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Abstract Background Cholesterol is an important contributor to morbidity and mortality risks due to its association with obesity, cardiovascular disease, and cancer. A system of mandatory military conscription is a useful tool for disease-risk monitoring in a given male population. Swiss military conscription data are representative for more than 90 % of a given male birth cohort (with Swiss citizenship). The medical examination also includes voluntary laboratory testing, for which approximately 65 % of the young men present at conscription give consent. Methods Here we present the temporal and subgroup analyses of total serum cholesterol levels (TCL) among Swiss conscripts from 2006 to 2012 (N = 174,872; mean age = 19.75 years). The voluntary blood samples were tested by a central laboratory (Viollier AG) with identical measurement standards and strict quality control. To test differences in TCL by socioeconomic occupational status, sports test performance, Body Mass Index (BMI), age, and place of residence of the conscripts we used a multivariable regression model with TCL as dependent variable. Results Mean TCL decreased significantly, by 0.125 mmol/l (95 % CI 0.108–0.142, p < 0.001) from 4.225 mmol/l (95 % CI 4.210–4.240) in 2006 to 4.100 mmol/l (95 % CI 4.091–4.109) in 2012. Similarly, the prevalence of conscripts with an elevated TCL ≥ 5.17 mmol/l decreased from ≥10.2 % prior to 2011 to 6.9 % in 2011 and 8.2 % in 2012. Multivariate regression showed an association between elevated TCL and lower socioeconomic occupational status, lower sports test performance, higher BMI, higher age, and area of residence. There was no longer a significant increase in mean TCL among the three grades of obesity (BMI ≥ 30.0 kg/m2) as defined by the WHO. Within the BMI categories of normal weight and overweight, TCL was stratified by sports performance (better sports performance = lower TCL). Conclusion Decreasing TCL in 2011 and 2012 fits the known pattern of conscripted persons' stabilizing BMI and sports test performance of the conscripts in recent years. However, small temporal drifts within the laboratory analyses cannot be ruled out as confounding factors. In conclusion, identifying subgroups with unfavorable lipid profiles will contribute to the continuing success of intensified public health programs.

With increased complexity in various global health challenges comes a need for increased precision and the adoption of more tailored health interventions. Building on precision public health, we propose precision global health (PGH), an approach that leverages life sciences, social sciences, and data sciences, augmented with artificial intelligence (AI), in order to identify transnational problems and deliver targeted and impactful interventions through integrated and participatory approaches. With more than four billion Internet users across the globe and the accelerating power of AI, PGH taps on our current augmented capacity to collect, integrate, analyse and visualise large volumes of data, both non-specific and specific to health. With the support of governments and donors, and together with international and non-governmental organisations, universities and research institutions can generate innovative solutions to improve health and wellbeing of the most vulnerable populations around the world. In line with the Sustainable Development Goals, we propose here a road map for the development and implementation of PGH.