Classification and identification of risk factors for type 2 diabetes

Abstract

The risk factors for type 2 diabetes mellitus (T2DM) have been increasingly researched, but the lack of systematic identification and categorization makes it difficult for clinicians to quickly and accurately access and understand all the risk factors, which are categorized in this paper into five categories: Social determinants, lifestyle, checkable/testable risk factors, history of illness and medication, and other factors, which are discussed in a narrative review. Meanwhile, this paper points out the problems of the current research, helps to improve the systematic categorisation and practicality of T2DM risk factors, and provides a professional research basis for clinical practice and industry decision-making.

Key Words: Type 2 diabetes mellitus; Risk factors; Classification; Prevention; Narrative synthesis

Core Tip: Type 2 diabetes mellitus (T2DM), as a worldwide public health problem, poses an enormous burden on public health and socioeconomics. This article summarizes the shortcomings of current research and its clinical guidance by providing a new, comprehensive, and systematic classification and identification of T2DM risk factors, in order to provide a reference for clinicians to develop T2DM prevention strategies.

INTRODUCTION

The number of diabetics aged 20-79 years worldwide totalled 529 million in 2021, and the global prevalence of diabetes is projected to reach 1.31 billion in 2050[1], with type 2 diabetes mellitus (T2DM) being the most predominant component of the diabetic population[2], accounting for 90% of the diabetes prevalence[3] and posing a huge economic burden to the world[4]. Currently, most preventive measures for T2DM focus on lifestyle interventions such as weight management, dietary adjustments, and addressing abdominal obesity. Early screening is recommended for individuals with a family history of T2DM to lower the risk of developing the condition. T2DM is a chronic disease that results from the interplay of various risk factors and their long-term effects on the body. More and more studies are proposing new risk factors for T2DM, but the lack of uniform identification and classification makes it difficult for clinicians to quickly and accurately access and understand all relevant risk factors so that they cannot carry out disease risk prevention and control in a timely manner, and provide personalized and targeted preventive and therapeutic advice to patients, while the complex and fragmented information also makes patients' self-management risk increase, which affects disease clinical outcomes.

This study was conducted by searching PubMed, EMBASE, and Cochrane databases, and the clinical studies retrieved spanned the years 2006-2024, mostly focusing on the last 5 years. Through a systematic and comprehensive reading of more than 200 high-quality articles, the risk factors for T2DM were categorized and identified into five categories: Social determinants, lifestyle, checkable/testable risk factors, medical and medication history, and other factors to develop a narrative review (Figure 1), in order to identify the high-risk groups of T2DM, to carry out risk management for such groups, to interrupt the development of T2DM, to provide clinicians with practical strategies for disease prevention, to further standardize medical behaviour, to scientifically allocate medical resources, to protect the rights and interests of patients, and to maximize the benefits for the risk groups of T2DM.

Figure 1 Classification and identification of type 2 diabetes mellitus risk factors. T2DM: Type 2 diabetes mellitus; PPI: Proton pump inhibitors; SA: Somatostatin analogues. The images in this article were drawn with Figdraw.

CLASSIFICATION AND IDENTIFICATION OF T2DM RISK FACTORS

Social determinants

Immutable elements of social determinants: Biological sex, age, blood group, and ethnicity are considered risk factors for T2DM. Research has shown that gender plays a significant role in the development of T2DM[5,6], with men being more susceptible to the condition compared to women in many regions worldwide[7-9]. Men are more predisposed to obesity, insulin resistance (IR), and hyperglycemia compared to women[10]. This disparity can be attributed to the presence of estrogen in women, which has been shown to decrease IR and the incidence of T2DM[11,12]. T2DM is most commonly diagnosed in adults aged 40 years and older[13]. Research indicates that individuals with blood type A or B have a higher risk of developing T2DM compared to those with blood type O[14], with a 76% higher prevalence of diabetes in individuals with blood type A[15]. Studies focusing on specific ethnicities have shown that native Americans, Blacks, and Hispanics/Latinos are at a higher risk for T2DM[16], with Blacks being more predisposed to diabetes compared to Whites[17].

The development of T2DM is closely linked to genetics. Sixty-nine percent of the risk of developing T2DM in individuals aged 35-60 years is attributable to genetic factors[18]. When the father, mother, or both have T2DM, the risk of a T2DM diagnosis in the offspring is approximately 2-4 times greater[19]. The hundreds of gene loci associated with T2DM can be divided into functionally specific groups of genes that work together to regulate the development of T2DM, and within this vast network of gene regulation is the central hub RFX6, whose reduced expression is associated with defective β-cell function[20]. The SLC30A8 gene polymorphism rs13266634 may be an important genetic factor in the risk of developing T2DM in Asian and European populations[21].

Modifiable factors in social determinants: Income level affects the incidence of T2DM. More than 80% of people with T2DM live in low- and middle-income countries[4]. There is an increased overall risk of T2DM in low socioeconomic status populations[22]. Children and adolescents in low-income families have a significantly higher risk of T2DM than those in high-income families[23]. In the United States, living in poor neighbourhoods increases the risk of diabetes among Blacks and poor Whites[17].

Adverse social experiences, as well as poor psychological states, increase the risk of developing T2DM. A better social environment is linked to better health[24]. Individuals who experience childhood adversity are at higher risk of developing T2DM in early adulthood, according to results from a 1.2 million-person Danish cohort study[25]. Individuals with adverse childhood experiences are at increased risk of developing diabetes in adulthood[26,27]. Experiences of violence and marginalized societies increase the risk of T2DM worldwide[28]. Clinicians should pay more attention to groups affected by crisis, displacement, and food insecurity by organizing diabetes screening programmes for such vulnerable groups[29]. Gene-environment interactions due to early famine and the consequent increased intergenerational risk of T2DM are the main causes of the current T2DM epidemic in China[30]. Famine exposure may increase the risk of developing T2DM, which may vary depending on the duration of the famine, the definition of diabetes, gender, and the duration of famine exposure[31]. Lower psychological well-being scores (poorer overall mental health) were found to be associated with an increased risk of developing T2DM in a dose-response relationship, independent of traditional risk factors and unrelated to genetic predisposition[32]. Two large prospective cohort studies from Europe and East Asia have shown that both social isolation and loneliness are associated with an increased risk of T2DM[33]. Individuals who feel lonely have a 2-fold increased risk of developing T2DM[34].

Occupation, work environment, and work stress can be risk factors for T2DM. The non-skilled occupation was found to be an independent risk factor for diabetes in a developed Asian setting[35]. A study screening all employees in Sweden found that among the 30 most common occupations, motor vehicle drivers had the highest prevalence of T2DM among men (8.8%), and manufacturing workers had the highest prevalence of T2DM among women (6.4%)[36]. Two large prospective cohort studies in the United States, conducted over a span of 22-24 years and involving a sample of over 140000 female nurses, revealed that nurses working night shifts faced an elevated risk of developing T2DM. The risk increased by 11%, 28%, and 46% for those working night shifts for 1-5, 5-9, and 10 years or more, respectively. Upon adjusting for various factors, the researchers observed that for every additional five years of night shift work, the likelihood of developing T2DM rose by 31%. Furthermore, when coupled with unhealthy lifestyle habits, the risk of developing the disease surged to 2.83 times higher[37]. Work stress is a significant risk factor for T2DM, particularly in women[38]. Research has shown that high work stress and heavy domestic responsibilities are linked to a higher risk of T2DM in women over the age of 60[39]. Conversely, a positive working environment, good relationships with coworkers, and lower levels of work stress have been found to decrease the risk of T2DM among employees[40,41].

The age and physical condition of the spouses in a marriage, as well as the quality of the marriage, can be risk factors for T2DM. The age of a man in a marriage is associated with his partner's risk of gestational diabetes mellitus (GDM), with men aged ≥ 45 years having a 28% increased risk of GDM in their partners compared with men aged 25-34 years; this risk increases to 34% when the man is over 55 years of age[42]. One spouse with T2DM has a 26% increased risk of the other spouse developing T2DM[43]. Among older British men, having an obese spouse increases their own risk of T2DM[44]. Additionally, poorer marital quality may be a unique risk factor for diabetes[45].

Lifestyle

Dietary habits: Dietary habits, dietary structure, and eating speed have a significant impact on the development of T2DM. An analysis of epidemiological data from 184 countries worldwide reveals that 70% of new cases of T2DM can be linked to 11 specific poor dietary habits. The most notable contributors include inadequate consumption of whole grains (26.1%), excessive intake of refined rice and pasta (24.6%), and high consumption of processed meat (20.3%) and unprocessed red meat (20.1%)[46]. Results from the China Health and Nutrition Examination Survey (1997-2018) showed that the risk of T2DM was negatively associated with meat intake below 75 g/day and positively associated with intake above 165 g/day[47]. Diets high in glycaemic index (GI) and glycaemic load have been linked to an increased incidence of T2DM[48]. Specifically, a higher consumption of white rice has been associated with a significantly elevated risk of T2DM, particularly in Asian populations[49,50]. Higher intake of dietary heme iron has been linked to a notable rise in the risk of developing T2DM[51,52]. Similarly, a higher consumption of sugar-sweetened beverages has been found to be positively correlated with an increased occurrence of T2DM[53,54]. Notably, the consumption of potatoes, particularly in the form of French fries, has also been associated with an elevated risk of T2DM. Specifically, a 10% increase in the risk of developing T2DM is linked to a 100 g/day increase in consumption of French fries[55]. Women consuming temperate fruits such as apples have a lower risk of developing T2DM, whereas men consuming high GI fruits such as bananas have a higher risk of developing T2DM, and the effect of fruit intake on the risk of diabetes varies according to fruit type[56]. Furthermore, studies indicate that a higher consumption of highly processed foods is associated with an increased risk of developing T2DM[57]. The study suggests that red, processed meat consumption is a risk factor for T2DM[53,58,59]. In a large prospective cohort of French adults, a direct association was found between the risk of T2DM and exposure to a variety of food additive emulsifiers widely used in industrial foods, with the greatest increase in risk being for tripotassium phosphate, with an increase of 500 mg/day associated with a 15% increase in risk[60]. In the dietary habits of new-onset diabetes, eating too fast was the only predisposing factor[61], and there is experimental data to support this idea[62]. This may be due to the positive correlation between rapid eating and body mass index (BMI) and weight gain[63]. A plant-based dietary pattern consisting of healthy plant foods and a small amount of animal foods may reduce the risk of T2DM by improving liver and kidney function and reducing underlying inflammation[64,65]. Some studies have found that intermittent fasting plus early time-restricted eating with calorie restriction improves postprandial glucose metabolism to a large extent in people at risk of T2DM[66].

Physical activity: Physical activity is a first-line strategy for the prevention and management of T2DM[67,68]. Accumulation of daily physical activity is a major determinant of insulin sensitivity[69]. Pedometer data were obtained from 7118 participants, with a 5.5% reduction in the risk of progression to diabetes for every 2000-step increase in the average daily step count (up to 10000), and an adjusted relative risk reduction of > 6%[70]. In multifactorial analyses, an increase of 2 hour/day in television viewing was associated with a 14% increase in the risk of diabetes, an increase of 2 hour/day in sitting at work was associated with a 7% increase in the risk of diabetes, and a 12% reduction in diabetes was associated with standing or walking at home (2 hours/day). A brisk walk every hour of the day is associated with a 34% reduction in diabetes[71]. Compared to those who did little or no exercise per day, moderate-to-vigorous exercise of 5.3-25.9 minutes per day was associated with a 37% reduction in the risk of T2DM; for 26-68.4 minutes, the risk was reduced by 59%, and for more than 68.4 minutes, the risk was reduced by 74%[72].

Sleep: A U-shaped dose-response relationship was observed between sleep duration and the risk of developing T2DM in a study involving 482502 participants followed for 2.5 to 16 years. The group with 7 to 8 hours of sleep per day had the lowest risk, while both shorter and longer sleep durations were linked to a significant increase in the risk of developing T2DM[73]. Habitual short sleep duration is associated with an increased risk of developing T2DM[74]. Individuals with poor sleep quality are at a significantly higher risk of developing T2DM compared to those with good sleep quality[75]. Moreover, people with insomnia have a 28% higher risk of developing T2DM compared to those without insomnia[76]. Research has indicated that the risk of developing insomnia rises as the follow-up period extends. This heightened risk is especially noticeable in individuals under the age of 40, with a 1.14-fold, 1.38-fold, and 1.51-fold increase in the risk of developing T2DM for insomnia durations of less than 4 years, 4-8 years, and over 8 years, respectively[77].

Smoking: Smoking is linked to an increased risk of diabetes[78-80]. The risk of developing T2DM is higher with greater amounts and longer durations of smoking. Quitting smoking at a young age may help lower the chances of developing T2DM[81]. Both active and passive smoking significantly increases the risk of T2DM, and new quitters have an increased risk of diabetes, but the risk of diabetes decreases substantially with longer periods of smoking cessation[82]. High consumption of snuff is a risk factor for T2DM, and this risk is similar to that of smokers, which means that smokers cannot reduce their risk of developing T2DM by changing their smoking patterns, and the findings similarly support the idea that nicotine increases the risk of T2DM[83].

Alcohol consumption: Alcohol consumption is associated with T2DM through effects on IR, changes in alcohol metabolite levels, and anti-inflammatory effects[84]. The relative risk of T2DM in men is most protective at 22 g/day of alcohol intake and becomes harmful at 60 g/day of alcohol or more, and in women, 24 g/day of alcohol intake is the most protective and becomes harmful at around 50 g/day of alcohol intake[85]. In women with a history of GDM, alcohol consumption of 5.0-14.9 g/day was negatively associated with the risk of T2DM[86].

Checkable/testable risk factors

Anthropometric and obesity indicators: Anthropometric and obesity indicators of T2DM risk factors include visceral adiposity index (VAI), BMI, hip circumference, body size index (ABSI, A Body Shape Index), age-related weight change, C-index, and relative fat mass. Studies have shown that higher VAI and body shape index scores are independently associated with the risk of diabetes[87] and can be utilized to predict diabetes progression[88]. Additionally, VAI has been found to be positively correlated with the risk of developing T2DM in Chinese populations[89]. Each 1-unit increase in VAI is correlated with a 42% higher risk of developing T2DM[90]. In obese adults, both excess visceral fat and IR are independently linked to the development of pre-DM and T2DM[91]. BMI is considered an independent risk factor for T2DM. Research has shown that the risk of developing T2DM significantly rises by 5.03% when BMI reaches 31 kg/m²[92]. Additionally, for every 5-unit increase in BMI, the risk of developing T2DM increases by 72%[90]. Hip circumference is negatively associated with an increased risk of developing T2DM[93]. ABSI and BMI are significantly associated with diabetes, with some studies suggesting that ABSI is a better predictor of diabetes risk than BMI[94]. Long-term weight gain from early adulthood to middle age increases the risk of developing T2DM, with this risk further escalating in women's later life as a result of weight gain[95]. It is noteworthy that moderate-to-heavy weight gain in early life presents a greater risk for the onset of diabetes compared to weight gain occurring after the age of 40[96]. The study revealed that certain calculated metrics, specifically the C-index and relative fat mass (RFM), were strongly linked to the development of new-onset T2DM. These metrics could potentially serve as valuable tools for assessing the risk of diabetes in extensive epidemiological research[97].

Indicators of atherosclerosis analysis: Brachial ankle pulse wave velocity (baPWV), an indicator of atherosclerosis analysis, is associated with the risk of developing T2DM. Low ABI was found to be mildly but independently associated with an increased risk of developing diabetes in the general population, and clinical attention should be paid to the glycaemic trajectory in people with low ABI but no diabetes[98]. A significant association was found between elevated baPWV and an increased risk of developing T2DM in middle-aged and older populations, and an elevated total white blood cell (WBC) count may partially mediate this association[99].

Resting heart rate: It has been suggested that high resting heart rate (RHR) is associated with an increased risk of metabolic syndrome (MetS), three MetS components [elevated blood pressure (BP), elevated triglycerides (TG), and elevated fasting plasma glucose (FPG)], and aggregated metabolic risk[100]. RHR was independently associated with an increased risk of T2DM, with a 19% increase in the risk of T2DM for every ten bpm increase in RHR[101]. A faster RHR is associated with a higher risk of impaired fasting glucose (IFG) and diabetes in the Chinese population, and this correlation is stronger in younger adults[102]. Moreover, there is a suggestion that the connection between elevated RHR and the development of T2DM is more noticeable in middle-aged individuals with significant vascular lesions[103].

BP: People with pre-hypertension are at increased risk of diabetes, a risk mediated by IR[104]. It has been found that people with elevated BP have an increased risk of diabetes, with increases in systolic BP (SBP) of 20 mmHg and diastolic BP of 10 mmHg increasing the risk of new-onset diabetes by 58% and 52%, respectively[105]. Patients with a mean SBP of 130-140 and/or a diastolic BP (DBP) of 80-90 mmHg at the time of treatment were found to have a 24% increased risk of diabetes compared to those with a mean SBP < 130 and a DBP of < 80 mmHg in the China Primary Stroke Prevention trial[106]. In a large sample of treated nondiabetic hypertensive patients, uncontrolled BP was associated with a 2-fold increased risk of developing diabetes mellitus (DM) and was not associated with age, BMI, baseline BP, or FPG[107]. Genetically elevated SBP is associated with an increased risk of developing type 2 diabetes[108]. There is an increased risk of T2DM in offspring of mothers with maternal hypertensive disorders of pregnancy (HDP) compared to offspring of mothers without[109]. BP reduction has a preventive effect on the risk of T2DM, with an 11% reduction in the risk of new-onset T2DM for every 5 mmHg reduction in SBP[110].

Blood glucose: The risk of diabetes was found to increase with increasing FPG even within the currently accepted normal range, with a 6% increase in the risk of diabetes for every 1 mg/dL increase in FPG, and subjects with blood glucose levels of 95-99 mg/dL were 2.33 times more likely to develop diabetes compared to those with FPG levels of less than 85 mg/dL, and subjects with blood glucose levels of 90-94 mg/dL were 49% more likely to progress to diabetes[111]. According to the Korean national health data, the risk of T2DM was found to increase progressively with increasing IFG exposure scores, with a 3.75-9.77-fold increase in the hazard ratio (HR) of developing diabetes in subjects with IFG exposure scores of 2, 3, or 4, and cumulative IFG exposure was associated with a higher risk of T2DM in a dose-response manner[112]. Random blood glucose ≥ 100 mg/dL (5.6 mmol/L) was the strongest predictor of undiagnosed diabetes[113]. Glycosylated haemoglobin (A1C) values between 5.5%-6.5% were associated with a significantly increased risk of developing diabetes[114].

Lipids: TG, high-density lipoprotein (HDL), and TG/HDL ratio in blood lipids are strongly associated with the risk of developing T2DM. Increases in TG levels within the normal range were found to lead to a sustained increase in the incidence of T2DM, even in healthy subjects[115]. For every one mmol/L increase in TG, the risk of T2DM increased by 81%[116]. The study from the Netherlands analysed the relationship between the levels of seven different HDL subtypes from H1P-H7P and the risk of T2DM and showed that H2P increased the risk of T2DM, with a corresponding 15% increase in risk for every one standard deviation increase in its level[117]. Low mean and high variability of HDL-C are independent predictors of diabetes with additive effects and elevating and stabilising HDL-C may be an important target for reducing diabetes risk[43]. Non-traditional lipid parameters, especially TG/HDL-C ratio, were associated with the risk of pre-DM and T2DM, and high TG/HDL-C, defined by sex-specific TG/HDL-C cut-off points, was a risk factor for pre-DM and T2DM[118], and an elevated TG/HDL-C ratio was significantly associated with an increased risk of new-onset DM (NOD)[119].

Uric acid: In a follow-up of 4536 subjects without diabetes at baseline for an average of 10.1 years, subjects with higher blood uric acid levels were found to be more susceptible to T2DM, with 1/4 cases of diabetes attributable to high blood uric acid levels, suggesting that blood uric acid serves as a strong and independent risk factor for diabetes[120]. It has been suggested that the association between hyperuricaemia and diabetes is mediated in part by the MetS, with participants in the highest uric acid quintile with the MetS having 3.3 times the risk of developing diabetes than those in the lowest uric acid quintile without the MetS[121].

Vitamin D levels: Blood vitamin D levels were negatively associated with diabetes risk, with a greater reduction in diabetes risk in participants with blood 25-hydroxyvitamin D levels above 125 nmol/L[122]. Vitamin D deficiency at baseline may be associated with a 50% increased risk of developing DM and a 62% increased risk of developing pre-DM after 4 years of follow-up[123]. The multivariable-adjusted HR per 10 nmol/L increase was 0.88[124]. At the same time, however, some studies have argued against it, suggesting that increasing 25 hydroxyvitamin D [25-(OH)D] concentrations may not reduce the risk of T2DM as expected from observational evidence[125,126].

Thyroid function: Adolescents with thyroid disease have an approximately 2-fold increased risk of developing T2DM compared to those without, and this association is confirmed in normal goitre and hypothyroidism and is apparent by the age of 30 years[127]. Abnormal thyroid hormone levels were found to be associated with an increased risk of T2DM by meta-analysis, with a J-shaped relationship with thyroid-stimulating hormone and an inverted J-shaped relationship with free triiodothyronine and free thyroxine[128]. Subclinical hypothyroidism increases IR only in the normoglycemic population, with an increased risk of developing diabetes as central thyroid sensitivity decreases[129].

Serum magnesium levels: Serum magnesium concentration is negatively correlated with DM, BMI, blood glucose, insulin, HbA1c, and homeostasis model assessment-IR (HOMA-IR)[130]. Lower serum magnesium concentrations are associated with a higher risk of IR and diabetes; when serum magnesium levels are below 0.82 mmol/L, the risk ratio for IR increases incrementally with increasing serum magnesium levels; when serum magnesium levels reach about 0.82 mmol/L, the risk of IR decreases with increasing serum magnesium levels, however, when serum magnesium is greater than about 0.93 mmol/L the risk of IR increased rapidly[131]. Mechanisms of serum magnesium effects on pre-DM and diabetes risk may be mediated on the one hand through IR and on the other hand through genetic variants in the magnesium-regulated genes TRPM6, CLDN19, SLC41A2, CNNM2, and FXYD2[132].

Inflammatory markers: A meta-analysis included ten prospective studies that detected a significant dose-response relationship between interleukin 6 (IL-6) levels, and the risk of T2DM, and elevated C-reactive protein levels were significantly associated with an increased risk of T2DM without publication bias[133]. Platelet count was independently associated with an increased risk of developing T2DM only in women[134]. Shorter WBC telomere relative length (rLTL) (a biomarker of biological ageing) was found to be associated with a higher risk of glycaemic progression, with an average 1.69-fold increase in the risk of diabetes progression for each 1-base decrease in absolute LTL value[135].

Liver function: In cross-sectional analyses, alanine aminotransferase (ALT) and gamma-glutamyltransferase (GGT) were strongly associated with obesity, IR, and MetS, and GGT and ALT were significant predictors of T2DM[136]. Meta-analyses have also shown that ALT levels have a dose-response effect on the risk of T2DM, with the risk of T2DM increasing by approximately 20% for every 5 IU/L increase in ALT levels[137]. The association of alanine, phenylalanine, and tyrosine with future T2DM risk was confirmed in two prospective cohorts of Chinese adults, and palmitoylcarnitine was further identified as a new metabolic marker for new-onset T2DM[138].

Sex hormones: Sex steroid hormones play a role in the metabolism, accumulation, and distribution of adipose tissue[139]. Low serum levels of sex hormone-binding globulin (SHBG) are linked to IR, compensated hyperinsulinemia, and abnormal glucose-lipid metabolism in individuals with polycystic ovary syndrome (PCOS)[140]. SHBG levels are a strong predictor of the risk of developing T2DM[141]. High testosterone levels are associated with a higher risk of developing T2DM in women but a lower risk in men; the inverse association of SHBG with risk is stronger in women than in men[12]. Higher endogenous plasma estradiol (TE) and testosterone levels are strongly associated with an increased risk of T2DM[142]. SHBG and TE are independent risk factors for the development of T2DM in women[143]. Maternal hyperandrogenaemia increases the risk of T2DM and overweight in female offspring later in life[144].

Triglyceride glucose-BMI: Elevated triglyceride glucose (TyG) index is associated with impaired β-cell function[145]. A longitudinal cohort study in Japan demonstrated that baseline TyG-BMI was positively associated with the risk of developing T2DM in a normoglycaemic population, that this risk was significantly higher in young people (18-44 years), women, non-hypertensive people and non-drinkers, and that TyG-BMI could be used as an independent predictor of T2DM development[146].

Branched-chain amino acids: Research findings have shown a negative correlation between peripheral insulin sensitivity and plasma branched-chain amino acid (BCAA) levels[147-149]. In three large prospective cohorts of men and women in the United States, consistent associations were observed between long-term intake of BCAAs, including leucine, isoleucine, and valine, either individually or in total, and increased risk of developing T2DM, and these associations were independent of traditional diabetes risk factors, including BMI[150].

Proteinuria: The presence of proteinuria was significantly associated with an increased risk of T2DM, with studies finding that test-paper proteinuria was an independent risk factor for the development of new-onset T2DM, and that the risk of T2DM increased proportionally with the severity of proteinuria[151].

Microbe-associated metabolites: Several bacteria (e.g., B. burgdorferi and E. faecalis) and enzymes (e.g., xylanase EC 3.2.1.156) involved in fibre degradation have been found to be positively correlated with fibre intake, inversely correlated with the incidence of T2DM, and positively correlated with metabolic profiles associated with T2DM[152]. Alterations in the gut microbiota have been associated with the growing prevalence of metabolic disorders, including obesity, IR, and T2DM[153]. The gut flora contributes to the disease development process as a trigger for metabolic inflammation in obesity and T2DM[154]. Additionally, T2DM has been linked to a deficiency in short-chain fatty acids[155]. Elevated levels of trimethylamine N-oxide, a choline metabolite produced by intestinal bacteria, have been associated with an increased risk of DM, with an odds ratio (OR) of 1.89[156]. Furthermore, an increased relative abundance of the family Oxalobacteraceae (OR = 1.0704), the genus Oxalobacter (OR = 1.0874), and the species faecis (OR = 0.9460) have been respectively associated with an increased or decreased risk of developing T2DM. Other microbial taxa such as β-Ascomycetes, Lactobacteriaceae, Sclerobacterium, and Proboscidea have also shown significant associations with T2DM[157]. The abundance of Blautia wexlerae was found to be negatively associated with obesity and T2DM in a cross-sectional study of Japanese adults[158].

Genomics: A chromatin map of pancreatic islet cells was created through single-cell nucleus ATAC-seq analysis. Among 15298 cells, a positive correlation was identified between fasting blood glucose levels and the enrichment of T2DM and β-cell transcription factor motifs, including PDX. In relation to fasting blood glucose, the strongest enrichment was observed for state-specific transcription factor motifs related to high insulin levels, with RFX and NEUROD being the most notable[159]. Mutations in the GIGYF1 gene, found in approximately 1 in 3000 individuals, significantly raise the likelihood of Y chromosome deletions and increase the risk of developing T2DM to 30%, as opposed to the 5% risk in the general population. This suggests that mutations in the GIGYF1 gene can elevate the risk of T2DM by up to 6-fold[160]. Additionally, simultaneous exposure to NPC1 L1 and other genetically reduced LDL-C loci is linked to a heightened risk of T2DM[161].

Medical and medication history

Medical history: (1) Respiratory system diseases. Snoring and obstructive sleep apnea-hypopnea syndrome (OSAHS): In the Chinese adult population, habitual snoring has been found to be independently linked to a higher risk of developing T2DM[162]. Habitual snoring is associated with an increased incidence of DM over a 10-year period in 30-69-year-old men in Uppsala[163]. Research indicates that snoring is connected to impaired glucose metabolism. Even in metabolically normal adults, higher snoring intensity and frequency are positively correlated with fasting glucose and HbA1c levels[164]. The main features of OSAHS include transient hypoxemia and sleep fragmentation, which are believed to be the primary factors leading to metabolic dysfunction[165]. The Apnea-Hypopnea Index shows a moderate positive correlation with glycaemic variability[166]. OSAHS can exacerbate obesity[167] and is associated with abnormal glucose metabolism and increased risk of diabetes[168].

Coronavirus disease 2019 (COVID-19): A large cohort study involving over 47.1 million participants demonstrated a notable link between COVID-19 and the development of new-onset diabetes[169]. Research indicates that individuals infected with severe acute respiratory syndrome coronavirus 2 have a higher likelihood of developing T2DM, with a roughly 60% increased risk of new-onset diabetes compared to non-infected individuals[170];

(2) Circulatory system diseases. Heart failure (HF) is a state of IR in which chronically increased sympathetic nervous system activity can lead to reduced insulin responsiveness, glucose utilisation, and cellular insulin secretion by affecting vasodilatory tone, free fatty acid levels, and oxidative stress. By reducing insulin sensitivity, HF may select patients with a genetic predisposition to develop cellular hyposecretion and diabetes[171]. Additionally, a Danish nationwide cohort study revealed that the severity of HF was linked to an increasing risk of diabetes[172];

(3) Digestive system diseases: Metabolic dysfunction-associated fatty liver disease (MAFLD). MAFLD, a metabolic disorder characterized by excessive accumulation of liver fat[173], increases the risk of developing T2DM[174,175]. Three hundred and ninety-five studies from 40 countries or territories covering 8051205 patients with MAFLD found that the prevalence of comorbid T2DM was 28.3% (95% confidence interval: 25.2%-31.6%) in patients with MAFLD, and 26.2% (23.9%-28.6%) globally[176]. Young people with MAFLD have a 6.1 times higher risk of developing diabetes compared to their peers without MAFLD[177]. About 35% of subjects with metabolically healthy abdominal obesity (MHAO) with MAFLD showed an excess risk of pre-DM plus diabetes[178].

Inflammatory bowel disease (IBD): Ulcerative colitis (UC) and Crohn's disease (CD), collectively referred to as IBD, are idiopathic, chronic, and recurrent inflammatory diseases of the intestine[179]. A nationwide Danish cohort study involving 6028844 individuals compared data from those diagnosed with IBD (CD or UC) to the general population, revealing a significantly increased risk of developing T2DM[180]. The link between T2DM and IBD/UC may be attributed to changes in various metabolic pathways and immune responses mediated by cytotoxic T-lymphocyte antigen 4[181].

Pancreas-related diseases: Fatty pancreas and pancreatitis have been identified as risk factors for developing T2DM. A 10-year prospective cohort study revealed that individuals with a pancreatic fat content exceeding 10.4% had a 1.81-fold higher risk of developing diabetes[182]. Additionally, it was observed that individuals who experienced acute pancreatitis had a 2-fold increased risk of developing diabetes, potentially attributed to pancreatic necrosis and reduction in β-cell area[183].

Gallbladder-related diseases: Both gallstone disease and cholecystectomy increase the risk of developing T2DM. Cholelithiasis was independently associated with an increased risk of developing T2DM[184]. Chinese community-dwelling individuals who have undergone cholecystectomy have an increased risk of developing abnormal blood glucose[185]. Cholecystectomy is an independent risk factor for the development of T2DM, and cholecystectomy increases the risk of developing T2DM by 20%[186];

(4) Endocrine and nutritional metabolic diseases: Obesity and MetS. Obesity is associated with an increased risk of developing IR and T2DM, and in obese individuals, adipose tissue releases large amounts of factors involved in the development of IR, and abnormal pancreatic β-cell function is a key determinant of the development of T2DM in obese patients[187]. Increased abdominal and intra-abdominal fat distribution and increased intrahepatic and intramuscular triglyceride content are associated with T2DM, causing both IR and β-cell dysfunction[188]. Individuals who switch from a metabolically healthy state to an unhealthy phenotype may be at increased risk for diabetes[189]. Central obesity was independently associated with an increased risk of developing diabetes, and after adjusting for confounding covariates, centrally obese individuals had a 72% higher risk of diabetes than non-centrally obese individuals in the propensity score matching cohort[190]. MetS is a cluster of metabolism-related symptoms, and IR and hyperinsulinemia are consistent features of MetS that significantly increase the risk of developing T2DM[191].

Rheumatoid arthritis (RA): There is increasing evidence suggesting that patients with RA are at a higher risk of developing DM and that RA can worsen the metabolic imbalances associated with DM. This is due to various factors present in RA, such as pro-inflammatory cytokines (e.g., tumor necrosis factor-α, IL-6, and IL-1β), RA-specific autoantibodies (e.g., antibodies to rheumatoid factor and cyclic citrullinated peptide), and elevated levels of adipokines associated with RA (e.g., leptin), all of which contribute to IR and the onset of DM[192].

PCOS: The primary causes of PCOS are IR and elevated levels of androgens[193]. Normal PCOS (NA PCOS) is an independent risk factor for T2DM; the incidence of type 2 diabetes in patients with NA PCOS is twice as high as in non-PCOS patients, and women with the hyperandrogenic PCOS phenotype face a higher risk of T2DM than women with the NA PCOS phenotype[194];

(5) Neuropsychiatric system diseases: Depression. Depression shares similar environmental and lifestyle factors with T2DM, such as socioeconomic deprivation, social adversity, smoking, and reduced physical activity[195]. The presence of depressive symptoms is associated with a modestly increased risk of T2DM[196]. Depressed adults have a 37% increased risk of developing T2DM[197]. Furthermore, studies suggest that in younger adults aged 20-50, depression may increase the risk of diabetes by 23%[198];

(6) Sexually transmitted diseases: Human immunodeficiency virus (HIV) infection. Studies have shown that among HIV-infected individuals, the risk of T2DM is higher. The older the age, the higher the BMI, the higher the TG, the lower the total cholesterol, the longer the duration of HIV infection, and the lower the lowest value of CD4. The prevalence of T2DM in HIV-infected individuals is almost twice as high as that in the healthy population, which is related to the typical risk factors in the general population, as well as to the duration of HIV infection and low minimum CD4 Levels[199];

(7) Pregnancy related diseases: Abnormalities of blood glucose during pregnancy. In a representative national cohort study of GDM screening, abnormal glucose tolerance that did not meet the GDM threshold was associated with an increased risk of T2DM that could be as high as ninefold[200]. Women with GDM are at an increased risk of being diagnosed with T2DM[201-203]. Women with a history of GDM have a nearly 10-fold higher risk of developing T2DM than women with normal blood sugar[201]. It was found that diabetes before pregnancy leaves a metabolic imprint in oocytes - a significant decrease in levels of the DNA demethylase Tet3 - and offspring are more likely to be insulin-deficient[204];

(8) Dermatosis (psoriasis): One study found more IR in patients with psoriasis compared to healthy controls, which supports the idea that psoriasis may be a pre-DM state[205]. In patients with psoriasis, psoriasis is associated with a 59% increased prevalence of diabetes and a 27% increased risk of diabetes, and the risk of diabetes may be higher in patients with severe psoriasis, especially in younger patients[206]. Furthermore, research indicated that pre-pregnancy diabetes can leave a metabolic imprint on oocytes, leading to reduced levels of the DNA demethylase Tet3, potentially resulting in offspring with insulin deficiency[207];

And (9) Other diseases: Cancer. A longitudinal study conducted over an 11-year period in Denmark revealed that the presence of lung, pancreatic, breast, brain, urethral, or uterine cancer is associated with an elevated risk of T2DM, with pancreatic cancer showing the highest risk[208]. This increased risk of developing diabetes is noticeable shortly after the diagnosis of cancer and is most pronounced within the initial two years following the cancer diagnosis[209]. Similarly, findings from a nationwide cohort study in Korea indicated that patients with thyroid cancer who underwent thyroidectomy faced an increased risk of T2DM, irrespective of age. Moreover, there was an observed U-shaped relationship between the dosage of postoperative levothyroxine and the risk of developing T2DM[210].

Solid organ transplantation (SOT): T2DM occurs in 10% to 15% of renal transplant recipients, and the pathogenesis of this DM is characterized by β-cell dysfunction and associated with reduced insulin sensitivity in the liver, muscle, and adipose tissues[211]. Recipients of SOTs are exposed to high doses of methylprednisolone intravenously at the time of surgery, followed by oral tapering glucocorticosteroid therapy, and, in the clinical setting, glucocorticoids induce increased transcription of gluconeogenic enzymes leading to hepatic IR, which results in abnormally elevated blood glucose[212].

History of drug treatment: (1) Antipsychotic drugs: A cohort study conducted on a Danish population revealed a significantly increased risk of developing diabetes after starting antipsychotic medication compared to schizophrenic patients who were not taking antipsychotic medication[213]. Another cohort study focusing on children aged 6 to 17 years found that the risk of T2DM was over three times higher in individuals using antipsychotic medication. Furthermore, there was a notable rise in risk with higher cumulative doses, and the risk persisted for up to 1 year post-discontinuation of the antipsychotic medication[214]. A study indicated a significant increase in the risk of developing T2DM in children and adolescents with psychiatric disorders who were exposed to atypical antipsychotics compared to those who were not[215]. Additionally, treatment with antiseizure medications (ASMs) was linked to a higher risk of developing diabetes compared to ASMs without enzymatic interactions. These effects are related to the cytochrome P450 family (CYPs), body weight, and IR. The risk of T2DM development tends to increase with the duration of ASM treatment, typically manifesting 6 to 9 years after treatment initiation[216]. Modern pharmacology suggests metabolic side effects of antidepressants, such as weight gain[217] or Mets[218]. The use of antidepressants was found to be associated with an increased relative risk of T2DM[219] in a time- and dose-dependent manner. Children and adolescents treated with the antidepressant selective serotonin reuptake inhibitor may have a small increased risk of T2DM[220]. However, the role of antidepressants in increasing the risk of T2DM remains controversial[221,222];

(2) Drugs to reduce cardiovascular risk: Four classes of drugs commonly used to reduce cardiovascular risk, namely, statins, niacin, thiazide diuretics, and beta-blockers, have been shown in meta-analyses or large-scale clinical trials to increase the risk of NOD by 9%-43%[223]. Statins can impair insulin sensitivity and secretory function of pancreatic β-cells and increase IR in peripheral tissues[224]. In examining the effects of the major antihypertensive drug classes, it was found that beta-blockers and thiazide diuretics increased the risk of disease compared with placebo[110];

(3) Proton pump inhibitors (PPIs): There is a significant association between PPIs and the risk of developing T2DM[225,226]. During the follow-up of 2127471 people, 10105 incident cases of diabetes were recorded, and the risk of developing diabetes was 24% higher in those who regularly used PPIs than in those who did not, with the risk of developing diabetes increasing with the duration of PPI use[227];

(4) Prostate treatment drugs: Androgen deprivation therapy (ADT) reduces testosterone levels and creates a state of IR that worsens glycaemic control, and ADT is associated with worsened diabetes control and increased HbA1c levels[228]. Additionally, men with an enlarged prostate are commonly prescribed 5-alpha-reductase inhibitors to decrease androgen production, with research indicating a heightened risk of developing T2DM in men using these medications[229];

(5) Somatostatin analogues (SAs): A phase III trial found that the SA drug paregoric acid increased new-onset diabetes threefold and hyperglycaemic adverse events in up to 30% of patients and that SAs inhibit insulin and glucagon secretion, which can lead to diabetes[230];

And (6) Glucocorticosteroids: Studies have shown that glucocorticoid oral drug use is associated with 2% of new-onset diabetes[231]. Glucocorticoid-induced hyperglycaemia is caused by stimulation of hepatic glucose production and increased lipolysis in adipose tissue, resulting in systemic IR and impaired insulin production and secretion by pancreatic beta cells[232].

Other factors

Pregnancy and foetal factors: (1) Premature and multiple births. Multiple and premature births are associated with the development of diabetes[233]. Preterm birth is thought to play an important role in the development of diabetes. Studies have shown that preterm birth is an important and independent risk factor for both T1DM and T2DM[234]. Preterm birth before 35 weeks of gestation is associated with an increased risk of developing T2DM in adulthood, which is independent of the risk associated with slow fetal growth[235]. Multiple births increase the risk of diabetes by impairing the proliferative capacity of pancreatic beta cells[236];

And (2) Low birth weight: Low birth weight is a known risk factor for T2DM[237,238]. Being thin in childhood increases the risk of developing T2DM in individuals who are obese in adulthood[239]. As birth weight increases (< 5000 g), the risk of developing T2DM decreases significantly, and the association between birth weight and T2DM is curvilinear and L-shaped[240]. It has been suggested that birth weight significantly alters circulating insulin-like growth factor-1 levels in adulthood, thereby affecting the risk of developing T2DM[241]. Fasting and 2-hour insulin concentrations and HOMA-IR were negatively correlated with birth weight[242]. Low birth weight and overweight in early adulthood are major determinants of the risk of developing T2DM in adult men, and they increase the risk of developing T2DM in an additive manner[243].

Menses: Women with prolonged or highly irregular menstrual cycles are at a significantly increased risk of developing T2DM[244]. After adjusting for potential confounders, those women who reported irregular menstrual cycles between the ages of 14-17, 18-22, and 29-46 had a 32%, 41%, and 66% increased risk of T2DM, respectively, compared with women of the same age with very regular menstrual cycles[245]. Early age at natural menopause is an independent risk factor for the development of T2DM, and subjects with significantly earlier menopause (before 40 years of age) have a nearly fourfold increased risk of developing T2DM compared with those with late-onset menopause, which may be due to disruption of the hypothalamic-pituitary-ovarian axis, resulting in an increased pituitary release of gonadotropins and follicle-stimulating hormone[246]. Scarcity of menstruation predicts MetS and IFG + T2DM, and the pattern of delayed menstruation during puberty should be considered an important risk factor for IFG + T2DM, MetS, menstrual scarcity, and future development of PCOS in young adults[247].

Environmental factors: Traffic noise, environmental pollution, and exposure to outdoor light all increase the risk of developing T2DM. The study found that for every 10 dB increase in traffic noise exposure, there was an 8% increase in the risk of developing T2DM among people aged 35-100 years living in Toronto[248]. A large number of recent studies have confirmed that long-term exposure to particulate matter with an aerodynamic diameter ≤ 2.5 mm (PM2.5) is a newly identified risk factor for diabetes[249-251]. Organic matter may be the most important factor in the PM2.5-diabetes relationship[252]. Arsenic is considered to be a toxic metalloid, mainly from drinking water and food (e.g., rice and cereals)[253]. Arsenic exposure was found to be positively associated with T2DM risk, with arsenic exposure levels measured by arsenic in drinking water or urine[254,255]. Genetic susceptibility to arsenic metabolism correlates with arsenic metabolism efficiency and may modify the correlation between inorganic arsenic and risk of T2DM[256]. As levels of organochlorine pesticides (OCPs) in groundwater increase, blood OCP levels tend to increase the risk of T2DM[257]. A prospective study originating in the United Kingdom with a 14-year follow-up of 283374 individuals found that middle-aged and older adults living in areas with high outdoor nighttime exposure to outdoor light levels may be at higher risk for T2DM and low sleep quality[258].

DISCUSSION

Current research has revealed a large number of new multifaceted risk factors for T2DM; however, these findings face an important challenge in their practical application: The lack of systematic classification and organization. This leads to difficulties for clinicians in fully understanding and utilizing these findings to guide patient management. Physicians in the clinic need to consider a variety of factors to assess a patient's risk of disease and develop individualized prevention and treatment plans. Due to the lack of effective classification and organization tools, it is often difficult for physicians to quickly and accurately extract useful information from numerous research findings. In order to better translate research results into effective tools in clinical practice, this paper provides a systematic categorization and identification of T2DM risk factors and a systematic and comprehensive overview of known risk factors according to different dimensions (social determinants, lifestyle, examinable/examined risk factors, medical and medication history, and other factors), which can help clinicians to improve the rate of early diagnosis of the disease, and help public health policymakers to better understand the risk characteristics of different groups and take more targeted preventive measures. At the same time, it can help establish risk prediction models. Currently, diabetes prediction models are usually based on a series of risk factors, such as age, weight, BP, and family history, and the relationship between these factors and the occurrence of diabetes is determined through statistical analysis. This paper adds more possibilities to help construct a more accurate and comprehensive risk prediction model through a more comprehensive comb, and this approach, from the careful consideration of multiple factors, also better reflects the complexity of the aetiology of diabetes, which is not caused by a single factor, but is the result of the combined effect of multiple factors.

At the same time, the review of risk factor-related research found that there are some shortcomings in the current research; the following will be discussed: The clinical significance of the current risk factor research and the shortcomings of the current risk factor research, in order to improve the systematic categorisation and practicality of T2DM risk factors by improving the shortcomings of current studies, thus providing a more scientific basis for clinicians and improving the effect of disease prevention and control.

CLINICAL IMPLICATIONS OF CURRENT RISK FACTOR RESEARCH

Individually modifiable risk factors

During the review, we found that some of the risk factors are the easiest and least costly to change; such individual modifiable risk factors include dietary preferences, behavioural activities, mental health, and disease medication choices. Individuals can reduce the risk of developing T2DM by eating right, exercising scientifically, losing weight, adjusting emotions, regular screening, and making sensible drug choices. Studies have found that at least 75% of T2DM can be prevented through a healthy lifestyle[259]. HbA1c can be reduced by 2% in newly diagnosed T2DM patients guided by medical nutrition therapy[260]. In addition, the following is helpful to prevent T2DM: Choosing a low-fat, low-sugar, high-fibre diet; avoiding excessive alcohol consumption; strengthening physical exercise and active sports, with at least 150 minutes of moderate-intensity aerobic exercise per week; paying attention to emotional regulation; maintaining a positive state of mind, and overcoming negative thinking and irritability and other negative emotions; undergoing regular screening to help determine the progression of the disease; and timely adjusting the therapeutic use of medication to reduce the risk of the onset of T2DM or delay its occurrence. The risk of T2DM can be reduced by timely adjusting the treatment and medication plan, truncating or delaying the occurrence of T2DM, and providing health benefits in the later stage through the improvement of personal lifestyle and emotions.

T2DM risk-ahead prevention strategies (level 0 prevention)

T2DM, as a chronic disease, is a gradual process due to the accumulation, superimposition, and synergistic effect of health risk factors on the body over time. This gradual process will not stop as long as these risk factors persist. Clinicians often advocate a good three-level prevention of T2DM[261], in which "primary prevention" is mainly to identify T2DM risk factors and adopt a healthy lifestyle to prevent the occurrence of T2DM; "secondary prevention" is to delay the disease progression through drug treatment and prevent the occurrence of T2DM complications; and "tertiary prevention" is to delay the progression of T2DM complications, reduce disability and mortality, and improve the quality of life for patients through standardised treatment. By reviewing the risk factors of T2DM, we propose the possibility to move the intervention forward, front-loading health regulation, and avoiding the occurrence of T2DM earlier, i.e., to achieve the "level 0 prevention" of T2DM so that the prevention of T2DM starts from the risk control before the occurrence of T2DM, and further move the gate of T2DM prevention forward, which is a more proactive health promotion approach. This is a more proactive health promotion management model, which is also a missing part of the current research. A review of risk factors for T2DM found that immutable social determinants include gender, age, blood group, ethnicity, and family history (genetic factors). With regard to age of onset, we can reduce the incidence of T2DM through early attention and intervention at the age when T2DM is most prevalent. Also the review found that family history (genetic factors) plays an important role in the development of T2DM. Our review of the literature found that multiple behaviours of the mother (including those during pregnancy) affect the health of the offspring[262-264], which has also been verified in several animal studies[265-268]. One article clearly suggested that mothers can inherit glucose intolerance to their offspring through oocyte TET3 deficiency[204]. Also foetal undernutrition has been suggested to be a predisposing factor for IR in individuals[269]. We propose that risk screening and intervention for the mother's offspring, including genetic testing of the mother's offspring to predict the individual's risk of developing the disease, motivating high-risk individuals to change their lifestyles before a clinical phenotype emerges, helping the offspring to stay healthy by changing the mother's health status, and focusing on the mother's health management during pregnancy to reduce the risk of the offspring's adult T2DM disease, could transform this immutable risk factor into a modifiable risk factor that can be effectively intervened upon. Fully recognising the possibility and importance of starting intervention at an early stage of life fundamentally changes the thinking of disease prevention and control, providing new insights into the prevention and treatment of T2DM. The above methods block T2DM from the source of development, prevent and control chronic diseases from the source of life, promote the health and development of human beings throughout the life cycle, and provide new perspectives and strategies to ensure the health and safety of the world.

Risk factors at different stages of T2DM development

There is a lack of research to categorize T2DM risk factors according to the different disease periods of T2DM. The prevention of T2DM can be divided into three main parts: Prevention of occurrence, prevention of progression (without complications), and reduction of disability and mortality (with complications). The risk factors for the development of T2DM reviewed in the paper also have an impact on the T2DM disease process. We further categorised the risk factors for T2DM to include risk factors associated with three phases: Pre-T2DM [The American Diabetes Association defines a patient with prediabetes as the presence of IFG and/or IGT and/or an A1C of 5.7-6.4 percent (270)] as well as during the period of healthy individuals, T2DM without complications, and T2DM with complications. Regarding risk factors that are closely associated with pre-T2DM as well as in healthy individuals, they can include body mass-related index, family history (risk genes), mental health status, lifestyle (diet, physical activity, smoking, alcohol consumption, and sleep), and blood glucose-related index. This is due to the fact that T2DM is prevalent in obese patients, and weight control is the first and foremost method to prevent T2DM. Intervention and control of the mother's generation of risk genes is an early-life intervention to prevent T2DM by improving the mother's generation, while adjusting the mood, quitting smoking and drinking, regular work and rest, and optimising the dietary structure are to increase the health index of the individual through optimising the individual's lifestyle and reduce the incidence of T2DM. At the same time, attention should be paid to screening blood glucose indicators, so as to keep abreast of the body's blood glucose situation and facilitate the adoption of therapeutic measures as soon as possible. Risk factors that are closely associated with the uncomplicated stage of T2DM include lifestyle and checkable/testable risk factors. At this stage, patients are affected by the "three more and one less" symptoms of diabetes and gradually lose weight, or even become thin, and the risk of disease caused by body weight is weakened. In this stage, diet control and exercise therapy become the most important, supplemented by drugs to bring good glucose-lowering effect, and attention should be paid to regular monitoring of blood glucose to avoid acute and chronic complications caused by fluctuations in blood glucose. Risk factors closely associated with T2DM complication stage may include: Lifestyle, medical and medication history, and examination/tests. Lifestyle intervention in this stage is equally important, while patients should pay more attention to medication, and screening and selection of drugs, to avoid other drugs and diseases that aggravate the burden of blood glucose. In the period of complications, attention should be paid to the examination, inspection, and blood glucose monitoring to understand the status of blood glucose control to achieve blood glucose individualisation and timely adjustment of glucose-lowering programmes. The relevant complications should be examined every 3 to 6 months to reduce the rate of disability and mortality, improve quality of life, and prolong the life. We can see that there is a different focus on risk factor interventions during the development of the disease, but the importance of lifestyle interventions is emphasised by the fact that they are carried out by the individual throughout the development of the disease. At the same time, some articles have suggested the importance of prioritising risk factors that contribute significantly to diabetes in different age groups for effective prevention and management of diabetes[271]. This is a direction for future research to help slow disease progression by establishing a map of diabetes risk factors that can be tested through a large number of clinical studies to develop personalised interventions for patients at different ages and disease stages.

DEFICIENCIES IN CURRENT RESEARCH

Lack of generalizability of research

In this article, we found that a number of examination and laboratory indicators can be used as risk factors for diabetes, and through a series of examinations and laboratory tests, the health status and physiological function of various groups of people can be assessed and at the same time, the diagnosis, assessment, treatment, and follow-up of the disease can be carried out. Among these risk factors that can be obtained through examinations/tests, in China, some of the tests are easy to obtain, which are mostly simple, inexpensive, fast, and sensitive, with high acceptance by patients, and can be done in hospitals or central laboratories. On the other hand, some of the tests are not easily available in the clinic due to their high price, high operator requirements, poor reproducibility, and high requirements for the testing environment. For example, gene sequencing of the human body is more expensive and less accepted by patients. The intestinal flora, with its high professional requirements for sample collection, preservation, and transportation conditions, is therefore not very generalizable and cannot be used as a routine test for large samples. Moreover, ethnicity, region, lifestyle, and customs[272-274] contribute more to the differences in individual flora, and factors such as race and geography should not be ignored so that only the study of intestinal flora for specific populations can provide guidance for precision nutrition and precision medicine. In order to solve the above problems and improve the universality and reproducibility of the test results of clinical trials, it is necessary to formulate standard operating procedures, strictly control the quality of instrumentation and reagents, converge the sample processing and storage conditions of different laboratories, provide professional skills training for laboratory personnel, and join the external quality control program. Through the above measures, the generalizability and reproducibility of laboratory results can be maximized to ensure that reliable data are obtained to guide clinical practice.

Conflicting results in the studies

Scientific inquiry is a step forward through various contradictions, verified (or disproved) step by step by various studies. In the article, we have included the findings of many large clinical trials, but some of the results of clinical trials have some contradictions, such as the effect of vitamin D levels and antidepressant drugs on the risk of T2DM. Some previous studies have concluded that vitamin D supplementation can prevent T2DM, and the use of antidepressants increases the risk of T2DM, but there are also some studies that do not support this conclusion, which is contradictory. In the later stage, we should pay more attention to the contradictory viewpoints in the previous clinical trials, carry out more in-depth explorations, and carry out more high-quality clinical studies through a complete protocol design so as to provide more high-level evidence-based basis for guiding the clinical practice, and extend the study from whether there is a risk association to the direction of the association (positive correlation/negative correlation), the number of associations (single correlation/complex correlation/biased correlation), and the strength of the association (significant correlation/highly correlated correlation). We should continue to think about and improve the results in order to provide more scientific support for disease prevention.

Limitations in the study population

Among the immutable social factors, we found that there are relatively few studies on ethnicity, and it is very important to consider the ethnic background when establishing risk stratification for clinical management in the era of precision medicine. Currently, there is a lack of high-quality, large-sample ethnicity studies to clarify the risk groups for T2DM in different ethnic populations, and most are single-country studies concentrated in Western countries, which results in a lack of ethnic diversity. In order to prevent further deterioration of existing health disparities, subsequent studies need to incorporate multi-country, multi-ethnic studies in order to obtain more generalizable and impactful results. There is a lack of uniformity and accuracy in the impact of blood glucose, blood lipids, blood uric acid, and other testing indicators on the risk of T2DM in the current study, and it is not possible to clarify what levels of blood glucose, blood lipids, and other testing indicators will form a qualitative change that will increase the risk of T2DM, and there is a lack of global consensus, which prevents clinicians from providing clear guidance, and does not help them to set up strict and clear blood glucose and blood lipid targets for their patients to manage disease risk. More high-level evidence-based studies are needed to help us form worldwide consensus guidelines to help clinicians make prevention and treatment decisions.

CONCLUSION

This article is a narrative review of T2DM risk factors, aiming to provide clinicians with a reference for T2DM prevention strategies, and timely identification and early warning of high-risk groups, thus delaying the onset of the disease. By optimizing treatment regimens and identifying potential drug targets, the preventive effect can be effectively improved. In the future, these risk factors can be translated into scales or disease prediction models to predict and estimate individual risk of disease. Although some of the findings can be directly applied to the clinic, not all of them need to be evaluated in the context of the actual clinical situation. Predictive models based on the risk factor identification and categorization in this review can help in the design of community-based multilevel screening strategies and can provide strong support for the development of public health policies and the improvement of related industry standards, especially in the discipline of endocrinology, metabolism, and other related medical fields. Overall, this paper provides important reference information on the prevention and treatment of T2DM, promotes multidisciplinary collaboration, improves risk assessment, and provides a professional research basis for clinical practice and industry decision-making.