence of tobacco consumption in the Eastern Mediterranean region, known as the Middle East, was 18.3%, with the vast majority of smokers being adult males [15]. The case is relevant in a country such as the United Arab Emirates (UAE), where the use of tobacco products is considerably high among males, with a prevalence of 36% for the current use of any tobacco products and 29% for tobacco cigarettes [23–24]

Existing evidence within this demographic region has focused only on DPN and revealed that smoking is associated with an increased risk of DPN among individuals with diabetes [25–28]. These studies also did not primarily focus on smoking as a risk factor for DPN, but smoking was studied among other possible risk factors, which could compromise the findings from these studies due to the inclusion of multiple exposures and outcomes, the lack of a primary focus on smoking, and the scarcity of research focusing on sex-specific differences in the tendency to smoke. To our knowledge, no study exists on this association, particularly among male individuals with diabetes in the UAE, meaning that the association remains unexplored in this population. Thus, this case-control study aimed to examine the association between tobacco smoking and DPN among male individuals with type 2 diabetes in the UAE.

A total of 280 males were enrolled in this study, and the mean (SD) age for the case was 63.7 (10.6) and for controls was 63.7 (10.50). (Table 1).

Table 1

Characteristics of Study Participants (N = 280)
Characteristics	Cases	Controls	Total	p-value
Age in years - mean (Sd)	63.75 (10.61)	63.69 (10.50)	63.72 (10.54)	0.96
Educational level
High School- n (%)	95 (67.86)	84 (60)	179 (63.93)	0.171
Graduate and above - n (%)	45 (32.14)	56 (40)	101 (36.07)	0.171
BMI categories (kg/M²) - n (%)
Normal (< 25)	24 (17.14)	28 (20)	52 (18.57)	0.182
Overweight (25–30)	55 (39.29)	66 (47.14)	121 (43.21)
Obese (> 30)	61 (43.57)	46 (32.86)	107 (38.21)
Sedentary activity per/min/day- mean (SD)	936.46 (181.05)	951 (133.02)	944.02 (158.83)	0.43
Physical Activity - n (%)
Sufficient (> 600 MET)	23 (16.43)	30 (21.43)	53 (18.93)	0.286
Insufficient (< 600 MET)	117 (83.57)	110 (78.57)	227 (81.07)	0.286
Dyslipidaemia - n (%)
Yes	129 (92.14)	123 (87.86)	252 (90)	0.232
No	11 (7.86)	17 (12.14)	28 (10)	0.232
Presence of major comorbidities - n (%)
Yes	108 (77.14)	94 (67.14)	202 (72.14)	0.062
No	32 (22.86)	46 (32.86)	78 (27.86)	0.062
Family history of DM - n (%)
Yes	98 (70.00)	108 (77.14)	206 (73.57)	0.175
No	42 (30)	32 (22.86)	74 (26.43)	0.175
Early onset of DM - n (%)
Yes	39 (28.06)	21 (15)	60 (21.51)	0.008*
No	100 (71.94)	119 (85)	219 (78.49)	0.008*
Duration of DM - n (%)
Newly diagnosed	12 (8.63)	25 (17.86)	37 (13.26)	0.046*
Short duration	24 (17.27)	31 (22.14)	55 (19.71)
Intermediate duration	42 (30.22)	39 (27.86)	81 (29.03)
Long duration	61 (43.88)	45 (32.14)	106 (37.99)
HbA1C% - n (%)
< 7%	64 (45.71)	60 (42.86)	124 (44.29)	0.630
≥ 7%	76 (54.29)	80 (57.14)	156 (55.71)	0.630
Insulin as initiation therapy - n (%)
Yes	17 (12.14)	9 (6.43)	26 (9.29)	0.100
No	123 (87.86)	131 (93.57)	254 (90.71)	0.100
Current Insulin therapy - n (%)
Yes	52 (37.14)	26 (18.57)	78 (27.86)	0.001*
No	88 (62.86)	114 (81.43)	202 (72.14)	0.001*
Presence of DM complications (microvascular or/and macrovascular) - n (%)
Yes	95 (67.86)	59 (42.14)	154 (55)	< 0.001*
No	45 (32.14)	81 (57.86)	126 (45)	< 0.001*
Presence of diabetic nephropathy - n (%)
Yes	50 (35.71)	26 (18.57)	76 (27.14)	0.001*
No	90 (64.29)	114 (81.43)	204 (72.86)	0.001*
Presence of diabetic retinopathies - n (%)
Yes	64 (45.71)	35 (25)	99 (35.36)	< 0.001*
No	76 (54.29)	105 (75)	181 (64.64	< 0.001*
Presence of diabetic foot/ ulcer - n (%)
Yes	6 (4.29)	6 (4.29)	12 (4.29)	1.000
No	134 (95.71)	134 (95.71)	268 (95.71)	1.000
Herbal supplements consumption - n (%)
Yes	8 (5.71)	2 (1.43)	10 (3.57)	0.053
No	132 (94.29)	138 (98.57)	270 (96.43)	0.053
Vitamin B supplement intake - n (%)
Yes	100 (71.43)	61 (43.57)	161 (57.50)	< 0.001*
No	40 (28.57)	79 (56.43)	119 (42.50)	< 0.001*
p < 0.05*

Participants in the case group had lower tertiary education attainment (32.14% vs 40.0%), longer diabetes duration (p = 0.046), and more frequently reported diabetes-related complications (p < 0.001) than those in the control group. The prevalence of obesity was also higher in the case group (43.57%) than in the control group (32.86%).

Diabetic microvascular complications were more frequently reported in the case group: retinopathy was present in nearly half of the cases (45.71%) and nephropathy in 35.71%. Similarly, the presence of chronic comorbidities (77.14 vs. 67.14) and dyslipidaemia (92.14 vs. 87.86) was more frequent in cases than in controls. Compared to the controls, the cases reported a higher use of vitamin B supplements (71.43% vs. 43.57%), and insulin as initiation therapy (12.14% vs. 6.43%) and as a current diabetes treatment modality (37.14% vs. 18.57%).

Most study participants had insufficient physical activity (81.07%) and reported a sedentary lifestyle (average sitting hours per day = 15.73). Table 1.

Participants in the case group were significantly more likely to smoke: 24.29% were current smokers and 36.43% were past smokers. Approximately 83.02% of current smokers smoked daily, 64.71% smoked tobacco cigarettes, 23.53% smoked waterpipes (also called shisha), and 11.76% smoked e-cigarettes.

Moreover, there was a statistically significant difference between the mean initial age (p = 0.0001) and regular smoking age (p = 0.0001) between the case and control groups (for both, past and current smoking). Overall, cases had a longer duration of smoking than the control (p = < 0.001). Table 2

Table 2

History of Smoking
	Cases	Controls	Total	p-value
Current smokers - n (%)	34 (24.29)	19 (13.57)	53 (18.93)	< 0.001*
Past smokers - n (%)	51 (36.43)	29 (20.71)	80 (28.57)	< 0.001*
Smoking initiation age - mean (SD) - past & current smokers	12.95 (12.32)	7.44 (11.14)	10.19 (12.04)	0.0001*
Regular smoking age - mean (SD) - past & current smokers	14.19 (13.70)	7.86 (12.18)	11.03 (13.32)	0.0001*
Smoking cessation age - mean (SD) - past-smokers	49.22 (10.71)	46.55 (14)	48.25 (11.99)	0.3426
Duration of smoking in years - mean (SD)- past & current smokers	18.06 (17.96)	9.65 (15.79)	13.85 (17.40)	< 0.001*
Frequency of smoking in current smokers - n (%)
Less than daily	6 (17.65)	3 (15.79)	9 (16.98)	0.863
Daily	28 (82.35)	16 (84.21)	44 (83.02)	0.863
Type of tobacco smoking consumed among current smokers - n (%)
Cigarette	22 (64.71)	13 (68.42)	35 (66.04)	0.737
Shisha	8 (23.53)	5 (26.32)	13 (24.53)
E-cigarette	4 (11.76)	1 (5.26)	5 (9.43)
* p < 0.05

Table 3 presents the results of univariate and multivariate graduated regression analyses of the association between smoking and DPN. Factors that were significantly associated in univariate conditional logistic regression with DPN included past (OR 3.31; 95% CI 1.75–6.27) and current smoking (OR 3.31; 95% CI 1.62–6.78), presence of chronic comorbidities (OR 1.88; 95% CI 1.02–3.44), early onset of diabetes (OR 2.8; 95% CI 1.36–5.76), longer duration of diabetes (OR 3.17; 95% CI 1.32–7.62), current insulin use (OR 2.63; 95% CI 1.48–4.67), presence of diabetes complications (OR 2.89; 95% CI 1.72–4.88), retinopathy (OR 2.45; 95% CI 1.46–4.12), and nephropathy (OR 2.6; 95% CI 1.43–4.72).

Table 3

Association of DPN with Smoking adjusted for plausible predictors in graduated model
	Univariate	Model 1	Model 2	Model 3	Model 4
	OR (95%CI)	OR (95%CI)	OR (95%CI)	OR (95%CI)	OR (95%CI)
Age				1.08 (0.85–1.38)	1.07 (0.83–1.39)
History of smoking ¹
Past smoker	3.31 (1.75–6.27)**	3.47 (1.74–6.93)**	3.64 (1.80–7.37)**	3.57 (1.71–7.44)**	4.12 (1.84–9.21)**
Current smoker	3.31 (1.62–6.78)**	3.25 (1.56–6.77)**	2.91 (1.37–6.18)**	2.83 (1.23–6.53)*	2.63 (1.09–6.35)*
BMI categories (kg/M2) ²
Overweight (25–30)	1 (0.51–1.98)	0.70 (0.32–1.55)	0.64 (0.29–1.44)	0.72 (0.31–1.66)	0.49 (0.19–1.22)
Obese (> 30)	1.66 (0.81–3.41)	1.20 (0.53–2.75)	1.18 (0.51–2.74)	1.25 (0.51–3.01)	0.93 (0.37–2.33)
Educational level ³
Graduate and above	0.68 (0.40–1.15)	0.72 (0.41–1.27)	0.71 (0.39–1.27)	0.59 (0.31–1.14)	0.59 (0.30–1.16)
Physical Activity ⁴
Insufficient PA	1.37 (0.76–2.47)		0.93 (0.47–1.81)	0.74 (0.36–1.53)	0.72 (0.33–1.60)
Have a comorbidity diagnosis ⁵	1.88 (1.02–3.44)*		1.95 (0.97–3.89)	1.85 (0.89–3.85)	1.55 (0.72–3.34)
Early onset DM ⁶	2.8 (1.36–5.76)**			2.16 (0.84–5.53)	1.63 (0.58–4.60)
DM Duration ⁷
Short duration	1.57 (0.62–3.97)			1.90 (0.63–5.71)	1.86 (0.56–6.19)
Intermediate duration	2.17 (0.93–5.09)			2.24 (0.81–6.21)	1.73 (0.57–5.24)
Long duration	3.17 (1.32–7.62)**			2.66 (0.87–8.13)	1.38 (0.40–4.71)
HbA1C (≥ 7% ) ⁸	0.89 (0.55–1.43)			0.81 (0.45–1.45)	0.65 (0.35–1.24)
Insulin as initiation therapy ⁹	1.89 (0.84–4.24)				1.65 (0.54-5)
Current insulin therapy ¹⁰	2.63 (1.48–4.67)**				1.62 (0.71–3.68)
Have any DM Complication ¹¹	2.89 (1.72–4.88)**				2.52 (1.29–4.94)**
Diabetic retinopathy diagnosis ¹²	2.45 (1.46–4.12)**
Diabetic Nephropathy diagnosis ¹³	2.6 (1.43–4.72)**
p < 0.05 * p < 0.01
†Model 1 adjusted for BMI categories, educational level
†Model 2 adjusted for BMI categories, educational level, physical activity, presence of any major comorbidity
†Model 3 adjusted for Age, BMI categories, educational level, physical activity, presence of any major comorbidity, early onset of DM, Duration of DM, HbA1C% categories
†Model 4 adjusted for Age, BMI categories, educational level, physical activity, presence of any major comorbidity, early onset of DM, Duration of DM, HbA1C% categories, starting insulin, current insulin, any diabetic complications
Dependent variable – DPN reference categories ¹ Never smoker, ² BMI (normal), ³ High school, ⁴ Sufficient PA, ⁵ No comorbidity, ⁶ No early onset of DM, ⁷ Newly diagnosed, ⁸ HbA1c < 7%, ⁹ No Insulin initiation at diabetes diagnosis, ¹⁰ No current use of insulin, ¹¹ No diagnosis of DM complications, ¹² No diagnosis of retinopathy, ¹³ No diagnosis of nephropathy

After adjusting for all plausible predictors (Age, BMI, Educational level, physical activity, presence of chronic comorbidities, early onset of DM, Duration of DM, HbA1C%, Insulin as initiation therapy, current insulin therapy, and presence of any diabetic complications), a higher risk of DPN was reported among past and current smokers ([4.12; 95% CI 1.84–9.21] and [OR 2.63; 95% CI 1.09–6.35], respectively). The presence of diabetic complications was also significantly associated with DPN (OR 2.52; 1.29–4.94).

This study demonstrated an association between tobacco smoking and DPN in male participants with type 2 diabetes. These findings are consistent with the existing literature that reported significant association between tobacco smoking and DPN in the Middle East [25–28], and other regions in the globe [14].

Tobacco smoking may lead to DPN by inducing oxidative stress, which is believed to be the primary cause of cellular damage in diabetic neuropathy [29]. Tobacco smoking may also lead to diabetic neuropathy by inducing insulin resistance [14, 30]. Insulin resistance has been shown to be independently associated with DPN and other neuropathy types in individuals with type 2 diabetes mellitus [31], regardless of glycaemic control. Tobacco smoking may also impair glycaemic control, which is believed to be the foundational approach for preventing or delaying DPN [32].

Results of this study showed that cases had a higher likelihood of having comorbidities and other diabetic complications than the controls. This is in line with previous studies that reported a link between coexisting diabetic complications, comorbidities, and an increased risk of DPN among individuals with diabetes [14, 28]. This may be related to the physiological impact of these conditions, leading to subsequent chronic illnesses. However, we conducted a conditional logistic regression and adjusted for these factors; the association between DPN and tobacco smoking remained significant throughout the different smoking status.

Previous studies have emphasised the need to develop and implement smoking cessation programs tailored to individuals with diabetes [33–34]. However, a personalised smoking cessation program, which is condition- and sex-specific, has yet to be established. The influence of smoking cessation on diabetic complications has recently been reported in the literature, and smoking cessation was found to be associated with a reduced risk of diabetes-related complications [35]. Despite current global evidence, a targeted cessation program for individuals with diabetes and related complications remains unestablished. This calls attention to the urgent need to implement and provide supportive cessation services tailored to individuals’ condition, needs and preferences, and the need for health professionals to engage in the process. Moreover, understanding facilitators of smoking and what is perceived as a barrier to quitting smoking in this population is essential as these may differ from those in the general population [36]. Addressing sex-specific differences should also be considered when designing personalised cessation programs, as each gender has its own motivation to smoke, cease smoking, or seek cessation services [37].

Personalised smoking cessation should be promoted at any level of patient encounter within the healthcare system, and it does not only need to be in the clinic. In a recent study that evaluated the quality of smoking cessation counselling in community pharmacies in the UAE, Al Zubaidi et al. proposed community pharmacies as a potential venue which offered patient-centred counselling to facilitate the dissemination of smoking cessation programs which could support current efforts of the UAE government to combat the increasing prevalence of smoking [38].

Despite the different smoking cessation interventions that exist worldwide, limited evidence has investigated their efficacy among male smokers [39–40], and none have targeted condition-based interventions such as diabetes or its related complications. Notwithstanding, these studies demonstrated a promising cessation attitude among male smokers who sought different smoking cessation interventions, including pharmacotherapies, behavioural support, and counselling sessions. Similarly, in the Middle East, evidence regarding the effectiveness of these strategies among male smokers are very limited [41–42]. However, the results from these studies were promising, and a high quit rate was observed among Arab males who sought simultaneous multiple smoking cessation strategies, including pharmacological and non-pharmacological interventions. Although these studies did not provide insight into their effectiveness in relation to diabetes-related conditions, they were personalised to provide effective cessation interventions by investigating barriers and facilitators of smoking from Arab males’ perspectives to ensure the delivery of effective interventions from a cultural perspective. This highlights the scarcity of research among Arab males with diabetes-related conditions, calls for exploratory research to identify barriers and facilitators of smoking among this particular population, and advocates for multilevel interventions based on the identified factors to design an effective cessation program for this community. The main barriers that hinder smoking cessation trials among Arab males in previous studies were mainly related to external influences, such as the urge to smoke during social gatherings, the high accessibility of smoking products in public, stress, and the lack of time to join smoking cessation programs [41–42]. The factors that facilitated their quit were family support, receiving proper educational intervention, and a former diagnosis of health-related conditions [41–42]. This may also explain why the past-smoker participants in our study reported that they were more likely to quit smoking after their formal diagnosis with major comorbidities and/or any diabetic complications [37, 41]. The impact of smoking-related stigma among Arab males should also be addressed among the barriers to quitting smoking, although men are believed to be the least affected compared to women [43–44]. However, its effect on their behaviour towards quitting or seeking smoking cessation services remains neglected.

To the best of our knowledge, this is the first case-control study conducted in the UAE to assess the association between tobacco smoking and peripheral neuropathy among male smokers with diabetes. The major strength of the study was the outcome assessment; DPN cases were diagnosed by a physician, and hospital medical records were used to retrieve medical diagnoses and clinical data. Another strength of this paper was the assessment of lifetime smoking exposure meant to minimise bias that may arise from length of exposure. Smokers with diabetes may quit smoking after the development of peripheral neuropathy or other types of diabetic complications which could lead to underestimation of the association. As we utilised a self-reported tool for collecting smoking data and to minimise bias that may arise from inaccuracy in smoking data reporting among females due to smoking-related stigma, we focused on enrolling male individuals to provide a reliable conclusion reflecting the true association between tobacco smoking and DPN. One limitation of this study was the use of self-reported exposure which could have been contaminated by forgetfulness and/or social desirability; however, the use of validated tools developed by the WHO was likely to minimize the impact.

This study revealed a significant association between tobacco smoking and DPN in the UAE. Future research should target a more in-depth assessment of the risk of peripheral neuropathy among smokers with diabetes, posed by the types and quantities of smoking. Smokers with diabetes may benefit greatly from personalised smoking cessation programs and campaigns. We advocate for the need to develop a personalised cessation program tailored to their needs and to integrate it into diabetes management care in healthcare systems.

5.1. study design, setting, and participants

This matched case-control study was conducted in two major hospitals in Sharjah, UAE. The data were collected between November 2023 and June 2024. The hospitals were purposively selected because they mostly served Arabic-speaking individuals with diabetes complications. Ethical approval was obtained from the Human Research Ethics Committee of Monash University (Project No. 36847) and Ministry of Health and Prevention Research Ethics Committee (Ref. No: MOHAP/DXB-REC/O.O.N /No.132/ 2023.). Verbal or signed informed consent was obtained from all participants prior to their partaking in the study. This study was conducted in accordance with the policies and guidelines of the Declaration of Helsinki.

Male participants aged ≥ 18 years with a minimum of one-year diagnosis of diabetes were included in the study. Those with neurological comorbidities (e.g., previous stroke, congenital or genetic conditions with potential neurological involvement), peripheral vascular disease, or diagnosed with type 1 diabetes were excludedCases were defined as male adults with a DPN diagnosis who were receiving treatment for diabetes at the designated hospital; controls were male patients with no DPN diagnosis who were also receiving treatment for diabetes at the same hospital. The inclusion and exclusion criteria were identical for cases and controls, except for DPN diagnosis, which was only a feature for cases. Subsequently, one control per case was selected by matching age (± 3 years) from the databases of the two hospitals.

Data collection took place in the outpatient clinic, where the hospitals provided research assistants with secured computer access to participants’ medical records. Potential participants were identified, screened for eligibility and approached through their contact details by trained research assistants. Participant data relevant to the research objectives were collected from the medical records, while self-reported data were collected through telephone interviews with the participants.

5.2. Study instrument and data collection

The study instrument consisted of two parts: the first part captured self-reported data including participant sociodemographic characteristics, tobacco smoking history using the Global Tobacco Surveillance System, and physical activity using the Global Physical Activity Questionnaire (GPAQ). The second part consisted of clinical and biochemical data obtained from the participants’ electronic medical records at the hospital.

Self-reported sociodemographic characteristics included participant age, sex, education level, family history of diabetes mellitus, their own diabetes type, and years since diagnosis. Participants’ smoking habits using items adapted from the Global Tobacco Surveillance System by the WHO were also collected [45]. Participants were asked to indicate their smoking status (current, past, or never smoker), frequency of smoking (daily or less), preferred method of tobacco consumption, duration of smoking, age at initiation of smoking, and any attempts at smoking cessation. The participants’ physical activity was assessed using a validated instrument by the WHO, the Global Physical Activity Questionnaire (GPAQ) [46]. The overall physical activity score was determined according to WHO scoring guidelines by adding metabolic equivalents (METs/min/week) of physical activity performed at work, during transportation, and during leisure time.. MET values were applied according to the intensity of the activity: four METs were assigned for moderate activity, eight METs for vigorous activity, and four METs for transportation activity. Accordingly, patients’ physical activity was classified as sufficient or insufficient in relation to their achieved scores.

The second section of the instrument consisted of clinical and biochemical data obtained from the medical records which confirmed a clinical diagnosis of diabetes and included participant information such as height, weight, diagnosis of DPN, hypertension, dyslipidaemia, use of vitamin B complex and/or vitamin B6, current and previous diabetes treatment including insulin, use of herbal supplements, and the presence of diabetes complications.

The study instrument was uploaded on secure web-based software, Qualtrics XM to collect the data. The questionnaire was initially written in simple English to ensure clarity for the research assistant. The research team translated the English questionnaire to Arabic, while a group of family medicine physicians and academics from public health and pharmacy reviewed and modified the English and Arabic versions of the questionnaire to accommodate all concerns regarding clarity and content. The research assistants were bilingual and administered the questionnaire to the participants. The questionnaire is provided in Supplementary Material 1.

5.3. Sample size

The minimum sample size required to achieve statistical power of 80% was calculated. Considering a 1:1 matched case-control design, accounting for the correlation between matched pairs of 0.1, in order to detect an effect estimate (OR; Odds Ratio) of 2, at a confidence level of 95% (2-sided), the estimated power was approximately 78.92%, indicating that a sample size of 140 was sufficient to detect the hypothesized effect.

5.4. Statistical analysis

Data were analysed using the Statistical Software for Data Science (Stata Corporation, College Station, TX, USA), Version 17.

Descriptive statistics were generated to compare patient characteristics between cases and controls. Differences were assessed using the chi-square test for categorical data and independent sample t-test for numerical data to identify potential confounders, including Age, BMI, Educational level, physical activity, presence of chronic comorbidities, early onset of DM, Duration of DM, HbA1C%, Insulin as initiation therapy, current insulin therapy, and presence of any diabetic complications. The association between smoking status and DPN was examined using multivariable conditional logistic regression, adjusted for plausible confounders. Adjusted Odds ratios and 95% confidence intervals were reported. All statistical tests were two-sided, and p < 0.05 was considered statistically significant.

Acknowledgements

The authors thank Dr. Manal Al Sha’rawy from the University of Sharjah for her invaluable contribution in facilitating the data collection process. The authors also thank Ms. Salwa Bader, Ms. Aya Baradie, Ms. Farrah Elsubeihi, Mr.Osamah Anaam for their dedicated efforts in collecting the data.

CRediT authorship contribution statement

Sana Bader: Conceptualization; Investigation; Methodology; Project administration; Visualization; Writing - original draft; Writing - review & editing. Sanah Hasan: Conceptualization; Investigation; Methodology; Project administration; Supervision; Validation; Writing - review & editing.Rakibul M. Islam: Visualization; Writing - review & editing. Ghisson Abdulrazak:Resources; Writing - review & editing.Khadija Al Zarouni: Resources; Writing - review & editing.Mariam Muayyad:Resources; Writing - review & editing. Nazmul Karim: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Supervision; Validation; Writing - review & editing.

Data availability

Available upon request from the corresponding authors.

Declaration of Interest

The authors declare no competing interests.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Assessing the risk of peripheral neuropathy among male tobacco smokers with type 2 diabetes: a matched case-control study in an Arabic-speaking country

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Abstract

1. INTRODUCTION

2. RESULTS

3. DISCUSSION

4. CONCLUSIONS

5. METHODS

5.1. study design, setting, and participants

5.2. Study instrument and data collection

5.3. Sample size

5.4. Statistical analysis

Declarations

References

Additional Declarations

Supplementary Files

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Version 1