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Incidence of type 2 diabetes and metabolic syndrome by Occupation 鈥� 10-Year follow-up of the Gutenberg Health Study

樱花视频 volume听25, Article听number:听502 (2025) Cite this article

Abstract

Background

In view of demographic change, rising retirement age, and a growing shortage of skilled workers, it is increasingly important to prevent widespread diseases such as type 2 diabetes or its risk factor metabolic syndrome. Since the workplace is an important setting for preventive measures and little is known about incident cases in the working population, the aim of this study was to identify vulnerable occupational groups for whom these interventions are particularly appropriate. Therefore, we investigated the 10-year incidence of type 2 diabetes and metabolic syndrome across occupational groups in Germany.

Methods

Employees of the population-based Gutenberg-Health-Study (GHS) were examined at baseline (2007鈥�2012) and 10 years later. We calculated age- and sex-standardised incidence rates and standardised incidence ratios (SIR) with a 95% confidence interval (CI) for occupations, job complexity levels, and supervisory and managerial positions. 5954 persons at risk for type 2 diabetes and 5103 at risk for metabolic syndrome were observed.

Results

Between baseline and follow-up, 388 cases of type 2 diabetes and 1104 cases of metabolic syndrome occurred, and standardised incidences were 6.9% and 22.6%, respectively. The highest incidence of type 2 diabetes was observed in the occupational group 鈥渇ood production and processing鈥� (20.7%) with a threefold increased incidence (SIR鈥�=鈥�3.0, 95% CI 1.8鈥�4.7) compared to the total working population of the GHS. Employees in 鈥渕etal production, processing and construction鈥� had the highest incidence of metabolic syndrome and a two times higher SIR (48.5%; SIR鈥�=鈥�2.1, 95% CI 1.4鈥�2.9). There was also a high incidence of both type 2 diabetes and metabolic syndrome in 鈥渃leaners鈥� (16.5% and 34.8%) and 鈥渄rivers and mobile plant operators鈥� (14.8% and 41.2%). An increased incidence of type 2 diabetes and metabolic syndrome was observed with decreasing job complexity levels.

Conclusions

This study shows wide differences in the incidence of type 2 diabetes and metabolic syndrome between occupational groups and highlights the vulnerability of certain occupations. As the workplace is an important platform for interventions, the findings of this study could guide the development of more nuanced and effective workplace health initiatives to promote a healthier workforce for the future.

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Introduction

Type 2 diabetes and its major risk factor, metabolic syndrome, remain a global burden on healthcare systems due to treatment costs [1], high mortality rates [2] as well as incapacity for work, and early retirement [3]. According to the International Diabetes Federation, globally 10.5% of the adult population (20鈥�79 years) were affected in 2021 [2]. In Germany, the proportion of diabetes cases (type 1 and 2) in adults aged 18 to 79 is estimated to be around 7.2% [4]. By 2040, the prevalence of type 2 diabetes among adults in Germany is predicted to increase by 54 to 77% [5]. In addition, in the year 2019, one in four people (26%) in Germany met the criteria for metabolic syndrome (abdominal obesity, hypertension, lipid metabolism disorder and impaired glucose tolerance) [6]. The average age of diabetes diagnosis (type 1 and 2) is currently around 53 years, an age at which most people are typically still working, highlighting the need to consider type 2 diabetes and metabolic syndrome in an occupational context [7]. In addition to lifestyle-related risk factors, the workplace can be both a risk factor and an opportunity for preventing type 2 diabetes and metabolic syndrome. Therefore, the workplace represents an important setting for health promotion and screenings that can identify undiagnosed diseases such as diabetes, obesity, and hypertension [8, 9]. To implement or strengthen targeted prevention measures, it is important to identify occupations with an increased risk. A systematic review showed that metabolic syndrome is a common health risk factor in all occupational groups and that the prevalence varies widely between countries and occupational groups, ranging from 5.2 to 39.2% [10]. Previous studies have examined the association between type 2 diabetes or metabolic syndrome and occupation cross-sectionally [11,12,13,14,15]. However, the extent to which new cases occur in different occupations cannot be estimated from cross-sectional studies. In addition, an examination of incidence can identify vulnerable occupational groups that need more prevention. To date, only one study is known that investigated the incidence of type 2 diabetes or metabolic syndrome in different occupational groups [16, 17]. Recently, one study identified differences in type 2 diabetes prevalence among the occupational areas in Germany using health insurance records [18]. However, the different occupational groups within broader occupational areas are varied, so occupations should be considered as differentiated as possible in order to identify occupations for preventive measures.

To address this research gap and enhance the current body of evidence regarding longitudinal studies, this study aims to examine the 10-year incidence of type 2 diabetes and metabolic syndrome among employees in Germany. Data from the Gutenberg-Health-Study (GHS) were used to identify occupational groups with an increased incidence.

Methods

Design and participants

We used longitudinal data from the GHS. The GHS is a population-based, prospective, observational single-centre cohort study in the Rhine-Main-Region in Germany.

The original focus of the GHS was to analyse cardiovascular risk factors and improve risk prediction for corresponding diseases [19]. Written informed consent was obtained from all participants. The local ethics committee and the local and federal data safety commissioners approved the study (#837.020.07(5555)). Participants were selected randomly from the local registry of the city of Mainz and the district of Mainz-Bingen. The random sample was stratified 1:1 for sex, residence (urban and rural areas), and age. Inclusion criteria were written informed consent and age between 35 and 74 years. Persons with insufficient German language knowledge were not included in the study, as well as persons who could not come to the study centre due to physical or mental impairment. A detailed description of the design and the rationale of the GHS is published elsewhere [19].

A total of 15,010 participants (response rate 60.4%) were enrolled in the baseline examination between 2007 and 2012. At the follow-up 10 years later (2017鈥�2022), 9575 individuals participated (retention rate 63.8%), 963 persons died, 2303 persons declined to continue participating, 123 persons were excluded, 399 could not be contacted, and 1647 people only took part in the telephone interview.

For this study, individuals were excluded from analyses if they were (1) not employed or working at baseline or if the occupation was unknown (n鈥�=鈥�6612), (2) older than 64 years of age (retirement age) at the day of the interview (n鈥�=鈥�206), and (3) diagnosed with type 1 diabetes or gestational diabetes (n鈥�=鈥�39). After excluding 23 participants due to missing information on diabetes, 8130 individuals were included for type 2 diabetes and 8153 for metabolic syndrome at baseline. For the examination of incidence, participants with prevalent type 2 diabetes (n鈥�=鈥�365) or metabolic syndrome (n鈥�=鈥�1736) at baseline were excluded, respectively. Due to lost to follow-up, 5954 persons at risk for type 2 diabetes and 5103 at risk for metabolic syndrome were ultimately observed (Fig.听1).

Fig. 1
figure 1

Flow chart of study population

Full size image

Exposure and听demographic variables

Information on age, sex, educational and professional qualifications, occupational phases and the occupational variables total working time, overtime per week, and full-time employment were obtained from a computer-assisted interview (see additional file 1).

Information about previous occupational phases, including the economic sector and job description, were then coded into occupational categories according to the German Classification of Occupations (KldB, Klassifikation der Berufe) 2010 [20]. The KldB 2010 reflects the current professional landscape in Germany and offers high compatibility with the International Standard Classification of Occupations (ISCO) [21]. The KldB is coded in five digits and is hierarchically structured. The first digit of the code describes the occupational area, the second the main occupational group, the third the occupational group, the fourth the occupational subgroup as well as supervisory and managerial positions, and the fifth the job complexity. The job complexity reflects the requirement level in four levels: 鈥渓ow鈥� (helpers and semi-skilled), 鈥渕edium鈥� (skilled workers), 鈥渉igh鈥� (specialists) and 鈥渧ery high鈥� (experts).

For this study, the occupation at the time of the baseline examination was used. Occupational groups were investigated at the one- and two-digit levels. The one-digit level contains 10 occupational areas, and the subsequent level is divided into 37 main occupational groups. The occupational area 鈥渕ilitary鈥� was not considered, because the classification in this group primarily reflects the rank and not the activity. Main occupational groups with fewer than 20 individuals or less than five incident cases between baseline and 10-year follow-up are not presented.

We examined job complexity as a proxy variable for socioeconomic status (SES). Job complexity is a good indicator of SES, as complex jobs tend to require higher levels of education and provide more financial compensation.

For quality assurance, 10% of the subjects were coded twice. Interrater reliability between primary and dual coding was calculated for different dimensions of the occupational code. Overall, the interrater reliability for the various dimensions was rated as good to very good (Cohen鈥檚 kappa coefficient鈥�=鈥�0.88, 95% CI 0.87鈥�0.89) [22].

Outcomes

Type 2 diabetes was defined as either a measured HbA1c-level鈥夆墺鈥�6.5%, answering yes to the questions 鈥淒o you have diabetes?鈥� and 鈥淗as this been diagnosed or confirmed by a doctor?鈥� or intake of insulin or oral glucose-lowering drugs [anatomical therapeutic chemical (ATC) code A10].

For the definition of metabolic syndrome, we followed the harmonized recommendations of Alberti et al. [23]. Participants had to fulfil at least three of the following five criteria:

  1. (1)

    obesity: waist circumference鈥夆墺鈥�80听cm for female and 鈮モ��94听cm for male participants.

  2. (2)

    triglycerides鈥夆墺鈥�150听mg/dl or on drug treatment for increased triglycerides (ATC code C10);

  3. (3)

    high-density lipoprotein cholesterol (HDL)鈥�<鈥�50 mg/dl in women and <鈥�40听mg/dl in men or on drug treatment for reduced HDL cholesterol (ATC code C10);

  4. (4)

    arterial blood pressure increased to 鈮モ��130 mmHg systolic or 鈮モ��85 mmHg diastolic or on antihypertensive drug treatment (ATC codes C02, C03, C07, C08 or C09);

  5. (5)

    fasting blood glucose鈥夆墺鈥�100听mg/dl or on drug treatment for increased glucose (ATC code A10).

Waist circumference was measured with a non-stretching tape measure midway between the lowest rib and the superior anterior iliac spine in position of expiration. Systolic and diastolic blood pressure were determined as the average of the 2nd and 3rd standardised measurement after 8 and 11听min of rest. Blood samples were drawn after an overnight fasting period of at least 8听h. Glucose, HDL cholesterol, and triglycerides were determined by routine laboratory methods. In general, all examinations were done according to standard operating procedures (SOPs) by certified medical technical assistants.

Statistical analyses

Descriptive data of the study population are presented as numbers and percentage听or mean and standard deviation. Type 2 diabetes and metabolic syndrome status were examined separately, and descriptive analyses were stratified by sex.

Age- and sex-standardised prevalence and incidence rates with 95% confidence interval (CI) were calculated for each occupational group, job complexity level, and managers and supervisors. For age- and sex-standardisation inverse probability treatment weighting [24] was used based on the age- and sex-distribution of the German population in 2021 (Federal Statistical Office of Germany) (see additional file 2). The incidence rates are expressed as percentages. Standardised incidence ratios (SIR) and 95% CI across occupations were calculated compared to the overall age- and sex-standardised incidence in the total GHS working population (reference incidence). Since the focus of this study is on the incidence of type 2 diabetes and metabolic syndrome, the prevalence across occupational groups is presented in the results but not discussed in detail.

Incidence rates were also calculated for job complexity level, and for supervisors and managers. The results were calculated for the total working population and separately for women and men. Due to the uneven representation of men and women in some occupations, several occupations could not be studied for both sexes. All analyses were conducted with R version 4.2.1 [25].

Results

Characteristics of the study population

Of the 8153 participants, 45.2% were female and the mean age was 48.4 years. Around half of the participants (48.1%) had the highest educational level (A-levels/high school diploma), and, regarding professional qualifications, 41.3% had a primary vocational school qualification, and 36.6% had a university degree. In addition, almost half worked in jobs with a medium job complexity level (44.6%), the other half in a job with a high or very high complexity level (21.0% and 30.8%, respectively), and only a small proportion worked in low-complexity jobs (3.6%). On average, employees had worked at their current jobs for around 14 years. Detailed information on the characteristics of the study population can be found in Table听1.

Table 1 Population characteristics at baseline, stratified by sex
Full size table

Incidence of type 2 diabetes

Between baseline and the 10-year follow-up, a total of 388 cases occurred. Overall, age- and sex-standardised incidence of type 2 diabetes was 6.9%. Of the nine occupational areas, 鈥渃onstruction, architecture, surveying and building technology鈥� and 鈥渢ransport, logistics, protection and safety鈥� showed the highest incidence (9.7% and 9.5%, respectively). Among the main occupational groups, the highest standardised 10-year incidence was observed among 鈥渇ood production and processing鈥� workers (20.7%), 鈥渃leaners鈥� (16.5%), and 鈥渄rivers and mobile plant operators鈥� (14.8%). The lowest incidence of type 2 diabetes was in the occupational group 鈥渕edical health professions鈥� (2.5%) (Fig.听2; Table听2). Compared to the total working population, the SIR showed a threefold increased incidence for employees in 鈥渇ood production and processing鈥� (SIR鈥�=鈥�3.0, 95% CI 1.8鈥�4.7) and a two times higher incidence for 鈥渃leaners鈥� (SIR鈥�=鈥�2.2, 95% CI 1.0鈥�4.7) and 鈥渄rivers and mobile plant operators鈥� (SIR鈥�=鈥�2.2, 95% CI 1.3鈥�3.3) (Table听2).

Fig. 2
figure 2

Age- and sex-standardized incidence and 95% CI of type 2 diabetes, stratified by main occupational group

Full size image
Table 2 Age- and sex-standardised incidence and SIR of type 2 diabetes for occupational areas and main occupational groups, job complexity level and managerial position
Full size table

Furthermore, a dose-response relationship was seen between job complexity level and incidence of type 2 diabetes. The highest incidence was observed among employees who worked in jobs with low job complexity level (10.6%) and decreased as the level of complexity increased. Employees in jobs with low complexity level had a 50% higher incidence of type 2 diabetes (SIR鈥�=鈥�1.5, 95% CI 1.0鈥�2.4). The incidence among supervisors and managers was similar compared to employees without a managerial position (7.3% and 6.8%) (Table听2).

In men, the incidence of the main occupational group of 鈥渇ood production and processing鈥� (SIR鈥�=鈥�3.0, 95% CI 1.4鈥�5.6) was increased threefold and more than twofold in 鈥渢ourism, hotel and restaurant鈥� (SIR鈥�=鈥�2.4, 95% CI 0.9鈥�5.0) and 鈥渄rivers and mobile plant operators鈥� (SIR鈥�=鈥�2.3, 95% CI 1.4鈥�3.5). Female employees had the highest SIR in 鈥渕athematics, biology, chemistry, physics鈥� (SIR鈥�=鈥�2.8, 95% CI 1.3鈥�4.7) and in 鈥渃leaners鈥� (SIR鈥�=鈥�2.1, 95% CI 0.7鈥�4.4). The sex-stratified incidence rates and SIR of type 2 diabetes are in the supplementary material (see additional file 3).

Incidence of metabolic syndrome

Within the 10-year observation period, 1104 participants of the working population developed metabolic syndrome, and age- and sex-standardized incidence was 22.6%. The highest 10-year incidence was observed in the occupational area of 鈥渢ransport, logistics, protection and safety鈥� (30.6%). Comparison of the main occupational groups showed the highest incidence among employees in 鈥渕etal production, processing and construction鈥� (48.5%), 鈥渄rivers and mobile plant operators鈥� (41.2%), and 鈥渃leaners鈥� (34.8%). The lowest incidence of metabolic syndrome was seen in 鈥減erforming and entertainment professions鈥� (10.1%) and 鈥渉orticulture and floristry鈥� (13.4%) (Fig.听3; Table听3).

Fig. 3
figure 3

Age- and sex-standardized incidence and 95% CI of metabolic syndrome, stratified by main occupational group

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Table 3 Age- and sex-standardised incidence and SIR of metabolic syndrome for occupational areas and main occupational groups, job complexity level and managerial position
Full size table

SIR calculations showed a two-fold increased incidence for employees in 鈥渕etal production, processing, and construction鈥� (SIR鈥�=鈥�2.1, 95% CI 1.4鈥�2.9) and for 鈥渄rivers and mobile plant operators鈥� (SIR鈥�=鈥�1.9, 95% CI 1.3鈥�2.3), and a 50% higher incidence for 鈥渃leaners鈥� (SIR鈥�=鈥�1.5, 95% CI 0.9鈥�2.4) as well as employees in 鈥渕echatronics, energy and electronics鈥� (SIR鈥�=鈥�1.5, 95% CI 1.1鈥�2.0) and 鈥渇ood production and processing鈥� (SIR鈥�=鈥�1.5, 95% CI 1.0鈥�2.1) (Table听3).

Again, an increased incidence was observed with decreasing job complexity levels. The incidence of metabolic syndrome in employees with low job complexity level was 27.0%, and compared to the total working population, the incidence was 20% higher (SIR鈥�=鈥�1.2; 95% CI 0.9鈥�1.5).

Supervisors and managers had a slightly higher incidence of metabolic syndrome than participants without a managerial position (25.0% vs. 22.3%, respectively) (Table听3).

The highest SIR for men was observed in 鈥渕etal production, processing and construction鈥� (SIR鈥�=鈥�2.2, 95% CI 1.5鈥�3.1) and for women in 鈥渃leaners鈥� (SIR鈥�=鈥�1.5, 95% CI 0.8鈥�2.4) and in 鈥渕athematics, biology, chemistry, physics鈥� (SIR鈥�=鈥�1.5, 95% CI 0.9鈥�2.2). A complete overview of the sex-stratified incidence rates and SIR of metabolic syndrome can be found in the supplementary material (see additional file 4).

Discussion

Principal findings and comparison to other studies

In the present study, we found wide differences in the incidence of type 2 diabetes and metabolic syndrome between occupational groups and identified occupations with increased incidences. The highest incidence of type 2 diabetes was found in 鈥渇ood production and processing鈥� and for metabolic syndrome in 鈥渕etal production, processing and construction鈥�. There was also a high incidence of both type 2 diabetes and metabolic syndrome in 鈥渃leaners鈥� and 鈥渄rivers and mobile plant operators鈥�, and several other occupational groups also showed a distinct higher incidence compared to the total working population.

A study of German health insurance records showed that the occupational areas 鈥渢ransport, logistics, protection and security鈥� and 鈥渉ealth sector, social work, teaching and education鈥� had the highest predicted probabilities for type 2 diabetes [18]. The results of this study only partially agree with ours, as we also observed increased incidences in 鈥渢ransport, logistics, protection and safety鈥�. However, the occupational area 鈥渉ealth, social affairs, teaching and education鈥� had the lowest incidence in our study.

Previous cross-sectional studies showed similar occupational groups with high prevalence of type 2 diabetes or metabolic syndrome. In a US study, the highest age-adjusted prevalence of metabolic syndrome was found in 鈥渇ood preparation and food service workers鈥� and 鈥渇arm operators, managers, and supervisors鈥� (29.6鈥�31.1%) [12]. In Japan, metabolic syndrome was most prevalent among males working in 鈥渃onstruction鈥�, 鈥渢ransportation鈥�, 鈥減rofessional services鈥�, and 鈥渃ooperative sectors鈥� and among females working in the 鈥渉ealth care sector鈥� and 鈥渃ooperative associations鈥� [11]. A New Zealand study showed similar results, which found an increased risk of type 2 diabetes among 鈥減lant and machine operators鈥� and 鈥渁ssemblers鈥� [26].

Our results are also largely consistent with existing longitudinal studies. A nationwide Swedish register-based study indicates that 鈥減rofessional drivers鈥�, 鈥渕anufacturing workers,鈥� and 鈥渃leaners鈥� have a threefold increased risk of type 2 diabetes compared to occupations in 鈥渉ealth and education鈥� sectors [16]. Similarly, a study of 75,000 Dutch workers showed an increased 3.8-year incidence of metabolic syndrome in male 鈥渟tationary plant and machine operators鈥�, 鈥渆lectrical and electronics trades workers,鈥� and female 鈥渇ood preparation assistants鈥� and 鈥渄rivers and mobile plant operators鈥� [17].

Interestingly, the occupational groups with high incidences are very heterogeneous in terms of their occupational exposures. Working conditions such as (night) shift work [27], long working hours [28], job strain [29] and high sedentary time [30] have been associated with an increased risk for metabolic disorders.

Furthermore, most of the occupational groups with a high incidence can be categorized as elementary and manual occupations. Previous studies have shown that 鈥渂lue-collar workers鈥�, who are predominantly engaged in manual labour, have a higher risk for type 2 diabetes and hypertension compared to 鈥渨hite-collar workers鈥� [31, 32].

However, the SIR in this study cannot be interpreted as a purely occupational risk. The results may also reflect the socioeconomic inequality that exists across the different occupational groups. One indication is the dose-response relationship we observed between decreasing job complexity and an increased incidence of type 2 diabetes and metabolic syndrome. Employees with low or medium level of job complexity in our study are likely to work in predominantly 鈥渂lue-collar鈥� jobs, while those with high and very high levels of job complexity are more likely to work in 鈥渨hite-collar鈥� jobs. Typically, 鈥渂lue-collar workers鈥� are often characterized by a low SES with a low educational attainment, which is in turn associated with type 2 diabetes [33, 34]. This assumption is supported by the study of Carlsson et al. [16], which showed that occupations with the highest incidence of type 2 diabetes were those characterized by low SES. In addition, a cohort study showed that low-skilled workers, especially in blue-collar occupations, had a significantly higher risk of metabolic syndrome than high-skilled white-collar workers and were more likely to exhibit unhealthy behaviour such as smoking, unhealthy diet, and low leisure time physical activity [35]. Therefore, occupation may indicate the presence of cardiometabolic risk factors. On the other hand, there are also major differences about the cardiometabolic risk profile in white-collar occupations, regardless of SES, which can be attributed to the heterogeneity in the respective occupations [26].

Occupational factors and exposures may also be contributing to risk. The longitudinal analysis by van Zon et al. shows that half of the corresponding occupational groups still had an increased risk of metabolic syndrome after adjustment for age and health behaviour [17]. Further, white-collar occupations showed significantly higher cholesterol and stress levels [31]. This supports the hypothesis that the occupational group plays an important role in the development of metabolic syndrome and does not just reflect the selection of workers with less healthy lifestyles into certain occupations.

The risk for type 2 diabetes and metabolic syndrome is likely the accumulated effects of SES, lifestyle factors, and occupational exposures. The partially conflicting results reflect the complexity of these relationships and more research is needed to investigate the extent to which these factors influence the occurrence of type 2 diabetes and metabolic syndrome. Our results primarily show which occupational groups are particularly vulnerable and would potentially benefit most from preventive measures.

Strengths and limitations of the study

To our knowledge, this is the first study that examined both type 2 diabetes and metabolic syndrome across common occupational groups. A main strength of the study is its long follow-up period of 10 years. This is particularly important because type 2 diabetes develops gradually and is often only diagnosed after a long latency period. Also, recruiting a large population-based study sample means that the study included a wide range of occupations. The occupations were coded twice by two coders for 10% of the subjects independently. A further strength is that the results are based on objectively measured outcomes taken by trained personnel and medication use was taken into account. This means that undiagnosed cases of type 2 diabetes and metabolic syndrome were considered. This helps avoid underestimation of incidence because there is a high estimated number of unreported cases. In 2021, globally almost every second adult (20鈥�79 years old) with type 2 diabetes was unaware of their diabetes status [36]. In Europe, the proportion of undiagnosed cases was 35.7%.

Further, we tried to ensure comparability with other studies regarding metabolic syndrome by using the harmonised definition of Alberti et al. [23], which attempts to unify the different criteria of the different organisations.

Our study has some limitations. A limiting fact is the small number of individuals in some occupational groups, resulting in few observed cases and wide CIs. The SIRs in these groups should therefore be interpreted and discussed with caution, because any risk difference is questionable if the CI encompasses the one. However, we counteracted this by only examining occupations with at least 20 people in the occupational group. Although our goal was to provide representative results, workers with low job complexity were difficult to recruit and are therefore underrepresented in this study. Standardisation according to SES or job complexity should possibly be considered in future studies.

Also, certain occupational groups are still dominated by women or men, making the comparison of the sex-stratified results challenging. Previous studies have also found sex differences in terms of incidence in the same occupational groups, but these differences were predominantly found in female- or male-dominated occupations [16, 17]. We also found sex-specific differences in occupations with an even distribution of women and men, such as the main occupational groups of the area 鈥渂usiness organisation, accounting, law and public administration鈥�.

Furthermore, we cannot rule out selection bias, as a proportion of participants did not participate in the 10-year follow-up. Within the 10 years, only about 5% of the included participants changed the occupational area and 7% the main occupational group, but we cannot rule out bias due to a change of occupation during the observation period.

Although we standardised for age and sex to ensure national comparability, it is unclear to what extent our results can be transferred to other regions in Germany with different socioeconomic structures or other countries with different health care systems. Further, the retention rate at the 10-year follow-up was 63.8%, acceptable for such a long follow-up period. However, lost to follow-up analyses have shown that employees with an unhealthy lifestyle, low job complexity and from the occupational area of 鈥渢ransport, logistics, protection and safety鈥� have a higher probability not to participate at the follow-up (unpublished analyses).

Meaning of the study and unanswered questions

The aim of this study was to investigate the incidence of type 2 diabetes and metabolic syndrome of a working population in Germany and to identify occupational groups with an increased risk over a period of 10 years. Alarmingly, we found that in some occupational groups, almost half of the employees met the criteria for metabolic syndrome and that in some occupational groups, the incidence of metabolic syndrome and type 2 diabetes was two to three times higher compared to the total working population in this study. Thus, our study illustrates the high need for preventive measures at the workplace, particularly in vulnerable occupations like 鈥渇ood production and processing鈥�, 鈥渕etal production, processing and construction鈥�, 鈥渃leaners鈥�, and 鈥渄rivers, and mobile plant operators鈥�, but also in further occupational groups. Our results also point to the heterogeneity of the incidences of the individual occupational main groups within an occupational area, meaning occupational areas with a high incidence also include occupational groups with low incidence, and vice versa. Therefore, occupations should be investigated and addressed as differentiated as possible for future studies and prevention initiatives.

Particularly in the face of demographic change, increasing retirement age and an increasing shortage of skilled workers, prevention is essential in all areas of life to ensure a healthy workforce. As most employees spend a large part of their time at work and are exposed to potential risk factors there, workplace prevention initiatives represent an important approach. Previous studies have provided evidence that preventive measures in the workplace could be useful and effective in reducing risk factors associated with type 2 diabetes [37]. In addition to the prevention of type 2 diabetes and metabolic syndrome, their progression and complications can be prevented when diagnosed. Thus, our results provide guidance, not only for primary and secondary prevention (incidence), but also for tertiary prevention (prevalence).

The occupations with high incidence identified in this study provide indications of where health promotion and screening programs as part of occupational health check-ups could bring the greatest benefit. In addition, workplace adjustments for people with existing cardiometabolic risk factors may be necessary to avoid complications and comorbidities.

Our study findings have implications for further research. First, vulnerable occupational groups and their work-related risk factors should be examined in more detail to be able to implement targeted and successful preventive measures. Second, future studies should investigate the complex relationship between occupational exposure, SES, lifestyle factors, and cardiometabolic risk. Third, it should be examined to what extent preventive measures in the workplace especially in occupations with increased risk can be made more effective in promoting health and preventing cardiometabolic diseases.

Conclusions

In conclusion, this study expands the existing evidence on the incidence of type 2 diabetes and metabolic syndrome in different occupational groups and highlights the vulnerability of certain occupations such as 鈥渇ood production and processing鈥�, 鈥渕etal production, processing and construction鈥�, 鈥渃leaners鈥� and 鈥渄rivers and mobile plant operators鈥�.

Our findings are consistent with previous research indicating increased health risk in these and similar occupations and underline the urgent need for targeted preventive measures within these work environments. As occupations serve not only as indicators of cardiometabolic risk but also as platforms for intervention, the findings of this study could guide the development of more nuanced and effective workplace health initiatives. Moving forward, it is imperative to address these occupational health disparities through comprehensive and tailored strategies to reduce the risks associated with type 2 diabetes and metabolic syndrome, and ultimately promote a healthier workforce for the future.

Data availability

The data that support the findings of this study are available from the Gutenberg Health Study (GHS), but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of GHS.

Abbreviations

ATC:

Anatomical therapeutic chemical

CI:

Confidence Interval

GHS:

Gutenberg Health Study

HDL:

High-density lipoprotein cholesterol

KldB:

Klassifikation der Berufe (classification of occupations)

SES:

Socioeconomic Status

SIR:

Standardised incidence ratio

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Acknowledgements

We thank F. Liebers (Federal Institute for Occupational Safety and Health), who provided significant support in the process of coding the occupational groups, and U. Latza (Federal Institute for Occupational Safety and Health) for her invaluable assistance in overseeing the early phases of this project.

Funding

Open Access funding enabled and organized by Projekt DEAL. The Federal Institute for Occupational Safety and Health financially supported the collection of work-related data and this project []. The GHS is funded through the government of Rhineland-Palatinate (Stiftung Rheinland-Pfalz f眉r Innovation [contract AZ 961-386261/733], the research programs Wissenschaft Zukunft and Centre for Translational Vascular Biology (CTVB) of the Johannes Gutenberg University of Mainz, and its contract with Boehringer Ingelheim and PHILIPS Medical Systems, including an unrestricted grant for the GHS. Philipp S Wild is funded by the Federal Ministry of Education and Research [BMBF 01EO1503]. Philipp S Wild and Philipp Lurz are PI of the German Centre for Cardiovascular Research (DZHK).

Author information

Authors and Affiliations

  1. Division Work and Health, Federal Institute for Occupational Safety and Health (BAuA), Berlin, Germany

    Juliane Bauer,听Janice Hegewald,听Karin Rossnagel,听Sylvia Jankowiak听&听Michaela Prigge

  2. Preventive Cardiology and Preventive Medicine, Department of Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

    Julian Chalabi,听Philipp S. Wild听&听Thomas Koeck

  3. FFAW: Freiburg Research Centre for Occupational Sciences, Freiburg, Germany

    Matthias N眉bling

  4. Institute and Policlinic of Occupational and Social Medicine (IPAS), Faculty of Medicine, TU Dresden, Dresden, Germany

    Alice Freiberg听&听Andreas Seidler

  5. Institute of Occupational, Social, and Environmental Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

    Merle Riechmann-Wolf听&听Pavel Dietz

  6. Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

    Philipp S. Wild听&听Stavros Konstantinides

  7. German Center for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Mainz, Germany

    Philipp S. Wild听&听Thomas M眉nzel

  8. Institute of Molecular Biology (IMB), Mainz, Germany

    Philipp S. Wild

  9. Department of Psychosomatic Medicine and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

    Manfred E. Beutel

  10. Department of Ophthalmology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

    Norbert Pfeiffer

  11. Institute for Clinical Chemistry and Laboratory Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

    Karl J. Lackner

  12. Department of Cardiology 鈥� Cardiology I, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

    Thomas M眉nzel听&听Philipp Lurz

  13. Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

    Konstantin Strauch

  14. Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

    Oliver T眉scher

  15. Department of Nephrology and Rheumatology, Center of Immunotherapy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany

    Julia Weinmann-Menke

Authors
  1. Juliane Bauer

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  2. Janice Hegewald

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  3. Karin Rossnagel

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  4. Sylvia Jankowiak

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  5. Michaela Prigge

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  6. Julian Chalabi

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  7. Matthias N眉bling

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  9. Merle Riechmann-Wolf

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  10. Pavel Dietz

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  11. Philipp S. Wild

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  13. Manfred E. Beutel

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  20. Julia Weinmann-Menke

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  22. Andreas Seidler

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Contributions

JB, JH and AS were involved in the conception and design of the study. JC analysed the data. PW, TK, MB, NP, KL, TM, KS, PL, OT, JWM and SK acquired the data. PL and PW had project administration. JB, JH, KR, SJ, MP, MN, AF, MRW, PD, PW, TK, MB, NP, KL, TM, KS, PL, OT, JWM, SK and AS contributed to data interpretation. JB wrote the first draft of the manuscript. All authors reviewed and edited the manuscript. All authors have read and agreed to the final version of the manuscript.

Corresponding author

Correspondence to Juliane Bauer.

Ethics declarations

Ethics approval and consent to participate

The local ethics committee (Ethics Commission of the State Chamber of Physicians of Rhineland-Palatine) (#837.020.07(5555)) and the local federal data safety commissioners approved the study in full compliance with the Declaration of Helsinki. Written informed consent was obtained from all participants.

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Not applicable.

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The authors declare no competing interests.

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Bauer, J., Hegewald, J., Rossnagel, K. et al. Incidence of type 2 diabetes and metabolic syndrome by Occupation 鈥� 10-Year follow-up of the Gutenberg Health Study. 樱花视频 25, 502 (2025). https://doi.org/10.1186/s12889-025-21732-5

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  • DOI: https://doi.org/10.1186/s12889-025-21732-5

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樱花视频

ISSN: 1471-2458

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